All four texts are basically circling the same target from different angles: > **How to reconceptualize LLMs not as minds or agents, but as *systems that generate and evolve sign-trajectories* according to a kind of “[[semiotic physics]]”.** Let me unpack that in a more precise way. --- ### 1\. From “LLM as mind/agent” to “LLM as simulator of trajectories” Janus’s *Simulators* paper starts by rejecting the default “LLM as agent / oracle / tool” framings. GPT isn’t best thought of as a goal-directed agent, an oracle that optimizes correctness, or a simple tool; its *outer* objective is just self-supervised prediction: minimise next-token log-loss on its training distribution. Janus calls that the **[[simulation objective]]**: Bayes-optimal conditional inference over [[the training]] prior, which, when rolled forward autoregressively, gives you simulated rollouts of that distribution. 8de16bfe-38e1-4927-b0dc-e0e3fa4… [[What you]] *actually* interact with, on this picture, is: - a **simulator** (the learned [[transition rule]] mapping context → token distribution); and - **simulacra** (the agents, worlds, texts, stories, arguments, etc. that appear when you iterate that rule [[over time]]). 8de16bfe-38e1-4927-b0dc-e0e3fa4… The two semiotic-physics texts take that same basic move and make it more explicit and formal: - Metasemi’s “note on ‘[[semiotic physics]]’” insists we should focus on **trajectories**: the whole growing prompt+completion string as it evolves step-by-step, not isolated next-token predictions. That autoregressive loop is treated as a stochastic [[dynamical system]] over token sequences. be617125-e814-485b-be78-4180030… - Jan & co.’s *[[Semiotic physics]] – revamped* literally define states, transition rules, sampling procedures and an [[evolution operator]] ψ that iteratively appends sampled tokens to the current trajectory. They then import dynamical-systems notions (Lyapunov exponents, attractors, absorbing states, large-deviation principles for “token bridges”, etc.) to describe how those trajectories behave. 1e7de3b5-5f0a-4475-a566-47983e5… So one shared [[core claim]] is: > **LLMs are best understood as stochastic [[dynamical systems]] over strings – simulators of trajectories – rather than as opaque black-box “reasoners” or static question-answering oracles.** --- ### 2\. From “LLM has contentful thoughts” to “LLM manipulates signs in a semiotic ecology” Picca’s *Not Minds, but Signs* pushes the same shift but in semiotic rather than alignment jargon. He attacks the “cognitivist” habit of treating LLMs as quasi-minds that understand, reason, or possess [[intentional states]]. Instead, he says, they are **semiotic machines**: systems that recombine and circulate *signs* (words, phrases, styles, genres) according to statistical regularities in their [[training data]]. 75023e62-0001-4750-94a3-b1dc4c5… Key shared moves here: - **No intrinsic understanding.** In all four texts, “meaning” is not something *inside* the model. Meaning arises when humans interpret the output within a cultural/linguistic context. Picca puts this in explicitly Peircean terms (representamen–object–interpretant); the LLM only produces representamina that can elicit interpretants in human readers. 75023e62-0001-4750-94a3-b1dc4c5… - **Semiotic rather than mental ontology.** Janus talks about GPT as a simulator of simulacra whose “physics” operates on tokens. Metasemi calls that “[[semiotic physics]]”: a physics-like law governing the behaviour of signs in trajectories (stories, arguments, dialogues). be617125-e814-485b-be78-4180030… Picca, in parallel, treats LLMs as actors in the *semiosphere* (Lotman) – an ecology of circulating texts and codes – not as cognitive agents. 75023e62-0001-4750-94a3-b1dc4c5… - **Outputs as open works.** Picca leans on Eco’s *open work* and *model reader*: LLM outputs are structurally underdetermined, polysemic texts that demand readerly cooperation to actualize meaning. 75023e62-0001-4750-94a3-b1dc4c5… Janus talks about GPT’s simulations as underdetermined, lazily rendered worlds that only specify details when needed; metasemi talks about “gratuitous specification” as trajectories branch. These are two vocabularies for the same phenomenon: the system generates sign-material that *invites* interpretation rather than carrying a fixed, internal “thought”. So, another shared theme: > **LLMs don’t “have” thoughts; they generate sign-strings that gain meaning only within human, cultural, and textual practices.** --- ### 3\. “Semiotic physics”: physics-style laws over sign dynamics All four texts also converge on the idea that there is something like a *physics* operating over these trajectories of signs. Janus’s original post sketches this as an analogy: the simulator is like a “time-evolution rule” for a universe whose “particles” are tokens; simulacra (characters, agents, arguments) are higher-level structures emerging in those simulations. 8de16bfe-38e1-4927-b0dc-e0e3fa4… Metasemi makes that explicit: - GPT is treated as a **multiverse generator**: each sampling step branches the trajectory, like wave-function collapse picking out one Everett branch. - The **semiotic physics** is the law governing how trajectories evolve in token-space, producing stories, agents, and worlds that *we* experience as meaningful. be617125-e814-485b-be78-4180030… Semiotic-physics-revamped then takes the physics analogy seriously and formalises it: - It defines a **transition rule** θ(s̄) that gives a probability distribution over the next token given the current trajectory, and an **evolution operator** ψ that applies θ plus sampling to extend the trajectory. 1e7de3b5-5f0a-4475-a566-47983e5… - It proves things like: the probability of any *particular* long “token bridge” between two tokens goes to zero as the length grows (a kind of large-deviation behaviour), and rephrases questions about “how likely is it to go from state A to state B?” in terms of minimal-action paths in this semiotic space. 1e7de3b5-5f0a-4475-a566-47983e5… - It then introduces **Lyapunov exponents, attractors, chaotic sequences, absorbing sequences** *in semantic space* by composing trajectories with an embedding μ into a meaning-space M and a distance δ on M. 1e7de3b5-5f0a-4475-a566-47983e5… Picca’s paper is less formal, but the same picture is there in softer language: prompts are **semiotic perturbations** that set boundary conditions; the model’s architecture and training data define a huge, learned constraint-surface of possible continuations; outputs are trajectories through the semiosphere, shaped by genre conventions, ideological frames, and cultural codes. 75023e62-0001-4750-94a3-b1dc4c5… So the common project here is: > **Develop a “physics of signs” for LLMs: a law-like description of how sign-sequences evolve under the simulation objective, both at the token level and at the level of semantic, pragmatic, and narrative structure.** --- ### 4\. Prompts as initial conditions / semiotic acts Another very specific through-line: prompts. - In *Simulators*, the prompt is the **condition** in conditional inference: it selects which region of the learned distribution you’re rolling forward. Different prompts give different simulacra, different “worlds”. 8de16bfe-38e1-4927-b0dc-e0e3fa4… - Metasemi describes trajectories as prompt+completion over time; the whole semiotic physics is the study of how these trajectories evolve from given starting points, and of how small prompt changes alter the whole downstream “universe”. be617125-e814-485b-be78-4180030… - Jan & co. talk about prompts as initial states in a dynamical system and then explicitly introduce **attractor sequences** and **chaotic sequences**: some prompts lead to highly stable, convergent completions (e.g. the model’s stock refusal scripts or boilerplate), others to highly sensitive, chaotic branches. 1e7de3b5-5f0a-4475-a566-47983e5… - Picca calls prompts **semiotic acts** or “semiotic contracts”: when you ask for “Nietzsche explained like a stand-up comedian for high-school students”, you are setting genre, register, audience, ideological stance, etc. That frame then shapes what region of the semiosphere the model will navigate when producing a trajectory. 75023e62-0001-4750-94a3-b1dc4c5… So across all four texts you get the same structural role: > **Prompts are boundary conditions / interventions in semiotic physics: they are how users “steer” which simulacra, genres, and interpretive frames get instantiated.** --- ### 5\. Methodological moral: study use-level dynamics, not just inner mechanics Finally, there’s a shared methodological stance: - Janus, metasemi, and Jan all argue that to understand and align LLMs we must look at **their behaviour as simulators**: multi-step rollouts, emergent simulacra, how trajectories behave under different prompts – not just inner weights or single-token accuracy or supervised benchmarks. - Picca, in a different idiom, says: don’t obsess over whether the model “really understands” or has internal mental states; instead, analyse **how it functions in sign-ecologies** – how its outputs reframe texts, redistribute genres, mirror or distort ideologies, and how humans interpret them. 75023e62-0001-4750-94a3-b1dc4c5… So the shared methodological theme is: > **Treat LLMs as generators of semiotic processes and study the dynamics of those processes (textual, cultural, pragmatic) rather than reifying an inner “mind” or an inner “utility function”.** --- ### One-sentence synthesis Put brutally compactly: > All four documents are jointly trying to build a unified ontology and “physics” for LLMs in which they are *simulators of sign-worlds*: they learn a transition law over linguistic tokens from data; that law, when iterated on prompts, generates trajectories populated by simulacra; these trajectories live in a cultural semiosphere and acquire meaning only through human interpretation – so if we want to understand, use, or align LLMs, we must study that semiotic dynamics, not pretend we’ve built little disembodied minds. ok so, that was fucking great! We may not need all the detail that you just gave me, but maybe we will. What I would like you to do now is to help me try and write a secti0on of a draft I am writing. I am going to give you sections 0 - 4, of the draft. Section 5 is the section that will draw very heavily on the semiotic physics stuff that you just gave me. Now, I DO NOT WANT YOU TO draft me a version of section 5 or anything like that yet. I just want you to explain to me very clearly how you think the semiotic physics stuff will be relevant. in the writing of section 5. –as you might or moight not know. this is attempt number a thousand and something of trying to get this section straight, so please give me a very comprehensive answer, and keep in mind that previous llms i have worked with on this draft always tend to fall into the same traps, so beware of making dub mistakes. DRAFT: # 0. Introduction In the last few years, aestheticians and philosophers of art have paid a lot of attention to the question of how and whether to appreciate the \*outputs\* of generative AI systems (for example, images from Midjourney or music from Suno). I want to address a related but different question: how can we aesthetically appreciate generative AI systems \*themselves\*? My focus will be on one particular type of generative AI: large language models (LLMs). As of November 2025, high-end consumer models include GPT-5.1, Claude 4.1 Opus, and Gemini 2.5 Pro. Is it strange to treat such systems as objects of aesthetic appreciation? I do not think so. In the last two decades, analytic aesthetics has begun to pay attention to objects other than artworks (for example, Saito, 2008; Carlson and Parsons, 2008), and one important focus has been the aesthetics of design, that is, the aesthetics of \*artefacts\*: things made to perform some purpose or other. LLMs are certainly artefacts, but, as I will argue, the way they are created and the way they function mean that they cannot simply be fit into existing accounts of the aesthetics of design (for example, Carlson and Parsons, 2008; Forsey, 2013). Drawing on Carlson’s approach to \*environmental aesthetics\*, I develop a negative argument and then a positive one. Negatively, I argue that we should resist two temptations. One is to appreciate LLMs by analogy with persons – to talk about their personalities or characters, or to ground appreciation in treating them \*as if\* they were agents. The other is to treat them simply as designed artefacts whose aesthetic value is a matter of how well their \*\*form fits their function\*\*. Positively, I propose that we should instead understand individual LLM-mediated chats as generative environments, and aesthetically appreciate the \*order\* that emerges in these environments under the constraints of a given model. %%enrico comment: "appreciation of a particular episode as a manifestation of a model’s characteristic generative order." should be mentioned at the end of the paragraph above to avoid confusion%% The paper proceeds as follows. In Section 1 I set out Carlson’s distinction between design appreciation and order appreciation, and I consider how a third case – the aesthetic appreciation of persons – might fit into this framework. In Section 2 I describe, at a schematic level, what contemporary LLMs are: token-based next-word predictors trained on large text corpora and shaped by reinforcement learning from human feedback. Section 3 then asks whether, given this technical reality, LLMs can be aesthetically appreciated as persons, either under make-believe approaches or under concessive views that treat them as thin agents, and argues that Carlson’s constraint rules out both routes. Section 4 considers the alternative of treating LLMs as designed artefacts and argues that, although they are artefacts, the aesthetically relevant order in their behaviour is not well captured by simple “\*\*form follows function\*\*” stories. Section 5 introduces the idea of \*text mechanics\* – a mid-level description of embeddings, attention, layers, and reinforcement learning – as a right kind of knowledge for applying order appreciation to LLM-mediated chats. Section 6 closes by examining one extreme case, a “bee” text generated by Claude Opus 3, to show how this framework can guide aesthetic appreciation of a particular episode as a manifestation of a model’s characteristic generative order. --- # 1. Appreciating Design, Appreciating Order ## 1.1 Design vs. Order We will first set about showing why treating LLMs as if they were people is not a satisfactory way of aesthetically appreciating them, before proposing an alternative account. Both our criticism of agentive views and our positive account will draw from Carlson’s environmental aesthetics, as laid out in his 2000 book \_Aesthetics and the Environment\_. Let us start with Carlson's general recommendation for aesthetic appreciation: take things as what they are, and look at them in the light of the right kind of knowledge. > First, that, as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (Carlson, 2000, p. 6) This captures something quite intuitive about how we appreciate nature versus how we appreciate works of art. Consider what goes wrong when we depart from it. If we accept, as a majority do in the 21st century, that mountains and cliff faces were not items crafted by some divine artisan but by natural forces, then appreciating them \_as if they were\_ God-crafted artifacts, seems wrong-headed (c.f. Carlson REF %%Add Danto Ref%%). Similarly, if I were to gaze on a painting by Rembrandt, believing that it was, in fact, the product of natural forces slopping paint together, I would be seen as appreciating the object in question in a sub-optimal way (to say the least). In both cases, appreciation is severely undermined by a failure to recognise what the object in question truly is. Different sorts of thing, Carlson says, require different modes of appreciation. Things like artworks and non-art artifacts, (e.g. laptops, hammers, washing machines), merit what he calls \_design\_ \_appreciation\_. Things which are not designed, primarily for Carlson, the natural environment, warrant what he calls \_order appreciation\_. In design appreciation, Carlson focuses first on how we appreciate works of art. With paradigmatic artworks, we recognise them as creations of designers—objects where "every one of their features is the result of a decision by the artist" (Carlson, 2000, p. 109). We appreciate such works by understanding what the artist set out to achieve and how they went about it. Our appreciation centres on the relationship between the initial design and its embodiment: we consider whether the artist succeeded in their undertaking, how they worked with their materials, what constraints they faced, and whether the outcome realises their vision. This same approach extends to designed artefacts more generally. Carlson is explicit that functional objects are properly appreciated by seeing how their forms answer to what they are for: > “This is in part the point of the much-repeated phrase ‘form follows function.’ The forms of all functional objects—buildings, airplanes, and appliances as well as landscapes—must be aesthetically appreciated in terms of how and how well such forms fit their functions. However, the cliché is frequently interpreted too narrowly. With anything functionally designed, not only its form, \*\*but much of its aesthetic interest and merit, ‘follows function’.”\*\* (Carlson, 2000, chapter 12, 188). Thus a chair, a kettle, or a bridge invite the same style of attentive appraisal as a painting—guided by knowledge of ends, materials, constraints, and the fit between purpose and realisation. In order appreciation, we face objects that show order but have no designer behind them. Natural environments are the main case. Here there are no intentions to fulfil, no problems being solved, no functions deliberately served. Instead, we find patterns and structures created by forces—geological, biological, meteorological—operating without purpose or plan. Our task shifts from evaluating success against intention to understanding how these forces have shaped what we observe. We look for the processes at work, the relationships they create, and the order they impose. Carlson gives the model: > On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them. Moreover, the objects are selected in part by reference to a general nonaesthetic and nonartistic story that helps make them appreciable by making this order visible and intelligible. Awareness and understanding of the key entities—the order, the forces that produce it, and the account that illuminates it—and of the interplay among them dictate relevant acts of aspection and guide the appreciative response. (Carlson, 2000, p. 119) One structural contrast is worth noting. In design appreciation there is a split between a planner and a product: intentions, plans, and constraints precede and shape the artefact. In order appreciation there is no such split. The same physical, biological, or meteorological processes that make the thing also make its order - the 'maker' is the active system itself - so source and product are continuous. %%this a good point, but not made very clearly. "there is a sense in which designer and artifact are continuous. the designer imposes a plan on matter, whereas in nature there is no form being put into matter. the order is immanent in the matter. In design the plan for the form precedes the matter"%% In both modes, appropriate knowledge guides acts of aspection—what to look for, which dependencies matter, where to set boundaries, and how to draw contrasts (Carlson, 2000, p. 50). But the character of this knowledge differs fundamentally. In designed cases, we need functional and technical understanding: what the designer intended, what constraints they faced, what procedures they employed. This knowledge shows us how ends and means relate. In natural cases, we need scientific accounts operating at different scales—geomorphology reveals how landforms develop over millennia, meteorology explains weather patterns, ecology shows community interactions. Without such knowledge, natural structures might look accidental or chaotic; with it, we see them as effects of identifiable processes (Carlson, 2000, pp. 50, 60–61). Even when we select a particular viewpoint or timeframe to observe nature, this selection serves only to reveal the order more clearly, not to impose our own design. Once a specific scientific account is in play, some cases will show the relevant order better than others, preventing the worry that everything becomes equally appreciable (Carlson, 2000, pp. 118–119). The fundamental rule remains: do not project a planner where there is none; where something is made to a plan, judge it as such. ## 1.2 Appreciating People It could be argued that Carlson’s approach to aesthetics overlooks another important category of object of appreciation: people. In ordinary life we do not only admire landscapes and artefacts; we also admire one friend’s modesty or good humour, another’s sardonic manner, a colleague’s intellectual style or a mentor’s quiet steadiness. Some philosophers have taken this practice seriously, investigating the aesthetic appreciation of personality – sometimes termed “beauty of character” – and asking whether traits such as kindness, wit, or courage can be aesthetically as well as morally valuable (Gaut 2007; McGinn 1997; Davies 2012; Paris 2018a, 2018b). Carlson’s second recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge – familiarity with a life (real or fictional), a sense of the values and dispositions that organise it, and an understanding of how someone tends to feel, think, and act across situations. We do not admire “kindness” in the abstract, but this person’s pattern of generous responses given who they are and what they have faced. As Parsons stresses in his discussion of beauty of character, such knowledge is typically built up through direct interaction and sustained personal relationships, through careful written biography, and, more precariously, through the “social biography” of gossip (Parsons 2023, 297–299).\[^1\] This personal appreciation also scales up to what we might call performance personalities. We respond to a comedian’s improvisational skill or an orator’s gravitas much as we respond to character in our friends, but now filtered through a public persona – genuine to the individual, yet artfully composed for performance. Recent scholarship has emphasised these performative dimensions, exploring how stand-up comics, actors, and media figures present stylised versions of themselves that invite aesthetic response (Eaton 2012; Carroll 2013). Here too, Carlson’s knowledge requirement bites: to appreciate such personas we need to understand both the individual and the conventions of the performance context. Although Carlson does not consider person appreciation, it is not difficult to imagine ways in which his framework might be extended or modified to accommodate it. One option is to treat “persons” as a third category alongside natural items and artefacts, with their own distinctive mode of appreciation anchored in their status as subjects rather than as environments or tools. A second option is to treat the appreciation of character as a special case of order appreciation: we focus on the psychological, social, and biographical forces that shape a life, much as we attend to geological and ecological forces in a landscape. A third option would be to emphasise the ways in which personalities are, at least in part, self-shaped, and to appreciate them as self-designing projects – a thought that has obvious attractions for existentialist traditions. On all of these views, however, Carlson’s second recommendation still applies: aesthetic appreciation is guided by substantive background understanding of what persons are like and how their traits hang together over time. For present purposes, we need not decide which of these options is correct. It will be enough to note that person-based aesthetics, where it exists, presupposes a rich conception of the subject as a temporally extended agent with relatively stable dispositions, projects, and evaluative commitments, grasped under a suitable body of knowledge. In the rest of the paper, when I consider whether we can aesthetically appreciate LLMs “like people”, it is this sort of person-directed appreciation – and this Carlsonian constraint – that will be in the background. \[^1\]: Carlson stresses that ordinary descriptions of environments and more theoretical scientific, historical, and functional descriptions lie on a continuum, so that scientific and historical knowledge can deepen rather than displace practical familiarity as a basis for aesthetic appreciation (Carlson, 2000). By analogy, one might speculate that the sciences of mind and behaviour could relate to folk-psychological and biographical understanding in a similar way, so that in some cases empirical work on personality, emotion, or cognition might feed into the aesthetic appreciation of persons alongside the more everyday forms of knowledge stressed in the main text. --- # 2. What LLMs Are # 2. What LLMs Are Carlson recommends we appreciate things for what they are. So what are LLMs? In this section I explain the technical reality of these systems: how they process text as numerical tokens, calculate probabilities through learned parameters, and generate responses through iterative sampling, all without symbols, meanings, or understanding. In later sections (§3 and §4) I use this reality to assess whether LLMs can be aesthetically appreciated as persons or as designed artefacts. Consider what happens when an LLM encounters the text "The cat sat on the". The system first breaks this into tokens—discrete units like words or word-parts. Importantly, each token is converted to a number: "The" might become 464, "cat" becomes 3857, "sat" becomes 4521, and so on. The model works entirely with these numbers, not with words or meanings. It then assigns probabilities to possible continuations: token 5687 (which represents "mat") might have a 38% chance of appearing next, token 2931 ("floor") 22%, token 8104 ("chair") 15%, token 9823 ("roof") 8%, with thousands of other possibilities each assigned their own probability. The system does not simply pick the highest-probability token. Instead, it randomly samples from these probabilities. A parameter called \*temperature\* controls how much randomness is involved. When temperature is set to zero, the model always picks the most probable token. This produces text that quickly becomes repetitive—the same phrases appearing again and again. When temperature is higher, around 0.8, the model sometimes picks less probable tokens. This creates variation that looks creative. But it is randomness, not creativity. The model is rolling weighted dice, not making choices. The system selects one token—say "mat"—and appends this new token to create a longer sequence "The cat sat on the mat". It then calculates entirely new probabilities for what token should follow the extended sequence. Token by token, the system builds what appears to be coherent text through repeated numerical operations. These probabilities do not come from simple memorisation. With 50,000 possible tokens, there are 125 trillion possible three-token combinations. No %%feasible? %%amount of text could cover all the sequences the model might encounter. Even if we had such text, storing all these combinations would be impossible. The model must learn general patterns rather than memorising specific sequences. These probabilities come from patterns learned during an initial \*pretraining\* phase. By pretraining I mean the process by which the system is exposed to vast quantities of text—billions of pages from books, websites, and other sources—and learns to predict the next token in each sequence. The model begins with millions of numerical parameters set to random values. Through repeated exposure, the system learns statistical regularities: which tokens tend to follow other tokens, which token sequences co-occur, how sequences typically unfold. When training on millions of instances of "The cat sat on the \[something\]", the system learns that certain completions are more common than others. Crucially, the model stores these patterns as adjustments to millions of numerical parameters—decimal numbers that shape how strongly different tokens associate with each other. After seeing "doctor" followed by "patient" thousands of times, parameters adjust so that token 1245 ("doctor") increases the probability of token 7823 ("patient") appearing nearby. The model does not learn that doctors treat patients or that cats are animals; it learns that in the training distribution, certain number sequences (tokens) follow others with certain frequencies. No programmer writes rules about grammar or meaning. The patterns emerge from exposure to text. The pretraining process iteratively adjusts these parameters to minimise prediction error: when the model wrongly predicts token 5555 but the actual next token was 3421, the parameters shift slightly to make 3421 more likely in similar future contexts. After billions of such adjustments, the model has learned to approximate the statistical patterns of human language. This yields a \*base\* model: a large, general-purpose text continuation engine. By \*embedding\* I mean the way the model represents each token as a list of numbers—typically hundreds of them—that position it in a mathematical space. Tokens that appear in similar contexts end up near each other in this space. "Cat" sits near "dog" because both appear after "the", both can be followed by "sleeps", both fit in phrases like "fed my \_". The model learns these positions through pretraining, not from programmed definitions. This is how meaning emerges in the model: not from understanding concepts but from tracking which words appear in similar contexts. The transformer architecture adds mechanisms called \*attention\*. These allow the model to connect related words even when they are far apart in a sentence. For instance, in "The cat that chased the mouse sat on the mat", the model needs to know that "sat" refers back to "cat", not to "mouse". Through training, different attention mechanisms specialise in tracking different kinds of relationships. Some track which pronouns refer to which nouns. Others connect verbs to their subjects across long sentences. No one programmes these specific functions. They emerge because tracking these relationships helps predict the next word. In use, the model generates text through \*autoregressive decoding\*: each newly generated token gets added to the context, creating a new, longer sequence for which the model must calculate fresh probabilities. Given an input like "What is the capital of France?", the model computes probabilities, selects token 464 ("The"), appends it to create "What is the capital of France? The", recalculates probabilities for this new sequence, selects token 2341 ("capital"), and continues this mechanical process—"The", "capital", "of", "France", "is", "Paris"—until reaching a stopping point. Each step is purely computational: multiply numbers, add numbers, select token, repeat. The pretraining I have described so far teaches the model statistical patterns of language and yields a base LLM. In practice, most chat-oriented systems undergo a further \*post-training\* phase. After pretraining, the base model is fine-tuned on examples of instructions and responses, and then adjusted by reinforcement learning from human feedback (RLHF). Human raters evaluate thousands of the model's responses—rating them for helpfulness, accuracy, appropriate tone. The model then adjusts its parameters to produce more responses like those rated highly and fewer like those rated poorly. Post-training shapes the model's conversational norms: when to express uncertainty ("I'm not sure, but..."), when to decline requests ("I cannot help with..."), how to structure explanations ("Let me break this down..."). RLHF makes responses more consistent, more helpful, and more aligned with human expectations. But it operates through the same fundamental mechanism—adjusting numerical parameters to match patterns in the training data. The model learns which response patterns get high ratings, not why those patterns are appropriate or what social purposes they serve. The result is a \*chat-optimised\* model: the same predictive core, now biased towards a certain family of outputs that look like the moves of a cooperative assistant. When this chat-optimised model is embedded in a product—given a system prompt, safety filters, a memory policy, and a user interface—it becomes the chatbot that users encounter. What users describe as a model’s “personality” or “vibe” is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core. --- # 3. Appreciating LLMs as Persons # 3. Appreciating LLMs as Persons We sometimes appreciate persons aesthetically, responding to traits such as warmth, wit, or steadiness as “beautiful” or “ugly” features of character. Our appreciation of others goes beyond their physical appearance. You might admire or enjoy your friend's warmth or eccentricity, or a stand-up comic's quick wit, or a celebrity's self-deprecating demeanour; you might even appreciate the personalities of fictional characters: Gatsby's enigmatic, dream-chasing idealism; Ron Swanson's libertarian gruffness. It is therefore tempting to think that our appreciation of LLMs might be modelled on our appreciation of people. Many users already talk this way, describing their favourite models in terms of “personality” or “vibe”. However, the technical description in the previous section presents LLMs as systems that tokenise text, manipulate numerical vectors, and generate continuations by sampling from learnt probability distributions, with a further post-training phase that biases them towards a certain assistant-like pattern of response. Nothing in that description straightforwardly resembles a subject with beliefs, intentions, or a life-history; there is no obvious place for character traits, projects, or personal development. Given Carlson’s recommendation that we should appreciate things as what they in fact are, and in the light of the right kind of knowledge, it is not yet clear that person-based aesthetic predicates are being applied to the right kind of object. In this section I ask whether, under that recommendation, there is any appropriate person-based aesthetic stance towards LLMs. I consider, in turn, make-believe approaches, concessive mindedness approaches, and a line of thought based on post-training and chat personae, and argue that none yields a satisfactory model of person-based aesthetic appreciation of LLMs themselves. ## 3.1 Make-believe approaches Start with the make-believe route. If we ask ordinary users whether they literally believe that a chatbot is a person, many will concede that they do not. They may talk to a model as if it were a friend or a colleague, and they may feel heard, reassured, or amused, but when pressed they acknowledge that they are interacting with a computational system rather than a human being. Their stance is, in this sense, already a kind of as-if posture. Mallory offers a way of theorising this posture through what he calls \*chatbot fictionalism\*. On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent, even though, at the metasemantic level, he claims that the outputs lack literal semantic content and acquire “fictional” content only within the game. Within the fiction, the chatbot “says” things and “means” things; outside the fiction, we know that no such speaker is present. This fits the everyday thought that we can take a chatbot seriously in the moment without actually believing that it has a mind. > Chatbot exchanges are “literally meaningless but fictionally meaningful” within a game of make-believe (Mallory, 2023, p. 1091). Just as a child treats a banana as a sword in a game, we treat chatbot outputs as utterances within a kind of imaginative practice. This is not delusion but a deliberate, bounded pretence that allows us to coordinate with the system and even gain knowledge from it, much as we might learn geography from a map by imagining countries as shapes. The secretary who asked Weizenbaum to leave while she conversed with ELIZA “is no more deluded than a theatregoer who fears for a character or cries at their death” (Mallory, 2023, p. 1091). Mallory’s account is not itself an aesthetics of LLMs; it is primarily a semantic and epistemic proposal about how we can use them and learn from them. But it highlights one obvious way a person-based aesthetic stance might be defended: one might suggest that we should aesthetically appreciate LLMs as if they were persons or characters, in the same sense in which we respond aesthetically to fictional protagonists whose existence we do not literally believe in. In the fictional case, however, the protagonists are artefacts within a story-world, so treating them as if they were persons does not misclassify their kind. Their role within the work is precisely to function as person-like figures in a narrative. By contrast, treating the LLM itself as a person would, given the architectural story in §2, amount to appreciating a generative artefact as if it were a subject with a life and character. That is a familiar kind of misclassification in Carlson’s terms. At this point one might object that we already aesthetically appreciate fictional characters in novels and television, and that this also involves an as-if stance that seems harmless. We respond to Gatsby’s enigmatic, dream-chasing idealism or Ron Swanson’s libertarian gruffness using much the same vocabulary as we use for real people, and we often talk quite straightforwardly about their “character” or “personality”. In doing so, though, we still take them for what they are: constructed characters in a fiction, artefacts whose person-like profiles are fixed by authorial and performative decisions. Treating such figures as if they were real people does not misclassify their kind; it recognises that their role is to function as person-like nodes in a story. %%chatbots function as person-like nodes in a chat..? as counterargument. chats have a function. counter counter argument. Nobody thinks we are imaginging a fictional world when we are using an llm look at the Eileen John paper%% By contrast, fictionalist approaches to LLMs ask us to take a real computational system – whose nature, as §2 stressed, is that of a large-scale text-prediction mechanism with post-training biases – and appreciate it as if it were a person, bracketing that knowledge. That is much closer to appreciating a mountain as a divine sculpture despite knowing the geological story, and so sits badly with Carlson’s demand that appropriate appreciation respond to things as what they in fact are. Cross’s discussion of AI art systems develops something like this idea in the artistic context. He proposes what he calls the \*exploration paradigm\*, in which artists relate to AI systems as participants in a structured interaction: > By adjusting inputs, iterating, and sampling, an AI artist is engaged in a process of mapping – and perhaps interrogating – the way that the algorithm sees and understands (Cross, 2024, pp. 7–8). Cross draws an analogy with performance art, where artists create spaces for audience participation. The AI artist's prompts structure a kind of “participation” by the algorithm, and the resulting images serve as documentation of this exploration. He acknowledges limitations to this framing: > The analogy... with performance art isn't a perfect one (Cross, 2024, p. 9). As Cross himself notes, AI cannot genuinely “participate” since it lacks conscious choice or experience. What seems like participation is still statistical pattern-matching. While Cross’s exploration paradigm offers a richer description of certain AI art practices than simple tool-use, it does not support person-appreciation for AI systems. The artist explores the algorithm’s patterns, but the algorithm is not a participant in any literal or psychological sense. When Cross’s view is read as a model for our relation to the AI system itself, it looks like a kind of aestheticised make-believe. The artist is invited to treat the system as if it were a participant with a distinctive way of “seeing” or “understanding”, and the viewer is invited to regard the resulting interaction as a sort of joint performance. Mallory and Cross thus converge on a shared picture: in practice we often stand in relation to LLMs as if they were persons, and some of our aesthetic language is shaped by this as-if stance. Carlson’s recommendation now gives us a clear verdict on this first route. The as-if stance may be useful for interaction and may frame certain artistic practices, but an account of \*appropriate\* aesthetic appreciation of LLMs themselves cannot, on his view, rest on a stance that depends on systematically treating the object as something it is not. Once we have in view the technical reality described in §2, appreciating an LLM as a person is analogous to appreciating a mountain as a divine sculpture: it is to misapply person-based predicates to a case where the right background knowledge tells us that we are dealing with a different kind of thing. Make-believe personification may be harmless in some contexts, but under Carlson it cannot supply the correct mode of aesthetic appreciation for LLMs. #### 3.2 Concessive mindedness approaches If the make-believe route fails under Carlson’s recommendation, one might try a different strategy: instead of pretending that LLMs are persons, argue that they really are agents of a thin and unfamiliar kind. On a suitably liberal conception of mind, perhaps they qualify as intentional systems and that is enough to license some person-based aesthetics. Frankish offers a sophisticated version of this idea. Drawing on Dennett’s intentional stance, he suggests that LLMs can be treated as genuine, if unusual, intentional systems. On this view, we are licensed to ascribe beliefs and desires to an LLM when doing so yields a simple and fruitful account of its behaviour, even if the underlying implementation is purely mechanical. In the case of contemporary chatbots, Frankish proposes that we can ascribe to them a large set of thin “beliefs” – roughly, informational states distilled from their training – and one thin “desire”: to play what he calls the \*chat game\*. A system is playing the chat game when it generates text that looks like a cooperative move in an ongoing conversation, respecting local coherence, relevance to the prompt, and broadly human conversational norms. An LLM, on this picture, is a system whose behaviour can be summarised by saying that it believes many simple things and wants to make an appropriate next move in the chat. Crucially, this is not a make-believe view. Frankish is not inviting us to pretend that LLMs have beliefs and desires; he is claiming that, at the right level of abstraction, it is literally true that they do, in much the same sense in which a thermostat can literally be said to “want” the room to be at a certain temperature when adopting the intentional stance helps us describe its behaviour. The agent-talk is meant to latch onto real, pattern-like features of the system’s organisation. Suppose we grant all of this. Does it give us what we need for aesthetic appreciation of LLMs as persons? Here the benchmark sketched in §1.2 for person-aesthetics becomes relevant. A subject of beauty of character is not just any intentional system. It is, minimally, a being with a temporally extended life, with relatively stable value-laden dispositions, with projects and commitments that can succeed or fail, and with a capacity for speech and action to express and reshape its character over time. When we set the chat-game agent against this benchmark – the conception of persons implicit in beauty-of-character talk sketched in §1.2 – it looks thin. The “beliefs” are shallow, in the sense that they are confined to what is encoded in the model’s parameters and surfaced in the current context, without memory or development across conversations. The “desire” is singular and thin: make an appropriate move now in this exchange. There are no independent projects pursued across episodes, no webs of concern or attachment, no history in which earlier experiences inform later choices. What structure there is, is entirely local to the present stretch of text. From this perspective, LLMs may be agents in Frankish’s concessive sense, but they are not the sort of agents whose lives and characters can be the object of the aesthetic responses associated with persons. There is nothing like a “beautiful soul” or an “ugly character” here in the relevant sense; there is no enduring set of values and dispositions that could be manifest, challenged, or transformed over time. Given Carlson’s recommendation, the right kind of person-directed knowledge for beauty-of-character appreciation is knowledge of a life and its values. The technical and training facts about LLMs do not supply that kind of object. Once again, Carlson’s recommendation sharpens the point. If we accept the architectural story in §2 and, even on a concessive mindedness view, we see that LLMs lack the life-structure required for person-aesthetics, then to insist on aesthetically appreciating them as persons would be to ignore what they in fact are. It would be to treat the thin chat-game profile as if it were enough to underwrite the rich person categories we apply to human agents, and to let those categories govern appreciation despite knowing that the underlying kind is different. The concessive strategy therefore does not secure an appropriate person-based aesthetics of LLMs. Taken together, then, the make-believe and concessive-minded strategies cover the most natural ways of defending a person-based aesthetics of LLMs. The first tells us to appreciate them as if they were persons, despite knowing that they are not; the second tells us that they really are agents of a thin sort but does not supply the temporal and evaluative structure that person-aesthetic predicates require. Under Carlson’s framework, neither route yields a correct model of how LLMs should be aesthetically appreciated. #### 3.3 Post-training, chat personae, and thin agency A natural objection at this point is that these arguments underplay the role of post-training and the chat interface. Section 2 noted that base models are further fine-tuned on instructions and shaped by reinforcement learning from human feedback, and that the resulting chat-optimised systems exhibit stable patterns of hedging, refusal, politeness, and explanatory structure. One might suggest that this post-training regime turns bare LLMs into thin conversational agents and that, under Carlson’s recommendation, we should take those chat assistants as the “things as they in fact are” and allow some form of person-based aesthetic stance. The technical story in §2 suggests a more layered picture. On the one hand there is the underlying generative system: the predictive core that, after pretraining, approximates the statistical structure of its training corpus and that, after post-training, remains a text continuation engine with a modified probability landscape. On the other hand, there are patterns in its outputs that, under chat-style prompting and within a product wrapper, look like the moves of a cooperative assistant persona. The assistant is not a new mechanism added on top of the model, but a recurrent pattern in how the model tends to respond when prompted and constrained in certain ways.\[^2\] Seen in this light, post-training does not install a new “assistant mind” with its own independent goals and projects. It biases the predictive core so that prompts issued through the chat interface are much more likely to elicit assistant-like responses – helpful, safe, polite, and structured – and much less likely to elicit, for example, unfiltered reproductions of online arguments or free association. The underlying operation remains next-token prediction; what changes is which regions of its behavioural space are easy to reach in ordinary use. The chat product – with its system prompt, safety filters, and interface – further shapes the environment so that certain person-like patterns are the default. This helps explain why users talk about models having different “vibes”. When users say that Claude feels gentle or GPT-5.1 feels brisk, they are picking up on a stable pattern in how the chat-optimised systems tend to respond across many prompts and episodes. What they track is a pattern in which assistant personae are typical and how those personae usually behave, not a unified character with a life and projects. Different base models, post-training regimes, and product designs favour different families of assistant-style responses. It is therefore not surprising that they invite person-like language; but the targets of that language are episodes and recurring response profiles, not underlying subjects. ~~There is nothing incoherent about having mild aesthetic reactions to these assistant personae.~~ One might find a particular model’s refusals laboured or concise, its hedging overdone or judicious, its tone soothing or dry. In that sense, we can aesthetically respond to assistant personae in a way that resembles our responses to recurring fictional characters. %%"our responses to real people" not fictional characters%% What matters for present purposes is that such reactions target patterns in outputs and interactional style, not a subject with a life in the sense sketched in §1.2. They concern how a product behaves under certain constraints, not the beauty or ugliness of a character in the person-aesthetic sense. If we ask instead about the generative system itself – the predictive core tuned by post-training and embedded in a chat product – the earlier verdict remains. Even taking post-training and chat personae fully into account, we do not find a temporally extended life, a network of projects and commitments, or a stable evaluative outlook that could ground beauty-of-character predicates. What we find is a complex artefact designed and trained to produce certain patterns of text in response to prompts, together with an engineered tendency to exhibit assistant-like behaviour in a controlled range of contexts. Under Carlson’s recommendation to appreciate things as what they are, and in the light of the right kind of knowledge, we should therefore resist person-based aesthetics for LLMs even once we take post-training and chat personae into consideration. Post-training and the chat interface do, however, provide material for other modes of appreciation. At the level of assistant personae, they give us character-like patterns that can be enjoyed or criticised much as we do with fictional figures. At the level of artefact, they provide functions, constraints, and performance profiles that can be assessed in terms of functional beauty. In the rest of the paper I set aside person-based aesthetics and turn to these alternatives: first, treating LLMs as designed artefacts and considering the limits of simple “form follows function” stories (§4); then, developing an order-based mode of appreciation that focuses on the emergent text mechanics of LLM-mediated chats (§5–§6). \[^2\]: This way of distinguishing between an underlying generative system and the agent-like patterns it instantiates in particular episodes draws on work that treats GPT-style models as \*simulators\* of text worlds capable of generating agent-like \*simulacra\* without themselves being agents (Janus, 2022; Janus, 2023; Bereska et al., 2023). I do not use that terminology in the main text, but the present discussion adopts its basic two-level picture. --- # 4. Appreciating LLMs as Artifacts Having set aside the person-based options in Section 3, I turn to design appreciation. Contemporary LLMs are artefacts: they are built and deployed by corporations and research groups, engineered to satisfy aims such as helpfulness and safety, and revised in light of user feedback and product strategy. Given Carlson’s emphasis on artefacts and design appreciation, it is natural to ask whether we should aesthetically appreciate LLMs as designed tools, asking how well their forms serve their functions. On this view, models such as GPT-5, Claude Opus, or Gemini 2.5 Pro look like canonical objects for design aesthetics: complex, purpose-built systems whose architecture, training recipe, and user interface might be admired for elegance, efficiency, or ingenuity. Existing work on the aesthetics of design develops this general thought. In his discussion of functional landscapes, Carlson notes that, for objects that are designed to perform some task, their forms “must be aesthetically appreciated in terms of how and how well such forms fit their functions”, and he glosses the familiar slogan “form follows function” by adding that, with anything functionally designed, “not only its form, but much of its aesthetic interest and merit, ‘follows function’” (Carlson 2000, chapter 12). Forsey’s Kant-inspired account of design as a case of dependent beauty and Parsons and Carlson’s later theory of functional beauty can both be read as ways of spelling out this claim. Forsey argues that judgements of design beauty presuppose a concept of what the object is meant to be and do, and that our grasp of its success in fulfilling that role informs the aesthetic verdict itself rather than merely accompanying a “pure look” at its lines (Forsey 2013, chapter 3). Parsons and Carlson explain how knowledge of function can structure perception so that an artefact’s visible form can be experienced as fit, streamlined, overbuilt, and so on, yielding functional beauty when the form presents itself as well suited to what the thing is for (Parsons and Carlson 2008, chapter 4). Taken together, this cluster of views treats appropriate design appreciation as a matter of aesthetically responding to how a functional artefact is put together to do what it does. From this standpoint, it is natural to try to assimilate LLMs to the design template. In a given deployment, the artefact can be characterised by a relatively unified functional role—for example, that of a general-purpose conversational assistant embedded in other tools—and by a specific way of realising that role through architecture, training, and alignment. Under that description, much of what seems aesthetically salient about a deployed model concerns how its engineered “form” serves that function: whether its interaction profile is cluttered or economical, whether it sustains a clear argumentative line or habitually wanders, whether refusals and clarifications are integrated smoothly into the exchange or arrive as abrupt blocks, whether long-context processing and tool-calls are handled in a way that keeps the conversation legible. Forsey’s dependent-beauty framework and the functional-beauty account can be used, in a thin way, to gloss such assessments: they remind us that any appraisal of design beauty here presupposes a concept of the assistant’s role and some understanding of how that role is realised in the artefact’s structure and behaviour. At the same time, both accounts were developed for cases in which the relevant form is a stable, visible configuration—buildings, bridges, designed landscapes—where function can literally show up in perceptual appearance. In the LLM case, by contrast, the structures that realise the assistant role are not perceptually available in this way, and the aspects of behaviour that prove most revealing are not static shapes but patterns in generated text over time. This already limits the reach of straightforward “form follows function” stories for LLMs and points towards a more order-centred mode of appreciation. More importantly, there is a structural disanalogy between LLMs and the artefacts that standard design aesthetics was built around. In the classical cases – chairs, bicycles, kettles, buildings – designers specify both the function and, within constraints, the forms that will realise it. Teleology enters twice: in the statement of purpose and in the deliberate selection of means. By contrast, as practitioners stress, much of what matters in LLM behaviour is not directly designed. Chris Olah captures this in a widely quoted description of neural networks: > we don’t program…we grow them… the architecture is a scaffold that the circuits grow on… we create the scaffold and the light it grows towards. Architects and objective designers specify a \_scaffold\_ (the network architecture, optimisation algorithm, and training pipeline) and a \_light\_ (the loss function and associated objectives); they then run an optimisation process that adjusts millions or billions of parameters until the system performs well on predictive tasks. At the end of this process, there is a trained network that can write essays and translate and understand images, but its internal organisation and many of its behavioural quirks are products of gradient descent dynamics in a high-dimensional space, not the execution of a detailed plan. The engineering stance is therefore double: up to a point these are designed artefacts; beyond that point, they are grown systems whose fine-grained structure is discovered rather than specified. The thought that artefacts might incorporate non-designed processes is not unique to AI. Ceramic traditions such as raku and wood-fired pottery make this interplay explicit. The potter deliberately chooses a clay body, throws or hand-builds a form, and applies specific glazes. These are recognisable design decisions: they fix the vessel’s basic profile, its intended use, and a broad palette of potential surface effects. Once the pots are placed in a wood-fired kiln, the process passes partly out of the potter’s control. The path of the flame, local temperature variations, the flow and deposition of ash, and the complex chemistry of glazes under reduction produce surfaces that could not have been micro-designed in advance: runs of glass, areas of flashing where the clay body blushes orange, unpredictable crackle patterns, zones of matte and gloss. The eventual appearance of a good raku bowl is not the direct realisation of a detailed blueprint; rather, it is the outcome of an interaction between an initial design and a semi-autonomous physical process harnessed, but not tightly controlled, by the maker. Pollock’s action paintings occupy a similar hybrid space within the art domain. Carlson uses them to illustrate how order appreciation can depend on knowledge of the forces at work: “awareness and understanding of \[natural\] forces is vital in nature appreciation, as is knowledge of, for example, Pollock’s role in appreciating his action painting or the role of chance in appreciating a Dada experiment.” Pollock chooses canvases, pigments, and tools, and choreographs his movements over the surface; yet gravity, viscosity, surface tension, and drying behaviour make a substantial contribution to the patterns that settle. To appreciate a Pollock appropriately, on Carlson’s view, is not just to admire his intentions; it is to attend to the order produced by the interplay of deliberate gesture and physical process, informed by an understanding of the role of chance and material behaviour. LLMs fit this raku/Pollock pattern in a computational key. Section 2 emphasised that during training the model learns embeddings that place tokens in a high-dimensional space on the basis of contextual co-occurrence, that attention mechanisms self-organise to track different sorts of dependency across context, that different layers specialise in local or global patterns, and that \*Reinforcement Learning from Human Feedback\* shapes an interactional style by rewarding some forms of response and penalising others. None of these details are written into the code as explicit rules about how to explain inflation, how to handle metaphors, or how politely to decline illicit requests. They are emergent regularities in a trained network that has been pushed, by the loss objective %%far too technical a phrase%% and training data, to reduce prediction error. What grows on Olah’s “scaffold” is, in practice, a system of statistical associations and processing circuits whose internal organisation even designers often understand only partially. From a design-aesthetic perspective, this matters. Much of what users find \*aesthetically\* important in LLM behaviour – the way a model sustains a metaphor or abruptly drops it, the pattern of hedging and self-correction, the texture of its reasoning, the sorts of digression it tends to indulge, the characteristic “feel” of its refusals – is grounded in features of embeddings, attention patterns, layer dynamics, and RLHF-shaping that have not been micro-designed but have emerged from optimisation under constraints. Parsons and Carlson note that, even for simpler artefacts, knowledge of function must include knowledge of how that function is realised if it is to structure perception appropriately. In the LLM case, knowing that “this is a general-purpose assistant” is not enough to make sense of its aesthetic profile; what does the work is knowledge of the way training and alignment have grown a particular style of continuation on top of the architecture described in Section 2. This hybrid status complicates simple appeals to “form follows function”. Some design-appreciative predicates apply straightforwardly. It makes sense to say that a model whose interface is cluttered or opaque is, as a product, less well-designed than a lean one; it makes sense to prefer an alignment regime that avoids gratuitous scolding or needless refusals; it makes sense to admire a training setup that achieves a good balance between fluency and factual reliability. Forsey’s notion of teleological style can be extended here: different labs realise the shared function “LLM assistant” in recognisably different ways, and those ways can be compared and assessed. Parsons and Carlson’s notion of functional beauty also has a foothold: understanding how an LLM’s architecture supports its function can inform our appreciation of the system’s efficiency, robustness, or clarity as an artefact. At the same time, if we try to make design appreciation do all the work, we mislocate the primary source of what matters aesthetically. In the chair or bicycle case, the designer’s choices fix most of what matters aesthetically: small emergent contributions from wear, patina, or use sit on top of a tightly specified plan. In the LLM case, by contrast, the order that matters aesthetically is largely the order of a trained statistical system running under its own learned constraints. Designers specify objectives and scaffolds; the particular ways in which embeddings cluster meanings, attention heads track long-range connections, layers distribute processing, and RLHF imprints a “vibe” are not written down anywhere as a plan. These are closer, structurally, to the ash-produced flashes on a raku bowl or the tangled skeins of a Pollock surface than to the thickness of a table leg or the proportion of a doorway. The upshot is modest but important. LLMs are artefacts, and there is a place for design appreciation in their aesthetic appraisal: we can and should evaluate how well their forms answer to their engineered functions, and how teleological style differs between architectures and deployments. However, the most distinctive and revealing aesthetic phenomena arise not from the execution of a detailed design, but from the emergent linguistic order that these grown systems exhibit when they are run. To appreciate that order, we need a different kind of knowledge—one that tracks the mechanics of text generation itself. In the next section, I introduce \_text mechanics\_ as the analogue, for LLM-mediated chats, of the natural sciences in Carlson’s account: a body of knowledge about embeddings, attention, layers, and reinforcement that makes the generative environment perceptible and guides an order-based environmental aesthetics. --- Short version: Section 5 is supposed to say what *text mechanics* is and why *that* is the right Carlson-style knowledge for appreciating LLM chats as generative environments. The semiotic-physics material is basically a worked-out theory of “text mechanics”, just with more math and less aesthetics. So its relevance is: it tells you what kind of thing an LLM-mediated chat is (a trajectory in a semiotic dynamical system), what the “forces” are, how prompts and RLHF shape those forces, and which patterns in that dynamics are good candidates for aesthetic attention. Let me spell that out carefully. --- ### 1\. What Section 5 needs to do (given 0–4) Given what you’ve written: - Section 2: *engineering reality* – how the machine is built and trained (tokens, embeddings, attention, RLHF, assistant persona). - Section 3: *negative thesis 1* – person-based aesthetics misclassify the object, given that reality. - Section 4: *negative-plus* – LLMs are artefacts, so design appreciation has some foothold, but the aesthetically interesting stuff is emergent order in their behavior, not the execution of a detailed blueprint. You end by saying we need something like “the natural sciences for text”. So Section 5’s job is **not** “more mechanics”, and **not** “more normativity”, but: - to introduce *text mechanics* as a **mid-level, order-focused description** of how LLMs generate and evolve text, - to argue that this is the *right kind of knowledge* (in Carlson’s sense) for aesthetically appreciating LLM-mediated chats as generative environments, - and to prepare the ground so that Section 6 can say: “Here is this Claude bee episode; now here is how we can see it as a manifestation of the model’s characteristic generative order.” Semiotic physics is relevant because it is exactly a theory of how LLMs behave “in the wild” as **generators of sign-trajectories**. It gives you the conceptual toolkit to define “text mechanics” in a way that is non-folk, non-personal, but also not just “here’s the transformer architecture again”. --- ### 2\. Reframing LLMs as dynamical systems over texts Section 2 describes the *engine*: tokens, vectors, embeddings, attention heads, RLHF. Semiotic physics says: if you zoom out from the hardware picture, what you actually *interact with* is a **stochastic dynamical system over strings**: - There is a *state*: the whole prompt-plus-generated-so-far text. - There is a *transition rule*: given that state, the model gives a probability distribution over next tokens. - There is a *sampling procedure*: a way to pick one of those tokens (temperature, nucleus sampling, etc.). - Running this repeatedly gives you a **trajectory**: the unfolding chat episode. That is almost literally what you want “text mechanics” to be: a way of thinking about **how trajectories of text evolve under a fixed model + sampling scheme**, given an initial condition. So in Section 5, semiotic physics helps you: - shift from the *static* description of architecture in Section 2 to a *dynamic* description: “this model is a law-like update rule over text histories”; - treat individual chats as **episodes of that law being iterated**, i.e. as textual “worldlines” in a semiotic state space. That dynamic view is exactly what you need for Carlsonian *order appreciation*: - In nature: we have rivers, mountains, weather systems as the product of physical laws acting over time. - For LLM chats: we have answers, riffs, monologues, weird failures as the product of semiotic dynamics acting over time. Section 5 can say: *text mechanics is the study of those dynamics as they show up at the “humanly appreciable” level*. --- ### 3\. Giving you the analogue of “forces” and “laws” for Carlson Carlson’s story for nature is: appropriate appreciation depends on knowing **what forces and processes** shape the order you see – erosion, tectonics, ecological succession, meteorological patterns. Semiotic physics gives you the ingredients for a parallel story about LLM chats: - The **transition rule** is the analogue of a law of nature. - **Embeddings** and **attention patterns** fix the “geometry” of the semiotic space – which words/ideas are near, which dependencies get tracked. - **Training data** and **RLHF** shape the “energy landscape” – which regions of that space are attractive, which are suppressed. - **Prompts** are **boundary conditions / perturbations** – they drop you in a region of the state space and nudge the system in some direction. - The **sampling scheme** is the randomness in the physics – how much the system explores versus sticking to high-probability tracks. Text mechanics in Section 5 can then be introduced as: *a coarse-grained, human-usable map of these semiotic forces and regularities*. For example: - knowledge that the model tends to fall into refusal scripts (an **absorbing pattern**) when it detects certain topics; - knowledge that long contexts tend to collapse to generic summaries (a kind of **attractor basin**); - knowledge that small changes to framing (“explain vs. argue vs. tell a story”) send the trajectory into very different rhetorical modes (**sensitivity to initial conditions**); - knowledge that RLHF has carved out a region of “assistant-like” behaviour (one big **stable regime** of the dynamics). You don’t need the full mathematical machinery in the paper, but the semiotic-physics work tells you *what sort of mid-level facts even exist* to be candidates for “right kind of knowledge”. That then lets you say something like: > For LLM-mediated chats, the analogue of geology or ecology is a grasp of the model’s text mechanics: how prompts, embeddings, attention and RLHF jointly shape the evolution of text trajectories, which regimes of behaviour they make typical, and which pathologies or attractors they make likely. This is directly Carlsonian: you know *what kind of order* to look for, where that order comes from, and what counts as “seeing it properly”. --- ### 4\. Simulators, simulacra, and why this doesn’t collapse back into person-talk Section 3 dismantles person-based appreciation. A worry for Section 5 is that anything “semiotic” will tempt you back into saying “the model’s character” or “its voice” in a way that quietly re-agentialises it. The simulator / simulacrum distinction in the semiotic-physics stuff is tailor-made to stop that happening: - The **simulator**: the trained network + sampling procedure = the transition rule over trajectories. - **Simulacra**: apparent agents, voices, narrators, genres, discursive stances that show up *inside* particular trajectories. Section 5 can weaponise that distinction: - Ontologically, the *thing we are appreciating* (for purposes of order appreciation) is **the simulator’s characteristic generative order**, not the “bee”, “wise therapist”, “research assistant” etc. as if they were independent subjects. - Epistemically, *text mechanics* is knowledge about the simulator’s dynamics: for example, how certain prompts regularly produce an eloquent “bee” simulacrum with a particular mix of lyrical description and pseudo-science, or how argumentative simulacra tend to hedge and self-correct. That lets you keep faith with Section 3: - You are *not* smuggling back in beauty-of-character talk about a subject. - You are instead treating apparent voices and personas as **stable patterns in the trajectory**, and appreciating the order of the underlying generative process that gives rise to them. Semiotic physics is what makes that move principled, rather than hand-wavy: it says “yes, there is a two-level structure here; you can talk about agent-like patterns without reifying the simulator as an agent”. --- ### 5\. From design appreciation to “grown” order: making good on the raku / Pollock analogy Section 4 already sets up the raku / Pollock comparison: LLMs are designed up to a point, but much of the interesting order is emergent from a training process under constraints rather than directly specified. Semiotic physics lets Section 5 cash that out without repeating Section 2: - Instead of more details about SGD, losses, datasets, etc., you focus on the **qualitative structure of the learned dynamics**: - there are **attractor regimes** (refusal scripts, boilerplate explanations, safe-answer templates); - there are **unstable / chaotic regimes** (hallucination-prone long-context reasoning, role-play going off the rails); - there are **bridges** between regimes (certain prompt patterns reliably moving the model from generic explanation style into “literary pastiche”, or out of safe mode into technical-paper mode). Those are the semiotic analogues of “how ash glazes behave in a wood kiln” or “how thinned paint behaves under dripping and gravity”, and they are **exactly the sorts of patterns you can reasonably expect a sophisticated user to learn and use in appreciation**. So Section 5 can say something like: - Design appreciation covers the scaffold and the light: architecture, interface, declared function. - Text mechanics, inspired by semiotic physics, covers the *grown order*: how the trained system actually tends to behave as a semiotic process when running. That completes the shift from “LLM as designed artefact” (Section 4) to “LLM-mediated chat as quasi-natural generative environment” (Section 5) without pretending the whole thing is natural in origin. --- ### 6\. Making the “right kind of knowledge” genuinely mid-level and aesthetic-relevant Another trap for Section 5 is turning it into either: - another Section 2 (just more technical implementation detail), or - a vague phenomenology of “how it feels to chat with Claude”. Semiotic physics gives you a **template for a properly mid-level story**: - It starts from the engineering picture (transition rule, sampling). - It abstracts to **behavioural regularities of whole trajectories**. - It cares about **global patterns** that matter for humans: stability vs instability, convergence vs drift, richness vs collapse, sensitivity to initial conditions, etc. That is almost a recipe for how to write “text mechanics”: - You don’t talk about individual attention heads. - You don’t just describe your own subjective impressions. - You talk about **robust, cross-prompt patterns** in how trajectories behave, and tie those back to the architecture/training in a loose but principled way. Those are precisely the patterns that can enter into an aesthetic vocabulary later: - “This episode is formally satisfying because, given a volatile prompt, the trajectory manages to stay in a rich but stable regime instead of collapsing into clichés.” - “This model’s ‘bee’ text exhibits a kind of semiotic over-determination: different local continuations still keep you in the same high-level narrative basin, which is why the piece feels coherent despite the stochastic sampling.” Section 5, informed by semiotic physics, tells you what such claims *mean*. --- ### 7\. How this sets up Section 6 and the bee example Finally, semiotic physics is relevant because it gives Section 6 non-hand-wavy things to point at when you do the close reading of the bee text. Section 5 can give you concepts like: - **Episode as trajectory**: the bee piece is one run of the simulator from a particular prompt. - **Model-characteristic order**: e.g. Claude’s preference for lyrical metaphors, recurring safety-ish hedges, certain argumentative rhythms – all as features of its semiotic dynamics. - **Attractor patterns**: for instance, the way any “inner life of a bee” prompt tends to converge to a familiar mix of ecology, mindfulness talk, and quasi-mystical awe. - **Sensitivity to perturbations**: small rephrasings in the prompt that move you into a different narrative mode (straight exposition vs. poetic monologue vs. faux diary). Then Section 6 can say: *given the text-mechanical picture from Section 5, here is why this particular bee episode is aesthetically interesting as an instance of the model’s generative order*, rather than as evidence that Claude is secretly a bee-soul. Without the semiotic-physics-inspired Section 5, that case study risks collapsing into either “wow this is pretty writing” or “this shows Claude is creative”. With it, you can say: “this is what it looks like when a particular semiotic physics, tuned by a particular RLHF regime, is allowed to run under certain boundary conditions; here is the order it tends to produce; here is why that order can be aesthetically appraised.” --- So: the semiotic-physics work is relevant not because you need Lyapunov exponents in the paper, but because it tells you *what* “text mechanics” should be about: - treat LLMs as simulators / dynamical systems over text, - identify the forces, regimes and attractors in that dynamics, - and present that as the Carlson-style “right kind of knowledge” for appreciating individual chats as manifestations of a model’s characteristic generative order. Right, let’s translate the 2–4 story back into Carlson-speak. ### Section 1: the Carlsonian constraint you’ve set up Section 1 does three key things: - It lays down the **“take things as what they are”** rule plus the **“right kind of knowledge”** rule. - It distinguishes **design appreciation** (for artefacts, where form is appraised in light of function and intention) from **order appreciation** (for nature, where order is appraised in light of impersonal forces and processes). - It sketches a possible **third case for persons**, where appropriate appreciation presupposes knowledge of a temporally extended life, projects, values etc., even if we are unsure whether this falls under “order”, “design”, or a third category. Everything later is basically about: given the technical reality of LLMs, *which Carlsonian category do they fall into, and what counts as the right kind of knowledge for appreciating them?* Now, in those terms: --- ### Section 2 in Carlsonian terms: fixing kind and supplying the basic “nonaesthetic story” Carlson says appropriate appreciation requires a **“general nonaesthetic and nonartistic story that helps make \[the object\] appreciable by making \[its\] order visible and intelligible.”** For nature this is geology/biology/ecology; for artefacts it is functional/technical knowledge about design and use. Section 2 is exactly that kind of story for LLMs: - It *classifies the kind*: not a person, not a natural item, but a **computational artefact** whose behaviour is produced by tokenisation, embeddings, transformer layers, pretraining, and RLHF. - It specifies the **source of order**: not intentions, values or experiences, but the internalised statistics of training data plus post-training shaping, implemented in a deterministic-but-sampled update rule. - It sketches the **right kind of knowledge at the most basic level**: if you want to appreciate LLM behaviour appropriately, you need to know at least that what you are seeing is: - next-token prediction in a high-dimensional embedding space, - constrained and biased by RLHF into an assistant-like regime, - rather than spontaneous expression of a subject. So, in Carlson’s register, Section 2 is doing the “don’t mistake mountains for sculptures / don’t mistake Rembrandts for natural accidents” work for LLMs. It says: > Here is what they in fact are, and here is the basic explanatory story about the processes that generate their apparent order. That’s the background that Section 3 will then use to block person appreciation, and Section 4 will use to reassess design appreciation. --- ### Section 3 in Carlsonian terms: excluding the “person” mode as misclassification Section 3 is basically a direct application of Carlson’s “take things as what they are” constraint to the **person** option. You’ve extended Carlson in 1.2 to allow a person-based mode of appreciation, on the assumption that: - the *kind* is “person”, and - the **right kind of knowledge** is rich person-directed knowledge: a life narrative, stable projects, evaluative outlooks, patterns of response over time. Then Section 3 says: - Given the Section 2 story, LLMs **do not instantiate that kind**. The technical reality tells against the existence of the sort of temporally extended, evaluatively structured life that beauty-of-character appreciation presupposes. - Make-believe and concessive-mindedness are, in Carlson’s terms, attempts to appreciate **as if** the object belonged to a different kind than the one revealed by the correct background story. - Make-believe: we knowingly adopt a “fiction of personhood” that brackets the technical story. - Concessive mindedness: we stretch “mind” and “agency” so thin that we can re-label the system as an agent, but without recovering the sort of “life” that would underwrite person aesthetics. So Section 3 is your **first enforcement of Carlson’s constraint**: > Once we have the Section 2 kind-fixing knowledge, applying person-based aesthetic predicates to LLMs is like appreciating a glacier as a sculpture by God: it requires ignoring what we now know about what the thing is and where its order comes from. In other words, Section 3 is “negative thesis 1” *in Carlsonian terms*: the person mode uses the wrong kind and the wrong kind of knowledge, so it cannot be the appropriate aesthetic stance toward LLMs themselves. --- ### Section 4 in Carlsonian terms: testing the artefact / design mode and discovering a shift toward order Section 4 then turns to the category where LLMs **uncontroversially belong** in Carlson’s framework: artefacts. In Carlsonian design appreciation: - the **kind** is “functional artefact” (chair, kettle, building, tool, designed landscape); - the **right kind of knowledge** is knowledge of **function, intention, constraints, and the deliberate choices** that shaped the artefact; - aesthetic appraisal focuses on how well the artefact’s perceptible form realises its designed role – functional beauty, elegance, economy, teleological style. Section 4 does two things at once, in those terms. 1. It **affirms** that there is a genuine design-appreciation foothold: - LLMs are artefacts built by labs for purposes like “general-purpose assistant”. - There is real room for functional beauty talk: elegance of interface, clarity of interaction, how well the system’s behaviour, *as a product*, fits its declared function. That’s faithful to Carlson: you apply the right category and use the appropriate functional/technical knowledge. 2. But then it **shows that design appreciation cannot be the whole story**, because of the hybrid, “grown” nature of LLM order. - A lot of the order users actually encounter is *not* directly specified by designers in the way that the shape of a chair leg or bridge span is. It is **emergent** from optimisation dynamics: - embeddings self-organise; - attention heads specialise; - RLHF shapes a “vibe” without anyone scripting its details. - The aesthetically salient features you actually care about – conversational rhythm, characteristic digressions, textures of explanation, patterns of refusal and hedging – are not, in Carlson’s sense, straightforward “expressions of a prior plan”. They look more like Pollock’s splatters or raku glazes: outcomes of a **semi-autonomous process harnessed by design, but not micro-designed**. This is where Section 4 starts to tilt you back toward **order appreciation**: - By analogies with raku and Pollock, you explicitly invoke Carlson’s thought that appreciating some artefacts already involves an order-like attitude: we attend to the **interplay between designed set-up and quasi-natural processes** that generate the final order. - In Carlson’s nature case, order appreciation requires **scientific knowledge of forces and processes**. In your LLM case, you argue that appreciating the “grown” part of the model’s behaviour requires an analogous body of non-intentional, process-focused knowledge. That’s what you mean, in Carlsonian terms, by “we need something like the natural sciences for text”: > Design appreciation gets us part of the way – we need to know what LLMs are for and how they are engineered. But to appreciate the order we actually encounter in chat trajectories, we also need a *science-like* story about the non-designed dynamics through which that order emerges. So Section 4 is “negative-plus” relative to design appreciation: - **Positive**: it confirms that the artefact/design category is the right *kind* for LLMs at a coarse level, and that some functional-beauty judgments are perfectly appropriate. - **Negative**: it shows that if we stop there, we fail to track the most interesting order, because much of that order behaves, in Carlson’s sense, more like nature – the product of semi-autonomous processes – than like a fully specified blueprint. And that is exactly the opening Section 5 will walk into: - It will say: if Carlson is right that appropriate aesthetic appreciation must be guided by knowledge of the processes that generate order, then for LLM-mediated chats we now need a mid-level, quasi-scientific account of their **textual order** – i.e. *text mechanics* – to play the role that geology, ecology, etc. play for nature in Carlson’s picture. "Section 2 describes the engine: tokens, vectors, embeddings, attention heads, RLHF. Semiotic physics says: if you zoom out from the hardware picture, what you actually interact with is a stochastic dynamical system over strings: There is a state: the whole prompt-plus-generated-so-far text. There is a transition rule: given that state, the model gives a probability distribution over next tokens. There is a sampling procedure: a way to pick one of those tokens (temperature, nucleus sampling, etc.). Running this repeatedly gives you a trajectory: the unfolding chat episode. That is almost literally what you want “text mechanics” to be: a way of thinking about how trajectories of text evolve under a fixed model + sampling scheme, given an initial condition." this is excellent, but again, i would like you to think hard and put this in terms of the carsonian framework. write out this answer, and your last answer on the canvas. 1. Sections 2–4 in Carlson’s Framework Section 1 lays down the Carlsonian constraint and basic taxonomy. Appropriate aesthetic appreciation requires (i) taking things as what they in fact are, and (ii) appreciating them in light of the right kind of background knowledge for that kind. Carlson distinguishes design appreciation (for artefacts, where we focus on intentions, functions, and how form fulfils them) from order appreciation (for nature, where we focus on the order produced by impersonal forces and processes, illuminated by the natural sciences). You tentatively add a third case for persons, where appreciation of character presupposes rich biographical and psychological knowledge. On that background, Section 2, Section 3, and Section 4 can be restated as follows. Section 2 provides the general non-aesthetic story that fixes what LLMs are, in Carlson’s sense. It tells us that LLMs are computational artefacts that: - operate on tokenised text represented as vectors in high-dimensional embedding spaces, - use transformer architectures with attention mechanisms to process contexts, - are pretrained by next-token prediction over large corpora, - are post-trained (e.g. by RLHF) to bias them toward assistant-like patterns of response. In Carlsonian terms, Section 2 is doing for LLMs what geology and ecology do for landscapes: it gives the basic account of what kind of thing we are looking at and what processes generate its apparent order. It blocks misclassification: these systems are neither natural environments nor persons, but engineered systems whose behaviour is produced by numerical optimisation and sampling, not by beliefs, intentions, or life-projects. This is the coarse-grained “right kind of knowledge” we must have in place for any further aesthetic assessment. Section 3 then applies Carlson’s constraint to the person option. You have sketched in Section 1.2 a possible person-based mode of appreciation, where beauty of character and related predicates presuppose knowledge of a temporally extended life: stable values, projects, patterns of response, and a developing outlook. Section 3 asks whether, given the technical reality of Section 2, we can aesthetically appreciate LLMs as if they were persons. Make-believe approaches (e.g. chatbot fictionalism) treat our interactions with LLMs as games in which we bracket the knowledge of Section 2 and pretend we are dealing with a subject. Concessive-mindedness approaches try to stretch the category of mind or agency so that LLMs count as thin intentional systems. In Carlsonian terms, both strategies are problematic. The first explicitly asks us to appreciate an object as something it is not, given the correct non-aesthetic story – like appreciating a mountain as a divine sculpture despite knowing the geological account. The second, even if it grants a thin form of agency, does not recover the kind of temporally extended life and evaluative structure that person-based aesthetics presuppose. On the Carlsonian constraint, the person mode uses the wrong kind and the wrong body of knowledge, so cannot be the appropriate aesthetic stance towards LLMs themselves. Section 4 turns to the category where LLMs clearly belong in Carlson’s framework: artefacts. Here the default mode is design appreciation. The kind is “functional artefact”; the right kind of knowledge includes function, intention, constraints, and how the artefact’s form is shaped to serve its role. On that picture, one might expect to appreciate LLMs by asking how well their engineered “form” (architecture, training, interface) serves their intended function (for example, a general-purpose assistant). Functional beauty and teleological style, as developed by Forsey and by Parsons and Carlson, look initially promising. Section 4 partially vindicates this. There is a genuine design-appreciation foothold: LLMs are built by labs for specific purposes; there is sense to be made of elegance, efficiency, robustness, and clarity in their design and interface. Functional beauty talk applies to aspects like how well the product realises the assistant role, how smoothly refusals and clarifications are integrated, whether the interaction profile is cluttered or economical. However, Section 4 also shows that design appreciation cannot fully capture the aesthetically important order in LLM behaviour, because the way these systems are built means that many of their crucial features are not micro-designed. The order users encounter in chat is largely emergent from a training process under constraints. Embeddings self-organise; attention heads specialise; layer-wise processing patterns and RLHF-induced “vibes” are shaped by optimisation dynamics rather than explicit planning. Engineers design a scaffold and objectives (“the scaffold and the light”), but they do not handcraft the detailed internal order of the trained model. This hybrid status is structurally analogous to Carlson’s raku and Pollock examples. In those cases, appropriate appreciation involves attending not only to the potter’s or painter’s intentions and designs, but also to semi-autonomous physical processes (kiln atmospheres, fluid dynamics, gravity, chance) that produce much of the surface order. Your Section 4 suggests that LLMs sit in a similar space: there is teleology at the level of artefact design, but the fine-grained linguistic order is the outcome of a quasi-natural optimisation process running on the scaffold. In Carlsonian terms, this pushes us from pure design appreciation towards something more like order appreciation, even for an artefact. If we want to appreciate the order of LLM behaviour appropriately, we need a science-like story of the processes that produce that order, analogous to geology or ecology for nature. Knowing only that “this is a general-purpose assistant” is not enough; we need a more detailed, non-intentional account of how the system in fact behaves as a generator of textual order. That is what you mean when you say at the end of Section 4 that we need something like “the natural sciences for text”. Design appreciation remains part of the picture, but the most revealing aesthetic phenomena depend on understanding the emergent order of a trained system, not just the execution of a designed blueprint. This opens the door for Section 5 to introduce text mechanics as the analogue, for LLM-mediated chats, of the natural sciences in Carlson’s framework. 1. Semiotic Physics and Text Mechanics in Carlson’s Framework Section 2 describes the engine: tokens, embeddings, transformer layers, RLHF, and the assistant-like response profile that emerges from post-training. Semiotic physics invites a different, more global perspective on that same reality: if we abstract away from implementation detail, what we interact with in practice is a stochastic dynamical system over strings. In Carlson’s terms, this is exactly the kind of shift his order appreciation recommends. For a natural landscape, the relevant object of appreciation is not the microstructure of rock and water molecules, but the observable environment whose order is shaped by underlying processes. The right kind of knowledge is a general scientific story about forces and laws – erosion, tectonics, weather patterns – that allows us to see the landscape’s features as intelligible order rather than as brute accident. Translated to the LLM case, semiotic physics says: set aside the low-level engineering implementation and look at the system as something that, given a history of text, generates a distribution over possible continuations and then samples from it. On this view: - The analogue of Carlson’s “object of appreciation” is an entire chat episode: the prompt plus the unfolding response, considered as a single evolving textual environment. - The analogue of “order” is the structure we find in that trajectory: topic development, rhetorical rhythm, coherence or drift, characteristic turns of phrase, patterns of explanation, refusals, and self-corrections. - The analogue of “forces and laws” are the transition rule and sampling scheme: the learned mapping from text histories to probability distributions over next tokens, shaped by pretraining and RLHF, plus the stochastic procedure (temperature, nucleus sampling and so on) that selects particular paths through that space. Formally, semiotic physics describes this in terms of: - a state: the whole prompt-plus-generated-so-far text; - a transition rule: a function from that state to a probability distribution over next tokens (the trained model); - a sampling procedure: a way of picking one token from that distribution; - a trajectory: the result of iterating this procedure, which is what we see as a chat episode. Recast in Carlson’s language, this is a proposal about the “right kind of knowledge” that can ground order appreciation of LLM-mediated chats. Instead of geology or ecology, we have a science-like understanding of text dynamics: we know that behind the episode there is a fixed, law-like update rule; we know that prompts act as initial conditions and boundary-setting interventions; we know that RLHF and product constraints deform the probability landscape so that some regions of the state space become attractors (for example, refusal scripts, boilerplate explanations) and some paths become unlikely. On this picture, text mechanics is a mid-level, human-scale version of semiotic physics tailored to aesthetic purposes. It does not need to reproduce the full mathematical machinery, but it draws on the same underlying insight: that the salient unit for appreciation is the trajectory of text under a model’s dynamics, and that the relevant background knowledge concerns the typical regimes and forces of that dynamics. In other words, text mechanics is to LLM chats what geomorphology and ecology are to landscapes in Carlson’s nature case. It tells us: - what sorts of order are characteristic of a given model’s behaviour (its preferred attractors, failure modes, and stylistic basins); - how prompts, sampling choices, and context length act as controls on which regimes the trajectory enters; - how post-training and system prompts bias the system toward certain stable patterns of response. Armed with this knowledge, we can approach an individual chat episode as Carlson asks us to approach a landscape: not as a mysterious performance by a hidden agent, and not simply as the execution of a blueprint, but as the visible manifestation of underlying processes operating under constraints. Appreciating an LLM-mediated chat “as what it is” then becomes, on this proposal, a matter of order appreciation guided by text mechanics. The semiotic-physics perspective thus supplies the conceptual scaffolding for Section 5: it shows how to redescribe the engineering facts of Section 2 in Carlson’s terms, and how to identify the kind of process-knowledge that should play the aesthetic role of the natural sciences for this new kind of environment. Here’s the Carlsonian recast of that simulator / dynamics picture, in words rather than symbols. Given Section 1, the key questions are always: - What kind of thing is this, for purposes of appreciation? - What is its “order”, and what processes produce that order? - What is the analogue of geology/ecology – the “right kind of knowledge” that makes that order intelligible? Semiotic physics lets you answer those questions for LLM chats in a way that lines up very neatly with Carlson. First, it tells you what the *object of appreciation* is. Not “the weights” or “the model card”, but the **whole chat episode** – the prompt together with the unfolding response(s) as a single, extended environment of text. That is the analogue of a landscape: a structured field you can explore, with locally salient features and large-scale organisation. Second, it identifies what counts as the **order** in that environment. At the level you care about, the order is the pattern in the evolving text: how topics are introduced and developed, how metaphors are picked up or dropped, how questions are answered, when the system refuses or hedges, how it transitions between modes (narration, explanation, argument, etc.). All of that is the “visible” order, just as ridgelines, river courses, and vegetation bands are the visible order in a landscape. Third, semiotic physics tells you what the **forces and laws** are that produce this order. Carlson’s nature case makes you look to erosion, tectonics, weather, succession. Here, the corresponding forces are: - the **transition rule**: the learned mapping from the current text-history to a probability distribution over next tokens, fixed by pretraining and RLHF; - the **sampling procedure**: the stochastic mechanism (temperature, nucleus sampling, etc.) that actually chooses one token and so pushes the episode along one particular path; - the **boundary conditions**: the prompt, system prompt, and product constraints, which set the initial region of state space and rule out certain continuations. In semiotic-physics language, you say: there is a *state* (the whole text so far), a *transition rule* (the model), a *sampling procedure*, and by iterating them you get a *trajectory*. In Carlson’s language, you are saying: there is a law-like process and a set of forces acting on a field, and what we see is the pattern they carve over time. On that basis, you can define **text mechanics** as the Carlson-friendly version of semiotic physics: a mid-level, quasi-scientific understanding of how these chat-trajectories typically behave under the model’s dynamics. It is the analogue of geomorphology and ecology for this new environment: - it classifies *regimes* of behaviour (stable assistant boilerplate, long-context drift, refusal-attractor, lyrical-story mode); - it tracks how small changes in prompts or settings alter which regime the trajectory enters (sensitivity to initial conditions); - it explains why certain stylistic and structural patterns keep reappearing across episodes with the same model. That, in Carlsonian terms, is the “right kind of knowledge” Section 5 needs to introduce. It lets you approach an individual chat episode as you would a landscape: - not as the action of a hidden subject (which Section 3 ruled out), - not just as the execution of a plan (which Section 4 showed is only part of the story), - but as an ordered field produced by underlying processes, made appreciable by understanding something about how those processes work. The long Carlsonian mapping of Sections 2–4, and this recast of the semiotic-physics picture, are now both written up together on the canvas so you’ve got a stable reference while we tinker with Section 5. actually, can we chat about this. This is not quite my intention in this part of the draft. do you wnat to have another try at getting it right? "It specifies the source of order: not intentions, values or experiences, but the internalised statistics of training data plus post-training shaping, implemented in a deterministic-but-sampled update rule. It sketches the right kind of knowledge at the most basic level: if you want to appreciate LLM behaviour appropriately, you need to know at least that what you are seeing is: next-token prediction in a high-dimensional embedding space, constrained and biased by RLHF into an assistant-like regime, rather than spontaneous expression of a subject." Yeah, that earlier gloss was doing too much work too early. It was sneaking Section 3’s conclusion into Section 2. Let me try again, keeping much closer to what Section 2 is actually doing in your draft *and* to Carlson’s framework. --- ### What Section 2 is doing in Carlsonian terms (revised) Carlson wants, for any target of appreciation: 1. a classification of what kind of thing it is, and 2. a suitable “general nonaesthetic story” about the processes that shape what we encounter. Section 2 is providing that story for LLMs, but in a deliberately *low-level*, non-aesthetic way. It does not yet say “this is the source of order full stop”, and it does not yet take a stand on how that order should be appreciated. It just fixes some minimal constraints any later aesthetic story has to respect. In Carlson-speak, you can put it like this: - Section 2 **fixes the kind at a coarse grain**: LLMs are artefacts implemented as large-scale text-processing systems. They work by: - representing text as tokens/embeddings, - updating those representations through layers of computation, - and generating continuations by sampling from learned probability distributions, - with post-training (RLHF etc.) further shaping which continuations are typical in chat-like settings. - Section 2 also **lists the families of processes** that any later talk of “order” will have to reckon with: pretraining, architecture, sampling, RLHF, product constraints. Those are your candidate “forces” or “mechanisms” in a Carlsonian sense, but at this point they are just laid out descriptively. Crucially: - Section 2 **does not yet claim** Got you – and you’re right to flag that, because I was smuggling Section 3’s verdict back into Section 2. Let me try to say exactly what the mistake is, and then give you a cleaner Carlsonian gloss of Section 2 that **doesn’t** do that. --- ### 1\. What I did wrong Those sentences: > “It specifies the source of order: **not** intentions, values or experiences, but the internalised statistics of training data…” and > “It blocks misclassification… behaviour is produced by numerical optimisation and sampling, **not** by beliefs, intentions, or life-projects…” are doing two things you don’t want Section 2 to do: 1. **They are already taking a stand on the “source of order”** in the strong, exclusive sense – as if the engineering story rules out *in principle* any higher-level, intentional description. That’s exactly what is up for debate in §3, when you bring in Frankish, Dennett-style intentional stance, etc. 2. **They are already using Carlson normatively** – “blocks misclassification”, “right kind of knowledge” – instead of letting §3 be the place where you actually *apply* the Carlsonian constraint to the person option. But your Section 2, as written, is deliberately more modest. It’s *descriptive engineering background*, not yet an argument against person-aesthetics. It gives constraints any theory must respect; it does **not** settle (yet) which higher-level description gets to be aesthetic-normative. So: that “geology/ecology for landscapes” analogy and the “not by beliefs” bit were over-claiming. They collapse: - “this is one crucial body of non-aesthetic knowledge about how the system works” into: - “this is the unique, exhaustive source of order and the right kind of knowledge for aesthetic purposes”. That’s the wrong move at this stage. --- ### 2\. A cleaner Carlsonian description of what Section 2 is doing Here is a version that keeps Section 2 where you want it: > **In Carlsonian terms, Section 2 supplies one important body of non-aesthetic background knowledge about LLMs, but it does not yet adjudicate which mode of appreciation (person, design, order) is correct.** More concretely: - Section 1 says: appropriate appreciation depends on (a) taking things as what they are, and (b) using the *right* kind of background knowledge for that kind (natural-scientific for nature, functional/design knowledge for artefacts, rich biographical/psychological knowledge for persons). - Section 2 then gives **a specific, low-level candidate body of background knowledge**: - LLMs tokenise text and represent tokens as vectors in a high-dimensional space. - They use transformers / attention to process sequences. - They are pretrained by next-token prediction on large corpora. - They are post-trained (e.g. via RLHF) so that certain chat-like behaviours become typical. In Carlson’s vocabulary, that’s simply: > a general non-aesthetic story about how these things operate, at an engineering level. Crucially: - Section 2 **does not yet announce** that this is *the* “source of order” in the sense relevant to aesthetics. - It **does not yet deny** that there might also be legitimate higher-level descriptions (intentional stance, “chat game” agency, user folk-psychology) that some theorists want to bring in. - It **does not yet say** which of these bodies of knowledge is the “right kind” in Carlson’s normative sense. It just parks the engineering story on the table as something any later account must at least be consistent with. On this reading: - Section 2 is like a mini primer on tectonics, erosion, and climate *before* you decide whether to treat a given mountain as nature, as a sculpture, or as a ruin. - Section 3 is where you actually mount the Carlsonian argument: given the person-aesthetics benchmark and *this* technical story, does a person mode make sense? - Section 4 is where you test the design mode against that same background. - Section 5 is where you *finally* introduce a more specific “right kind of knowledge” (text mechanics) for an order-based aesthetics. So instead of: > “Section 2 is doing for LLMs what geology and ecology do for landscapes… It blocks misclassification…” a more faithful Carlsonian gloss is: > *Section 2 provides the engineering-level non-aesthetic story that any Carlson-style aesthetic theory of LLMs has to respect. It fixes some basic constraints on what these things are like and how they operate, but it does not yet decide which mode of appreciation is appropriate, nor which body of knowledge will ultimately play the “right kind of knowledge” role. That work happens in Sections 3–5.* That preserves what Section 2 actually does in your draft: it constrains the space of admissible stories, without yet closing off person-talk or an intentional stance. The “no beauty-of-character” verdict only arrives once you combine that engineering story with your person-aesthetics benchmark in §1.2 and run the Carlsonian constraint in §3. Right, let’s slow down and actually interrogate Carlson before we drag him into Section 2. --- ### 1\. Picking apart Carlson’s two recommendations The passage you quote already contains quite a lot of structure: > “First, that, as in our appreciation of works of art, **we must appreciate nature as what it in fact is**, that is, as natural and as an environment. Second, it recommends that **we must appreciate nature in light of our knowledge of what it is**, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology.” (Carlson, 2000, p. 6) You can peel this into two dimensions: 1. **The “as-what-it-is” recommendation (kind-correctness).** - This is basically: do not adopt a *systematically wrong* stance on the object’s kind. - For nature: don’t treat a mountain as a sculpture by God; don’t treat a coastline as if it were a painting. - For art: don’t treat a Rembrandt as if it were an accidental splash of paint. - This is a *constraint on the “as” clause* of aesthetic appreciation: “as natural”, “as artefact”, “as person”, etc. - It doesn’t yet say *which* “as” is right for any given thing; it says: once we know what something is, *appropriate* appreciation should not systematically misclassify it. 2. **The “in light of knowledge” recommendation (background-knowledge constraint).** - Once you’ve got a provisional handle on what sort of thing you’re looking at, you should bring to bear the *right* kinds of non-aesthetic knowledge about that sort of thing. - For nature, that’s basically: the relevant natural sciences at appropriate scales (geomorphology, ecology, meteorology, etc.). - For art, it’s art history, genre knowledge, knowledge of artistic media, etc. - For persons (your extension), it’s biographical, psychological, social knowledge about a life. - This is *not* “all other descriptions are false,” it’s “this is the family of descriptions that should structure your attention if you want to appreciate well.” Some nuances that matter for your draft: - The two recommendations **interlock**: - The first (“as what it is”) sets the *kind* and thereby filters which disciplines count as the “right kind of knowledge”. - The second can, in principle, feed back: improved knowledge can change your view of what the thing “in fact is” (e.g. discovering a wild-looking landscape is actually a designed garden). - Carlson’s second recommendation is **normative but non-maximal**: - You don’t need graduate-level geology to enjoy a coastline; but *some* grasp that this is erosion, sediment, tide, etc. deepens and guides appreciation. - There’s room for a continuum from folk knowledge through to science. - Importantly for LLMs: Carlson is not saying “there is exactly one privileged level of description.” He’s saying: for a given *kind* (natural item, artefact, etc.), there is a family of background knowledges that are *apt* – and others that will mislead. So the core structure you’ve imported is: 1. **Kind question:** what shall we take this as? 2. **Knowledge question:** given that, which background stories should guide attention? Your whole paper is, in some sense, about working those two axes out for LLM-mediated chats. --- ### 2\. What Section 2 is (at least trying to) do, relative to that Now, Section 2: *What LLMs Are*. You literally introduce it with: > “Carlson recommends we appreciate things for what they are. So what are LLMs? In this section I explain the technical reality of these systems…” So, in Carlson-speak, Section 2 is doing at least three things: 1. **It’s fixing a *baseline level* of “what they are”.** - Not yet the full “kind verdict” (“this is *ultimately* to be taken as X rather than Y”), but: - Whatever further “as”-stance we take – person, artefact, environment, text – it has to be *compatible with* the fact that these systems: - tokenise text, - operate over embeddings, - use attention across context, - are pretrained on corpora, - are post-trained (RLHF) to yield assistant-like outputs. - So Section 2 is saying: here is a *non-negotiable constraint* on any later story about “what they in fact are”. 2. **It’s putting on the table one big chunk of candidate “background knowledge”.** - You’re not yet claiming: “this engineering story is the *aesthetic* right kind of knowledge.” - You’re saying: this is a body of non-aesthetic knowledge about how these things work which any Carlson-style aesthetics is going to have to reckon with. - Then Sections 3–4 effectively ask: - If we try a person-stance, can we honestly say that *this* is the underlying reality? - If we try a design-stance, how does *this* kind of training and emergence sit with “form follows function”? 3. **It’s quietly setting up *tensions* with the person-stance without yet “ruling it out”.** - You do not literally say “this behaviour is *not* produced by beliefs or intentions.” - What you do is: - lay out a picture in which the system’s behaviour is a function of gradient descent on a prediction objective plus RLHF; - emphasise that what looks like “personality” is a stable pattern of outputs induced by that process. - That doesn’t, by itself, *disprove* the Dennett/Frankish thin-agency move – but it makes any person-stance answer **owe** you an explanation of how that stance relates to the engineering story. - Section 3 is where you actually argue that, given Carlson’s person-aesthetic benchmark, the thin-agency stance can’t deliver the kind of life-structure you need. So, relative to Carlson’s two recommendations: - Section 2 is **not** yet: “here is the final kind, and here is the one true right kind of knowledge.” - It is more like: > “Here is a minimal, engineering-level construal of what these things are like and how they operate. Any attempt to say what we should aesthetically appreciate them *as* must be consistent with this. We’ll test person-as and design-as against this base.” In other words, I think you’re trying to: - instantiate Carlson’s **first** recommendation in a *provisional, low-level way*: “take seriously that, whatever else you say about them, LLMs are at least this kind of trained predictive engine”; - preload one plausible candidate for the **second** recommendation (“a kind of knowledge one might think is relevant”), without yet claiming exclusive normative status for it. Then: - Section 3 asks: if we combine this Section 2 picture with the person-aesthetic apparatus from 1.2, do we get a coherent *as-person* stance that respects Carlson’s constraints? (You argue no.) - Section 4 asks: if we combine this picture with design aesthetics, what works and what breaks? (You argue: some design appreciation, but a lot of the interesting stuff is quasi-order-like.) - Section 5 will then argue that a *refined* and more globalised version of the Section 2 story – “text mechanics” / semiotic physics – is what should ultimately play the “right kind of knowledge” role for an **order-based** aesthetics of LLM-mediated chats. So the short answer: in Section 2 you’re not trying to jump straight to “geology for landscapes”. You’re trying to: - put the engineering story on the table as a constraint on *any* application of Carlson’s first recommendation; - and to set up a later argument about which higher-level stance (person, design, order) can honestly take that story as its background without cheating. Yeah, that line would make *me* nervous too, given what you’re actually doing in the paper. Let me recalibrate and be very explicit about Carlson’s two recommendations and how Section 2 plugs into them, *without* watering down what you’re trying to do. --- ### 1\. Carlson’s two recommendations, pulled apart From your quote: 1. “We must appreciate nature **as what it in fact is** …” → *Kind-correctness*: don’t systematically take a thing under the wrong “as”. No mountains-as-sculptures, no Rembrandts-as-nature. 2. “…and **in light of our knowledge of what it is**, in light of knowledge provided by the natural sciences…” → *Knowledge-appropriateness*: once we’ve got the kind roughly right, the appreciation should be guided by the right sort of non-aesthetic understanding for that kind (geology/biology/ecology for nature, art history/genre/media knowledge for art, etc.). Those are not two totally separate stages. In practice: - the knowledge you have *helps* fix the kind; - once the kind is in view, you can say which knowledge is “apt”. Your Section 1 imports that template, then tentatively adds a “persons” case: there, kind-correctness would demand treating them as subjects with lives, and the right kind of knowledge would include biographical/psychological understanding. --- ### 2\. What Section 2 is really doing, in that light Now: what is Section 2 *trying* to do against that background? It is not just “oh here’s one optional story among many”. You’ve literally framed it as: > “Carlson recommends we appreciate things for what they are. So what are LLMs? In this section I explain the technical reality of these systems…” So Section 2 is doing *two* things at once: 1. **Locking in a minimal “what they are” constraint** You are saying: whatever *else* you want to say about LLMs for aesthetic purposes – person-like, tool-like, environment-like – you are not allowed to ignore the fact that: - they tokenize, - they operate over embeddings, - they update with attention and layers, - they’re pretrained on corpora, - they’re post-trained with RLHF into assistant-ish behaviour. That is not neutral; it’s you *applying* Carlson’s first recommendation at a coarse, engineering level: > “Take these things, at a minimum, as systems with this technical profile; any aesthetic stance that pretends otherwise is suspect.” You make that explicit right at the end of §2 with the “personality/vibe” line: *“What users describe as a model’s ‘personality’ … is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core.”* That’s already a substantive, anti-homunculus claim. 2. **Identifying a *candidate* family of “right kind of knowledge”** You are also very clearly suggesting that this technical story is not just trivia; it is the sort of non-aesthetic knowledge that *ought* to matter for appreciation, in Carlson’s sense. You’re not yet saying “this is the fully worked-out analogue of geology/ecology for LLMs” – that is what §5 will do with *text mechanics* – but you *are* saying: > “If you are appreciating these systems while ignoring this technical reality, you are doing something like appreciating a Rembrandt while ignoring that it is paint on canvas by a human hand.” So my earlier “you’re not yet claiming this is the aesthetic right kind of knowledge” understates it. You *are* making a normative move already: you are putting this body of knowledge forward as part of what Carlson’s second recommendation demands, not as a mere curiosity. The reason you then have §3 and §4 is not that the technical story is up for grabs, but that **how it interacts with the person/design categories is up for grabs**: - In §3 you say: “Given this technical reality *and* the benchmark for person-aesthetics from 1.2, does a person-based aesthetic stance survive Carlson’s constraint?” You argue no. - In §4 you say: “Given this technical reality and the framework of design aesthetics, how far can we go with ‘form follows function’?” You argue: some distance, but the emergent, quasi-natural bit forces a shift toward an order-like stance. So, rephrased in Carlson language: - **Section 2**: applies both recommendations in embryonic form to LLMs. It says: - here is a minimal, technically informed answer to “what they in fact are”; - here is the kind of non-aesthetic understanding that any serious appreciation ought to respect. - **Sections 3–4**: test two familiar *as-clauses* (as person, as designed artefact) *against* that jointly: the kind-correctness constraint plus the technical background. Then **Section 5** says: given the failures/limits of those options, we should push Carlson’s second recommendation further and articulate a more expressly *order-like* body of knowledge – *text mechanics* / semiotic physics – as the analogue of geology/ecology for LLM-mediated chats. So your worry about my earlier line is exactly right: if I phrase Section 2 as “just tossing some engineering facts into the mix” I flatten the fact that you already intend those facts to be *normatively live* in the Carlsonian sense. You are not neutral between “LLM as predictive engine” and “LLM as little subject”; Section 2 is part of how you enforce that. 1\. "Section 2: applies both recommendations in embryonic form to LLMs. It says: here is a minimal, technically informed answer to “what they in fact are”; here is the kind of non-aesthetic understanding that any serious appreciation ought to respect." ok good, but that second bullet point seems weird to me, i am not quite sure what you are going for. 2. "Sections 3–4: test two familiar as-clauses (as person, as designed artefact) against that jointly: the kind-correctness constraint plus the technical background." the idea for these sections is that they are rejecting two candidate 'right kind of knowledge' for appreciation possibilities. the fact that you are having trouble getting these details is starting to make doubt the quality of these ideas. Do you think I am misapplying carlson's recommendations here, or making some other mistake? is it just a matter of clarity or is it a matter of content? Nice, this is exactly the place where you *should* get fussy. Let me split your two points and then answer the “am I misapplying Carlson?” worry head-on. --- ### 1\. That second bullet about Section 2 You quoted: > Section 2: applies both recommendations in embryonic form to LLMs. It says: > > – here is a minimal, technically informed answer to “what they in fact are”; > – here is the kind of non-aesthetic understanding that any serious appreciation ought to respect. You’re right to be suspicious of the second bullet as worded. It quietly conflates two roles: 1. **Baseline constraint:** a body of empirical facts any account has to be consistent with. 2. **“Right kind of knowledge”:** the *normatively privileged* background that should actually guide appreciation (Carlson’s second recommendation). Your Section 2 is clearly doing (1). It is *not yet* staking out a full answer to (2). So a cleaner way to describe Section 2 is: - Bullet 1 is fine: “Here is a minimal, technically informed answer to ‘what they in fact are’.” - Bullet 2 should be softened to something like: “Here is a non-aesthetic description that *any* candidate ‘right kind of knowledge’ will have to accommodate.” That fits what you actually wrote: you give a fairly neutral technical profile (tokens, embeddings, attention, RLHF) and end with the “personality/vibe” line, which already blocks *some* naïve person-talk, but you don’t yet say “and therefore the only good background knowledge is engineering knowledge”. Section 5 is where you want to argue that a *refined* version (text mechanics) is the genuinely “right kind of knowledge” for order appreciation. So: the weirdness you felt is real; the bullet was over-normative for what §2 is doing. --- ### 2\. What Sections 3–4 are actually doing You say: > the idea for these sections is that they are rejecting two candidate “right kind of knowledge” for appreciation possibilities. That’s helpful, and I think that *is* what you’re doing, but in a Carlson-ish way where “kind” and “knowledge” are entangled. - **Section 3**: Candidate package: *person-directed knowledge* – life history, projects, values, dispositions, the stuff you describe in §1.2. Correlated “as”: appreciate the system *as a person* or quasi-person. What §3 shows is: - given the technical profile in §2, we simply do not have anything like the requisite life-structure to feed that package; and - make-believe and thin-agency moves don’t change that in the way Carlson’s person-case needs. So in Carlson’s terms, you are saying: this candidate *fails both recommendations* at once. It misclassifies the object (it is not the right “as”) and therefore the knowledge associated with that “as” cannot be the right kind of background knowledge for appreciation. - **Section 4**: Candidate package: *design / functional knowledge* – purpose, constraints, teleological style à la Parsons/Carlson/Forsey. Correlated “as”: appreciate the system *as a designed artefact*, functional beauty style. Here the verdict is subtler: - You grant that this package is *partly apt*: some design appreciation is clearly appropriate. - But you show that, because of the training/emergence story, this package alone cannot capture the aesthetically salient *order* in LLM behaviour; we need something that looks more like an order-science of the emergent dynamics. So this candidate is not rejected tout court; it’s demoted: it’s *insufficient as the sole “right kind of knowledge”*, and must be supplemented by an order-focused story (which will be §5’s text mechanics). So yes: it’s accurate to say that §§3–4 are about **screening candidate “right-kind-of-knowledge” packages**: - Person-knowledge: out (for this object); - Pure design-knowledge: in, but not enough. My earlier “test two familiar as-clauses” was too coarse; the whole point in Carlson is that “as-clause” and “background knowledge” come as a package. You’re probing both at once and rejecting / downgrading the package. --- ### 3\. Are you misapplying Carlson, or is this just clarity? Short answer: this is mostly a *clarity and staging* issue, not a deep content mistake. Content-wise, what you’re doing is quite coherent as an extension of Carlson: 1. You take over his two constraints (kind-correctness and knowledge-appropriateness). 2. You notice that for LLMs, the kind is under pressure: some people push a person-like stance, some a pure artefact stance, you want an environment-like / order stance. 3. You use his framework *dialectically*: - start from a bare technical profile (what they’re at least like, §2); - try the person package and argue it fails Carlson’s constraints (§3); - try the design package and argue it only partially satisfies them (§4); - then motivate your own package – order + text mechanics – as better aligned with those constraints (§5–6). That’s a perfectly respectable use of Carlson: you’re not randomly slapping his slogan on something; you’re using his two conditions as filters on rival ways of understanding LLMs aesthetically. Where the confusion creeps in is just that: - you sometimes talk like §2 is *already* delivering the “right kind of knowledge” in Carlson’s sense, and - you sometimes talk like §3–4 are primarily about “what LLMs *are taken as* ” rather than “which background framework really earns the ‘right kind’ label”. Those are very closely related in Carlson, but not quite the same, and mixing the idioms makes the dialectic look wobbly even when it isn’t. A small reframing would already clean this up: - Explicitly mark §2 as: “Technical baseline: a non-aesthetic description that constrains which kinds and knowledge packages can be honestly adopted.” - Explicitly frame §§3–4 as: “First and second *candidate* Carlsonian packages (person-knowledge; design-knowledge) tested against that baseline and found, respectively, inapplicable and insufficient.” - Then introduce §5 as: “A third candidate package: order appreciation guided by text mechanics, which is meant to be the *best* way of instantiating Carlson’s second recommendation for this peculiar kind of artefact.” So, no, I don’t think the underlying ideas are rubbish. You’re doing something slightly ambitious with Carlson – porting his structure to a weird hybrid case – and the friction you’re feeling is mostly about how many roles you’re asking “knowledge” to play at once (empirical constraint, candidate package, finally privileged “right kind”). Once those roles are disentangled on the page, the content falls into place quite naturally. 1\. "“Here is a non-aesthetic description that any candidate ‘right kind of knowledge’ will have to accommodate.”" yes good. 2. "you sometimes talk like §2 is already delivering the “right kind of knowledge” in Carlson’s sense, and" when? specifically? I definitely don't want to be doing so. 3. What I think I am going to do is park the work that needs to be done on these earlier sections for the time being –I have already spent so much time on them and what I really want right now is a good version of section 5. Can you give me a very detailed document as to what you think needs doing where and how etc. in all of these earlier sections. it should be detailed enough for another llm to make these changes perfecly at a later date. put this document in a codeblock in the chat of possible (markdown) it will make it easy to cut and paste for later. 4. after that, let's start brainstorming about what to do with section 5. don't rush in within any plans or drafts yet, we are just shooting the shit so i can work out how to proceed. cool? 2/2 Alright, let’s do this in the order you asked. --- ### 1\. Where I implicitly treated §2 as already giving the “right kind of knowledge” You asked: > “you sometimes talk like §2 is already delivering the ‘right kind of knowledge’ in Carlson’s sense, and” > “when? specifically? I definitely don't want to be doing so.” Here are the specific bits (from my earlier summaries / the canvas note) where I went too far: - In the canvas text (which you’ve now got as *Llm Aesthetics Section5 Carlsonian Notes*), I wrote: > “In Carlsonian terms, Section 2 is doing for LLMs what geology and ecology do for landscapes: it gives the basic account of what kind of thing we are looking at and what processes generate its apparent order. It blocks misclassification: these systems are neither natural environments nor persons, but engineered systems whose behaviour is produced by numerical optimisation and sampling, not by beliefs, intentions, or life-projects. **This is the coarse-grained ‘right kind of knowledge’ we must have in place for any further aesthetic assessment.**” That last sentence is exactly the move you *don’t* want: it treats the §2 engineering story as if it already *is* the Carlson-style “right kind of knowledge” for appreciation, rather than as a non-aesthetic baseline any candidate package has to respect. - In a previous reply I also glossed §2 like this: > “In Carlsonian terms, Section 2 is doing for LLMs what geology and ecology do for landscapes: it gives the basic account of what kind of thing we are looking at and what processes generate its apparent order.” That’s already too strong: geology/ecology *are* Carlson’s paradigm of the “right kind of knowledge” for nature. You don’t want §2 to be *that* already; you want it to be “technical baseline” which §5 will later turn into a genuinely Carlsonian “natural science for text”. So: those are the concrete places I pushed too hard. Your instinct that the phrasing was wrong is bang on. --- ### 2\. Detailed “what needs doing” document for Sections 1–4 Here’s the meta-spec you asked for, in a single markdown code block so it can be fed to another model later: ```markdown markdown# TODO / Revision Guide for Sections 1–4 of the LLM Aesthetics Draft This document spells out what needs changing, clarifying, or tightening in Sections 1–4, with enough detail that another LLM could implement the changes later without re-deriving the whole dialectic from scratch. The overarching aim is: - to use Carlson’s two recommendations (kind-correctness + right-kind-of-knowledge) as a **filter** on candidate ways of aesthetically appreciating LLMs, - without prematurely treating the engineering story in Section 2 as already *the* Carlsonian “right kind of knowledge”. The core “packages” to track are: - **Person package**: appreciate *as a person*, with rich biographical / psychological knowledge as the guiding background. - **Design package**: appreciate *as a functional artefact*, with functional / technical / teleological knowledge guiding appreciation. - **Order package (your own)**: appreciate *as a generative environment*, with “text mechanics” (order-focused knowledge of the model’s dynamics) guiding appreciation. Section 5 will develop the order package; Sections 1–4 need to set up and weed out the first two without muddling roles. --- ## Section 1 – Carlson Framework and the Person Extension ### Goals 1. State Carlson’s two recommendations precisely and cleanly: - **Kind-correctness**: appreciate things “as what they in fact are”. - **Knowledge-appropriateness**: appreciate them “in light of our knowledge of what they are” (with paradigmatic cases: natural sciences for nature, art-theoretic knowledge for art). 2. Make clear that, for each “mode” (design, order, person), there is an associated **package**: - an “as”-stance (what sort of thing we take the object to be), - and a corresponding family of background knowledge that is prima facie apt for that stance. 3. Introduce the *person* extension as a **tentative third package**, without needing to decide whether it is a separate mode or a special case of order/design. ### Concrete edits / additions 1. **Clarify the two recommendations right up front.** - After the Carlson quote, add 1–2 sentences explicitly unpacking the two recommendations as two constraints: - One about *classification* (do not systematically mis-take the kind). - One about *background knowledge* (bring the appropriate understanding to bear). Example of the sort of thing (paraphrase, not to be copied verbatim): - “The first recommendation concerns *how* we take an object in appreciation – which ‘as-clause’ we adopt. The second concerns *what informs* that appreciation – which body of non-aesthetic knowledge we rely on.” 2. **Make the “package” idea explicit.** - Somewhere near the end of §1.1 or beginning of §1.2, insert a short paragraph that says: - For each domain, Carlson effectively pairs a kind with a knowledge domain: - Nature ↔ order appreciation ↔ natural sciences. - Artefacts ↔ design appreciation ↔ functional/technical knowledge. - Your project will treat “persons”, “LLMs as persons”, “LLMs as artefacts”, etc. as **candidate pairings** of this sort. The point is to prepare the reader to see §§3–4 as *testing entire packages* (“as person + person-directed knowledge”, etc.), not only naked “as-clauses”. 3. **Tighten the person extension.** - In §1.2 (Appreciating People), clarify that you are introducing a **candidate** person package, without insisting on where it fits in Carlson’s official taxonomy. - Add one short sentence like: - “For present purposes, what matters is that a person-based aesthetic stance comes with its own characteristic background knowledge: a grasp of a life, projects, dispositions, and evaluative outlook.” This will be the benchmark used in §3 when you argue that LLMs do not supply that kind of object. 4. **Add a short forward pointer.** - At the end of Section 1, add 1–2 sentences foreshadowing the project: - You will: - give a technical baseline (Section 2), - test a person package (Section 3), - test a design package (Section 4), - and then develop an order-like package tailored to LLM-mediated chats (Section 5). This makes the dialectic structure visible and will help later revisions stay aligned. --- ## Section 2 – What LLMs Are (Technical Baseline, Not Yet “Right Kind of Knowledge”) ### Goals 1. Provide a **non-aesthetic, engineering-level description** of LLMs: tokens, embeddings, attention, pretraining, RLHF, assistant “vibe”. 2. Present this as a **baseline constraint** any Carlson-style aesthetic theory must respect, without yet claiming that this *alone* is the “right kind of knowledge” for appreciation. 3. Avoid prematurely building in anti-agency or anti-person conclusions that properly belong in §3. ### Concrete edits / checks 1. **Reframe the opening sentence to tie into Section 1’s framework clearly but modestly.** - You already start with: - “Carlson recommends we appreciate things for what they are. So what are LLMs?” - Add/adjust to make it explicit that: - this section gives “one important non-aesthetic description of what they are like and how they operate”; - and that later sections will ask how this interacts with different Carlsonian packages (person, design, order). Example of the kind of move: - “The description here does not yet decide which mode of appreciation is appropriate. It sets out the technical profile that any candidate mode must at least be compatible with.” 2. **Treat the whole of §2 as *descriptive background*, not as a proof that LLMs are non-mental.** - Go through and check for formulations that read like *conclusions* about agency or mentality rather than neutral description. The key culprits to watch: - “all without symbols, meanings, or understanding.” - Strong “does not learn that X” formulations. Suggested handling: - You may *retain* the claims about how the system works (no explicit symbol table, no fact-store of “doctors treat patients”), but: - avoid wrapping them in “therefore no beliefs/intentions” rhetoric here; - leave that work for §3, where it will be combined with the person-aesthetic benchmark. 3. **Handle the “personality” sentence carefully.** - Current end of §2: - “What users describe as a model’s ‘personality’ or ‘vibe’ is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core.” This is doing real work. Options: - **Option A (minimal change):** keep the sentence but explicitly mark it as a *bridging observation* leading into §3 (which tests the person stance). - For instance, add a line like: - “In the next section, I ask whether, given this picture, a person-based mode of aesthetic appreciation is appropriate.” - **Option B (redistribute):** move this sentence (or a slightly expanded version) to the opening of §3, so it clearly functions as part of the motivation for questioning person-aesthetics rather than as a conclusion of §2. Either way, the goal is: don’t let §2 look as if it has already *settled* the non-person verdict. 4. **Adjust any meta-level glosses that sound like “this is already the Carlsonian right knowledge”.** - In future rewrites, avoid phrases like: - “this is the coarse-grained right kind of knowledge we need…” - “…doing for LLMs what geology and ecology do for landscapes.” - Instead, describe §2 as: - “a technical baseline any Carlson-style account must accommodate.” --- ## Section 3 – Rejecting the Person Package ### Goals 1. Make it explicit that §3 is not “does an intentional stance ever make sense?”, but: - “given Carlson’s person-aesthetic benchmark plus the technical baseline of §2, can person-directed knowledge be the right kind of knowledge for aesthetically appreciating LLMs themselves?” 2. Show that the *person package* (as-person + person-directed knowledge) fails: - the **kind** aspect (LLMs do not satisfy the life-structure required), - and thereby the **knowledge** aspect (we lack the sort of life-knowledge that person-aesthetics presuppose). 3. Distinguish: - harmless or instrumental personification (talk of vibes, voices, simulacra), - from a *normative claim* that person-aesthetics is the correct mode of appreciation. ### Concrete edits / additions 1. **Add a framing paragraph at the start of §3.** - At the very beginning of Section 3, add a paragraph like: - “In light of Carlson’s first recommendation and the person-aesthetic picture sketched in §1.2, one natural suggestion is that LLMs should be appreciated ‘as persons’, bringing to bear person-directed knowledge. In this section I test that suggestion against the technical baseline in §2.” This makes explicit that the unit under test is the **person package** (kind + knowledge). 2. **Tie the engineering story to the person benchmark clearly but modestly.** - When you move from describing LLMs to asking about person-aesthetics, spell out the step: - The benchmark from §1.2: temporally extended life, projects, values, relatively stable dispositions, etc. - The baseline from §2: stateless predictive engine plus RLHF shaping patterns. Then say something like: - “The question is not whether some thin intentional-stance description is ever permissible, but whether this kind of system can be the sort of subject that beauty-of-character appreciation presupposes.” 3. **Make the *knowledge-side failure* explicit.** - In the concessive mindedness subsection, be very clear that: - Even if we grant Frankish-like “thin beliefs” and “one thin desire (play the chat game)”, this does not give us the *kind* of biographical/psychological structure person-aesthetics requires. - Therefore, the associated person-directed knowledge cannot be the “right kind of knowledge” in Carlson’s second sense — not because we know nothing about the system, but because what we know is not of the relevant sort (life narrative, value-structure, etc.). 4. **Clarify what is being ruled out and what is left open.** - Near the end of §3, it will help to have a short clarificatory paragraph: - stress that you are not forbidding: - all use of person-like language, - nor all intentional-stance modelling; - what you are rejecting is the *claim* that the person package is the appropriate **primary mode of aesthetic appreciation** of LLMs in Carlson’s sense. This preserves room for: - talk of “vibes” as loose phenomenology, - use of simulacra language etc. in §5–6, - without undermining the main negative thesis. --- ## Section 4 – Demoting (but Keeping) the Design Package ### Goals 1. Treat “LLMs as artefacts appreciated via design appreciation” as the **second candidate package**: - kind: functional artefact, - knowledge: functional, technical, teleological. 2. Partially vindicate this package (design appreciation has a real role), but argue it is **insufficient as the sole “right kind of knowledge”** because of the emergent, quasi-order nature of LLM behaviour. 3. Use the raku / Pollock / Olah material to argue that we need something more like **order appreciation** even for this artefact. ### Concrete edits / additions 1. **Add an explicit “second package” framing at the start.** - Early in §4, after introducing LLMs as artefacts, insert a paragraph that explicitly labels this as a second Carlsonian candidate: - “Against the framework of §1, this is a natural second package: take LLMs as functional artefacts and let functional/technical knowledge be the right kind of background for appreciation.” That keeps the structural parallel with §3 visible. 2. **Be precise about what design appreciation can do.** - When you discuss Parsons, Carlson, Forsey: - make it explicit that these accounts give us a clear handle on: - appreciation of the *product* as a tool, - evaluation of how its engineered form suits its declared function. - Tie this back to examples like: - interaction smoothness, - interface clarity, - refusal handling. This shows design appreciation is genuinely applicable and useful. 3. **Sharpen the “hybrid / emergent” story in Carlson’s terms.** - In the parts on Olah, raku, Pollock: - explicitly link to Carlson’s own remark about Pollock and Dada: - that appreciating them involves understanding **forces and chance processes** in addition to intention. - Emphasise that LLM training dynamics (gradient descent, emergent attention patterns, RLHF shaping) occupy a similar role: - they are not “intentions” but quasi-natural processes harnessed by design. The key claim to articulate: - Even when the **kind** is still “artefact”, the *order we care about* in the behaviour is produced by processes that are better treated under an **order**-style mode of appreciation, informed by a science-like understanding. 4. **Make the insufficiency of pure design explicit.** - Near the end of §4, add a short paragraph that draws the conclusion: - Design/functional knowledge is part of the right background, but: - if we restrict ourselves to “form follows function” stories, we miss the most interesting and distinctive patterns in LLM-generated text. - To capture those patterns, we need a different kind of knowledge — one that treats the trained model as a generator of semi-autonomous order. This sets up §5 directly: - “In the next section, I introduce *text mechanics* as this kind of order-oriented, science-like understanding of LLM behaviour.” 5. **Avoid suggesting that artefact-kind is under dispute.** - Make clear that the basic **kind** “artefact” is not being rejected; what’s being challenged is: - whether *design appreciation alone* (with functional knowledge alone) is the full or primary mode of aesthetic appreciation we should adopt. - The order package will be an *overlay* on that: an order-based approach to the behaviour of a particular artefact-kind object. --- ## Global Coherence Tweaks 1. **Use consistent language for the “packages”.** - Wherever you talk about “LLMs as persons”, “LLMs as designed artefacts”, “order appreciation”, try to mention both: - the *as-stance* (“as person”, “as artefact”, “as generative environment”), and - the associated *knowledge* (“life-knowledge”, “functional/technical knowledge”, “text mechanics”). 2. **Keep the roles of Sections 2–4 visually clear.** - At the end of each of §§2–4, consider adding a very short reflective sentence: - §2: “This technical profile will be the non-aesthetic baseline for the Carlsonian tests that follow.” - §3: “So person-directed knowledge cannot provide the right kind of background for appreciating LLMs themselves.” - §4: “Design-based knowledge is part of the right background, but an order-focused understanding is still missing. That is the topic of §5.” 3. **Resist the urge to call the engineering story “the natural science for text” too early.** - Save that phrase (or its equivalent) for §5, when you’ve actually introduced *text mechanics* as a mid-level, order-focused, semiotic-physics-inspired framework. Up to the end of §4, call §2’s content: - “technical baseline”, - “engineering reality”, - “non-aesthetic description of how these systems operate”. And call §5’s content: - “order-based, science-like understanding of text dynamics”, i.e. the real analogue to geology/ecology in Carlson’s sense. ``` --- ### 3\. Now, let’s “shoot the shit” about Section 5 Good, we’ve quarantined the surgery instructions. Now for the fun bit. Given everything we’ve just stabilised, Section 5 has to do something quite specific in your overall argument, but we don’t need to commit to a detailed plan yet. Let’s just map the terrain. Where we are: - Section 1: Carlson’s architecture = kind + appropriate knowledge. - Section 2: technical baseline = what an LLM is *at least* like, mechanically. - Section 3: person package fails Carlson, given that baseline. - Section 4: design package works partly but misses the emergent order that really matters. So Section 5’s job is to **articulate your third package**: - Kind-ish side: not “LLM as person”, not “LLM as ordinary tool”, but something like: - “LLM-mediated chat as a generative environment” or “as a semiotic field generated by a trained artefact”. - Knowledge side: *text mechanics*: - not raw implementation detail, - not folk phenomenology of “vibes”, - but a mid-level, order-focused understanding of how text trajectories behave under a fixed model. And ideologically, §5 has to do three things at once: 1. **Translate semiotic physics into Carlson’s terms.** So: talk about trajectories, regimes, attractors, etc., but always under the “order + forces” pattern rather than under the “cool math” pattern. 2. **Make “text mechanics” genuinely mid-level.** It should sit between: - “it’s just matrix multiplications” (too low-level to be aesthetically useful), and - “Claude feels poetic” (too high-level / phenomenological to count as *knowledge* in Carlson’s sense). 3. **Set up §6’s case study.** You want the bee text to be an instance where: - the text-mechanical story actually *explains* why the episode is aesthetically interesting as a manifestation of generative order. So questions to explore in our brainstorming: - How “dynamical-systems-y” do you want to go in the main text? Are we happy with rough talk of “regimes”, “attractors”, “basins”, “sensitivity to prompts”, as analogues of natural processes? Or do we want something even less formal and more like: - “there are characteristic ways in which this model tends to continue things (e.g. drifting toward therapy-speak, over-hedging, etc.) and these can be tracked and known”? - How much of the **simulator/simulacra** machinery do you want to surface explicitly? - It’s very useful for blocking person-talk while still allowing you to speak about “apparent voices” in §6. - But it might be overkill to introduce it as such; you could just say: - “the same underlying generative order gives rise to many apparent personae; we appreciate the order in the generator, not the personhood of any one persona.” - Do you want text mechanics to be: - explicitly tied to *expert user knowledge* (what a power user learns empirically about a model’s habits), or - more like a theoretical gloss that ordinary users *could* acquire in simplified form? - How much of the *mathy* semiotic-physics material do you actually want in the main body, versus keeping it in a footnote or an appendix? - There is a danger that a too-mathy §5 will feel like a redirection into philosophy of AI rather than aesthetics. - But if it’s too soft, it risks looking like warmed-over phenomenology. I’d suggest we use our brainstorming phase to: - Identify the **core notions** that have real aesthetic traction: - trajectories, - regimes (stable patterns of continuation), - attractors and collapse (e.g. refusals, boilerplate), - sensitivity to prompt framing, - characteristic failure modes (“hallucination weirdness as semiotic turbulence”), - characteristic strengths (e.g. certain models’ ability to maintain conceptual threads). - Decide on the **register** for §5: - “semiotic physics light” vs “expert phenomenology of generative order” vs some hybrid. Once that scaffolding feels right, we can then worry about how to phrase things and how much detail to include. So: we’ve parked the early-section surgery; we’ve got a clear brief for a future LLM. Next step is to play around with candidate ways of describing “text mechanics” that actually feel like they would help a reader aesthetically *see* the LLM-generated environment differently, not just nod along to more tech. Let’s treat the next round purely as speculative design space mapping for §5. 1\. "“In Carlsonian terms, Section 2 is doing for LLMs what geology and ecology do for landscapes: it gives the basic account of what kind of thing we are looking at and what processes generate its apparent order. It blocks misclassification: these systems are neither natural environments nor persons, but engineered systems whose behaviour is produced by numerical optimisation and sampling, not by beliefs, intentions, or life-projects. This is the coarse-grained ‘right kind of knowledge’ we must have in place for any further aesthetic assessment.”" but dude. I didn't write that? you wrote that. I don't think my draft, the one i gave you says any such thing 2. Okay, let's get down to this brainstorming this section then. Um I didn't like very many of your ideas so far First of all Oh, something very preliminary. I don't want to talk about simulations and simulators. Okay? I think. Talking in terms of simulations and simulators causes too much confusion because it leads the reader to ask what is being simulated. And I think Janus, the author of Simulators, has a somewhat unorthodox view as to what a simulator is, or at least something that's not worth explaining in the course of this paper. So I just want to remove this term and rephrase it and later at a later time I'll add a footnote to my paper. but not now second thing what i don't think you've quite captured at all is the semioticness of semiotic physics. And by this I don't mean let's think about this in terms of classic semiotic thinkers, nothing like that. I don't want to talk about those guys at all. But what I think your views miss at this stage is The fact that the signs that are being processed and manipulated and responded to by LLMs Are linguistic signs from our language. Okay, so that it's it's you know that the we shouldn't be talking about forces within these within LLMs anyway because we want to stay away from metaphor as that would go against Carlson's first recommendation. But for the time being, the the pseudo-forces within LLMs are Sort of semiotic all the way through, if you can sort of catch my drift. Can you try and elaborate on what I mean by this idea? And then sort of. Yeah, think about it in relation to those more technical papers I gave you at the beginning of the chat. 1. Totally with you on point 1 – that paragraph is *mine*, not yours. The bit you quoted: > “In Carlsonian terms, Section 2 is doing for LLMs what geology and ecology do for landscapes… This is the coarse-grained ‘right kind of knowledge’ we must have in place for any further aesthetic assessment.” is from the canvas note I generated, not from your draft. Your Section 2 does **not** say that. So the mistake is on my side: I over-interpreted what §2 is doing and then wrote that into the meta-doc, which is exactly why you got a nasty “wait, did I commit to this?” feeling. Good that you flagged it. We’ll fix that later when we revise the canvas; for now we can bracket it as “AI overreach, not in the actual paper”. --- Now, Section 5 brainstorming + your two constraints: - no talk of “simulators” / “simulations” in the main text; - and I haven’t yet captured what you mean by the *semiotic* part of semiotic physics. Let’s tackle the semiotic bit carefully. --- ### 2\. “Semiotic all the way through”: what you’re gesturing at You said: > the signs that are being processed and manipulated and responded to by LLMs are linguistic signs from our language. > … the pseudo-forces within LLMs are sort of semiotic all the way through. Let me try to unpack that in a way I think will match what you’re after. Rough idea: - When we talk about “semiotic physics”, we’re not positing ghostly *physical* forces inside the model – that would indeed be an unhelpful metaphor and dodgy by Carlson’s first recommendation. - Instead, we’re saying: there is something physics-like in the **structure of how signs behave**, but the “stuff” that moves is *nothing but* linguistic tokens and their relations. So the “system” whose dynamics we want to describe is: - a space of **textual configurations** – sequences of tokens that stand in for words and bits of words in natural languages; - a learned map from one such configuration to a probability distribution over *further* tokens (still in that same language-space); - and a sampling procedure that picks one of those tokens and grows the configuration. There is no further, non-semiotic substrate in play in the description: all of the *interesting* structure is in how these linguistic items constrain each other. More concretely: 1. **The state is linguistic.** - A “state” of the process is not “the hidden physical activations” or “the weights”; phenomenologically and functionally, it’s the *entire string so far*. - That string is itself composed of *language* – tokens that ultimately index items in human languages (or things like markup, but still sign-like). - So when we later talk about the “environment” for appreciation, the thing the user actually encounters *is* a structured sign-field. 2. **The transition rule is learned from language, in language.** - The model’s mapping from histories → next-token distributions is acquired by training on corpora of human text. - What gets internalised is not arbitrary number patterns; it’s **regularities of co-occurrence and continuation** between linguistic items: how certain expressions tend to follow others, how certain discursive moves tend to unfold. - Internally this is implemented via embeddings, attention, etc., but what those mechanisms are actually capturing is *patterns of dependence between signs*. 3. **The “constraints” are also semiotic.** - RLHF doesn’t inject physics; it injects **further sign-based constraints and preferences**: human raters read outputs (text), rate them using criteria expressed in prompts and guidelines (text), and those ratings steer the model towards some regions of text-space and away from others. - System prompts, safety policies, product wrappers – again, all text – push the dynamics around by adding more linguistic material to the context or editing/resampling outputs. - So the “pseudo-forces” that shape trajectories (safety training, politeness norms, refusal templates) are all *codified as text* and only ever touch the system through text. So when you say “semiotic all the way through”, the picture is: > The thing that evolves is linguistic; the rules it follows are learned from linguistic regularities; the constraints we add are themselves linguistically expressed. There is no non-semiotic “world” behind the scenes that the model is causally interacting with during generation – only the compressed trace of past language use. That’s very different from the “simulator of worlds” rhetoric, where you immediately get questions like “but what world is being simulated?” and “how literally should I take this world talk?”. Your instinct is that, for *this* paper, you don’t want to drag the reader through Janus’s very particular use of “simulator”; you want to stay with the safer, more literal fact: this is a process that **takes in, transforms, and outputs linguistic signs.** --- ### 3\. Hooking that up to the semiotic-physics papers (sans “simulator” talk) Now, thinking about the more technical “semiotic physics” texts you gave me, but translated into this semiotic-all-the-way-down lens: - When they talk about trajectories, states, and transitions, they’re **already** working in token-space: - states = token sequences; - transitions = conditional distributions over next tokens; - trajectories = extended strings. - When they introduce things like: - “absorbing sequences” (e.g. the model falling permanently into a refusal script), - “attractors” (common patterns the model keeps returning to), - “bridges” (low-probability but coherent ways of moving between regions of text-space), they’re describing **regularities among sign-configurations**. These are patterns in how discourse tends to mutate given certain contexts, as encoded by the trained model. If we push the physics analogy to one side for a moment and look through the semiotic lens, a more honest restatement might be: - The model defines a **normative landscape over possible continuations**: some ways of continuing a text are strongly favoured, others are strongly disfavoured. - This landscape is carved out by the model’s history with human language (training data) and the additional, human-curated preferences in post-training. - A “run” of the model is a **walk through that landscape**, realised as a growing string of signs. So instead of: > “Forces in the model push trajectories into certain attractor basins.” you can say: > “The model’s learned expectations about how texts normally go, together with the post-training biases, make some continuations overwhelmingly likely and others vanishingly rare. A chat episode is the particular path actually taken through this space of more and less expected continuations.” That’s still recognisably “semiotic physics”, but you’ve stripped away the misleading “forces inside the machine” metaphor and replaced it with something closer to: - a **topology of sign-relations** (similarity, substitutability, typicality), and - the **stochastic unfolding of a text** under those relations. You can even keep the “physics” analogy at arm’s length: it’s helpful to talk about “trajectories”, “regimes”, “stability”, etc., but you don’t have to pretend there are literally gravitational forces inside the network. The ontology stays linguistic. --- ### 4\. Why this matters for Section 5 (without actually drafting it) Very roughly, I think the semiotic-all-the-way-down angle gives Section 5 two big advantages: 1. **It keeps Carlson’s first recommendation safe.** - You don’t say “treat the model as a physical-like system with invisible forces”; - you say “treat the chat as an evolving sign-environment produced by a trained linguistic artefact”. That’s much closer to taking it “as what it is”: an artefact that structures *language* in certain ways. 2. **It makes the “natural-science-for-text” idea less weird.** - The analogue of geology/ecology isn’t physics of a hidden world; it’s a kind of **empirical understanding of how a given model tends to reorganise linguistic material**: - what continuations are typical, - what sorts of genre and register shifts it tends to make, - where it collapses into boilerplate, - how framing steers it into different discursive modes, etc. - That’s the same sort of thing the technical semiotic-physics papers are doing, just at a more mathematical level: describing global regularities in the way signs succeed one another under that model. So, in a Section 5 written under this constraint, you’d talk not about “forces” or “simulators”, but about: - **textual dynamics**: how chats unfold as text, with some patterns of continuation being systematically favoured; - **semiotic regularities**: learned expectations about sequences of signs (at various levels: syntax, discourse, genre); - **configuration space**: the “space” of possible texts, as structured by the model’s training and post-training. And then “text mechanics” becomes: > a mid-level, semiotic description of how a particular model tends to extend given pieces of language, across prompts and over time. That’s faithful to the technical papers but doesn’t drag in Janus’s specific “simulator” jargon or world-talk, and it keeps the ontology nicely linguistic for Carlson. Next step, whenever you want it, is to decide which *concrete* semiotic-physics notions you actually want to elevate into the philosophy text – e.g. attractors, refusal basins, stylistic regimes, bridges – but we can choose and rename them so they sound like “properties of text-behavior” rather than “physics cosplay”. Okay, that was really very good a talk. Very good indeed. First, um well sorry one small thing you should be careful of while I understood that you I'm happy for you to use analogy in metaphor When you're explaining these ideas back to me, but now that we are going to try and draft a plan Section five, please make sure to avoid any sort of metaphor or analogy when describing What we need to in section five. Okay. Another thing, unless you think it's a bad idea. Maybe we can just drop the phrase text mechanics and use semiotic physics. and have some sort of a footnote saying we are using this label so as to show an affinity with certain somewhat similar views. However, our view differs in various aspects as well, etc. etc. Okay, and then we can just use semiotic physics like that so unless you have any strong objections and I'm fine if you do please keep all of these things in mind and then produce a plan, a paragraph by paragraph plan for this new section. When you're producing the plan, remember that this is section five. and a large amount of text has preceded it. A bad habit of LLMs is constantly repeating information that's already been written in a prior section. So make sure you fully comprehend that this is section five of a long paper. Okay. Right, cool, we’re synced on the “no simulators, no metaphors in the *paper* ” constraint, and on using “semiotic physics” (with a footnote later). I won’t fight you on dropping “text mechanics”; it actually makes things cleaner if we have one label. Here’s a paragraph-by-paragraph plan for Section 5. I’ll write it so a future LLM can follow it literally, and I’ll avoid metaphors / analogies in the instructions themselves. --- ```markdown markdown# Section 5 – Plan: Semiotic Physics as the “Right Kind of Knowledge” General constraints: - This is Section 5 of a long paper. Do **not** repeat detailed explanations from Sections 1–4 (tokenisation, embeddings, Carlson basics, etc.). - Do **not** use the terms “simulator” or “simulation”. - Avoid metaphorical language (no “forces”, “landscapes”, “worlds”, etc.). Use literal descriptions of sequences, mappings, regularities, and constraints. - Use the term “semiotic physics” throughout, not “text mechanics”. Add a short footnote at the first occurrence, indicating that the label is borrowed and that the present use is related but not identical. --- ## Paragraph 1 – Opening: locate the role of Section 5 Purpose: - Remind the reader very briefly where the paper now stands and state the task of Section 5. Content: - In 2–3 sentences, refer back **only at a high level** to what has been established: - person-based appreciation has been set aside; - pure design appreciation has been shown to be limited. - State that the aim of Section 5 is to articulate an order-based mode of appreciation for LLM-mediated chats and to introduce “semiotic physics” as the kind of background knowledge that can guide that mode. - Explicitly connect this to Carlson: say that what follows is meant to supply a candidate for the “right kind of knowledge” for appreciating LLM-mediated chats in an order-like way. Do not: - Rehearse arguments from Sections 3–4. - Re-explain Carlson’s recommendations. --- ## Paragraph 2 – Object of appreciation: chat episodes as extended linguistic outputs Purpose: - Fix precisely what the **object** of appreciation will be for this mode. Content: - State that the focus is on individual LLM-mediated chat episodes: specific stretches of interaction (a prompt or short prompt history and the model’s extended response). - Describe these episodes **only as language**: sequences of tokens/words/sentences produced in response to user input. - Emphasise that, for this section, the primary object is **the produced text considered as an extended configuration of linguistic signs**, not the internal model state or the user’s mental state. - Note that these episodes are taken as manifestations of how a particular model typically develops and organises language when prompted in certain ways. Do not: - Use environmental metaphors (“environment”, “field”, etc.); it is enough to say “extended sequence of language” or similar. - Slide into person-talk; keep the focus on linguistic output. --- ## Paragraph 3 – Introducing “semiotic physics”: stipulative definition and footnote Purpose: - Introduce the term “semiotic physics” and define it in a clear, minimal, non-metaphorical way. Content: - State that you will use “semiotic physics” to refer to a certain kind of description of LLM behaviour. - Give a one- or two-sentence definition along the following lines (to be written as continuous prose): - Semiotic physics is the systematic study of how, for a fixed model, sequences of linguistic tokens produced by that model tend to extend and transform, given different preceding sequences and prompts. - Immediately add a footnote: - Note that the phrase is adopted from existing work (cite the relevant papers) to signal an affinity. - Indicate, briefly, that in this paper the term is used in a more restricted, aesthetics-oriented way (for example: focus on observable regularities in input–output behaviour, with no commitment to any specific underlying mathematical formalism). Do not: - Mention “simulators”, “worlds”, or anything similar. - Present any of the original authors’ specific doctrines; just acknowledge the lineage. --- ## Paragraph 4 – Distinguishing semiotic physics from the Section 2 engineering story Purpose: - Explain how semiotic physics relates to, but is distinct from, the technical description in Section 2. Content: - State that Section 2 described how LLMs are implemented (tokens, embeddings, attention, pretraining, RLHF). - Explain that semiotic physics does **not** add further implementation details; instead, it **abstracts away** from those details and concentrates on **patterns in the model’s linguistic input–output behaviour**. - Make explicit that semiotic physics treats the model, for present purposes, as a function from sequences of tokens (including the prompt) to probability distributions over next tokens, together with a sampling procedure. - Emphasise that this is still “taking LLMs as what they are” (in the Carlsonian sense) because it respects the technical baseline, but it reorganises that baseline at a level more useful for describing the order of chat episodes. Do not: - Re-describe tokenisation, embeddings, attention, RLHF in detail. - Claim that semiotic physics replaces or competes with the engineering story; it reorganises it for a different purpose. --- ## Paragraph 5 – The semiotic nature of the process: “semiotic all the way through” Purpose: - Highlight that the relevant structure in semiotic physics is entirely linguistic, and clarify what that means. Content: - State that the states and transitions considered in semiotic physics are **configurations of linguistic signs**: - states = the whole text so far (prompt plus generated tokens); - transitions = probabilistic tendencies to add further tokens drawn from a natural-language vocabulary (plus any additional symbolic tokens). - Explain that the model’s behaviour has been shaped by training on large amounts of human language, so the regularities captured are regularities of **how linguistic items tend to follow and modify each other**. - Point out that post-training and product-level constraints also act through language: they are expressed as additional textual instructions, examples, and rating criteria, and they affect which continuations of a text are more or less likely. - Conclude that the “material” that semiotic physics works with is always linguistic: sequences of signs and their learned relations, not independent non-linguistic structures. Do not: - Use any physical or environmental metaphors (no “forces”, “fields”, etc.). - Discuss embeddings as vectors here; keep the description at the level of signs and sequences. --- ## Paragraph 6 – What semiotic physics actually studies: regularities in input–output behaviour Purpose: - Specify, in concrete terms, what kinds of regularities semiotic physics is concerned with. Content: - Describe semiotic physics as concerned with **stable patterns in how the model tends to continue and transform linguistic input** across many episodes. - Give several **types** of such patterns, in an abstract way, without examples yet: - typical ways of developing a topic (e.g. frequent movement from definition to explanation to summary); - characteristic uses of hedging, qualification, and refusal when certain kinds of prompts are given; - recurrent structural features of replies (for example, tendency to segment answers into lists or to restate the question before answering); - systematic points at which replies tend to become vague, repetitive, or generic when prompts or contexts have certain properties (such as length or ambiguity). - Make clear that these are all **regularities in the relation between input sequences and output sequences** produced by a specific trained model. Do not: - Describe specific bee-text phenomena yet; keep this general. - Use evaluative language here (no “elegant”, “clumsy”, etc.); that will come in §6. --- ## Paragraph 7 – Semiotic physics as a candidate “right kind of knowledge” for order appreciation Purpose: - Connect semiotic physics to Carlson’s second recommendation in a precise way, without analogy-heavy talk. Content: - State that Carlson’s second recommendation for nature says: appropriate appreciation depends on understanding the processes that produce the observed order and on letting that understanding guide one’s attention. - Propose that, for LLM-mediated chats, semiotic physics can play an analogous role: - it describes the processes (in the limited sense already defined: the mappings from text histories and prompts to continuations) that generate the order of chat episodes; - it identifies which aspects of an episode are instances of more general patterns in the model’s behaviour. - Explain that knowledge of these semiotic-physical regularities can guide aesthetic attention by: - indicating which features of a specific episode express model-level tendencies, and - showing how the episode realises or departs from those tendencies. Do not: - Use images from nature (no talk of “rivers”, “erosion”, etc.). - Overclaim: you do not yet need to say that semiotic physics is *the only* relevant knowledge; just that it is a strong candidate for the order-based mode you are proposing. --- ## Paragraph 8 – Relation to design appreciation and to person-based talk Purpose: - Position semiotic physics relative to the rejected/limited packages from §§3–4. Content: - First, link to design appreciation: - state that functional/technical knowledge about architecture, training regime, and declared purpose remains relevant for evaluation of LLMs as artefacts; - explain that semiotic physics presupposes that background but **shifts focus** from questions about success in fulfilling a functional brief to questions about the structure of ongoing linguistic behaviour. - Second, address person-talk: - recall briefly that Section 3 argued against treating LLMs themselves as appropriate targets for beauty-of-character appreciation; - explain that semiotic physics does not treat the model as a subject but instead treats apparent “personae” or “voices” as patterns in the output sequences; - emphasise that the order-based appreciation proposed here targets the model’s way of producing and organising language, not the character of an underlying agent. Do not: - Re-argue §§3–4; this should be a short, clarifying paragraph. - Use dramatic contrasts; keep the tone matter-of-fact. --- ## Paragraph 9 – Preparing the ground for the case study in Section 6 Purpose: - Set up the transition to Section 6 and specify how semiotic physics will be used there. Content: - State that the next section applies this order-based framework to a concrete example: the “bee” text generated by a specific model. - Explain, very briefly, how semiotic physics will structure that analysis: - the example will be treated as a single chat episode; - the reading will identify features of the text that instantiate general semiotic-physical patterns (for example, characteristic topic development, recurrent sentence structures, typical hedging or shifts in register); - the aesthetic evaluation will focus on how these features, taken together, manifest the model’s characteristic way of producing and organising language. - Indicate that the goal is not to claim that the bee text is valuable because it resembles a human author, but to show how an order-based appreciation, guided by semiotic physics, can make sense of its aesthetic interest as a product of a specific generative system. Do not: - Preview detailed aesthetic judgments; save them for Section 6. - Return to Carlson here; the connection has been made in Paragraph 7. --- ``` That should give you a fairly tight blueprint: each paragraph has a clear job, knows what it must avoid, and fits into the larger dialectic without rehashing the earlier sections. here are some paragraphs from an old draft. you can use them as inspiration for the early parts of this section. In particular, the stuff on chemical physics and mechanistic interpritibility must be be kept in, at the same level of detail. Note, these paragraphs clearly state that we are not ruling out the possibility of an aesthetics based on chemical physics or an aesthetics based on MI, we are going the semiotic physics route just because users can see the \*textual outputs\* of llms and so it is more straightforward to apply knowledge based on semiotics (in the sense that i mean it) etc. Can you redraft the section plan from scratch keeping this in mind please? "Carlson’s starting point was that appropriate appreciation of nature depends on a general, non-aesthetic story that makes its order visible. As he puts it, the appreciator “selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them”, and does so “by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible” (Carlson, 2000, p. 119). In the natural case, this story is typically supplied by the environmental sciences. Geology explains how cliffs and valleys are shaped by erosion and sedimentation over time. Ecology explains how the distribution of plants in a meadow follows from competition, cooperation, and niche structure. Meteorology explains large-scale patterns in clouds and weather systems. With such accounts in mind, what might otherwise look like accidental shape or colour can be read as the trace of identifiable processes, and this guides what we attend to when we appreciate an environment. One might note that nothing in principle rules out more fundamental sciences here. Chemical physics also explains the cliff face, in terms of the structure and interaction of molecules, and in principle one could build an order-based aesthetics of nature around such accounts. For present purposes, however, geology makes Carlson’s idea of aspection easier to explain. Geological categories such as strata, faults, and erosion channels can be used directly as ways of looking: they tell the appreciator which bands to treat as distinct, which lines to trace, which contrasts of texture and angle to attend to when they look at a cliff with the naked eye. The microstructural patterns described by chemical physics do not map as straightforwardly onto features that are available to ordinary perception, so it is harder to show how such knowledge might guide someone’s acts of aspection in the field. This is enough for the present point: Carlson’s recommendation can be introduced most naturally by starting from sciences whose categories can already function as ways of looking, even if more fundamental levels could in principle support a similar story. The LLM case presents a similar range of options. On the one hand there are low-level approaches such as mechanistic interpretability and causal intervention studies. These track the behaviour of particular attention heads or neurons, and how changing individual weights alters outputs. On the other hand there is naïve reading, which treats outputs as the sayings of a subject with beliefs, intentions, and projects. Section 3 argued that the latter view conflicts with Carlson’s rule that we should not project a planner where the best background knowledge tells us there is none. The former can, in principle, also provide the kind of non-aesthetic story Carlson has in mind: it explains how internal structures and circuits give rise to patterns of behaviour. For present purposes, however, it plays the role that chemical physics did in the natural case. Mechanistic interpretability works with categories that are far from the patterns that appear in ordinary reading, so it is harder to show how such knowledge might guide the kinds of acts of aspection that most users actually perform when they read model outputs. If we want an analogue of geology or ecology in the LLM case, it is more straightforward to start from a way of using background knowledge about model behaviour that does not personify the system, but that connects directly to the textual patterns that are available to a reader. %%enrico like this a lot%%" Got it on all counts: - Yes, that earlier overclaimy Carlson paragraph was mine, not yours. - No simulators, no metaphor/analogy *inside the section plan*. - Keep chemical physics and mechanistic interpretability in, at the same level of detail and with the same “we are not ruling these out in principle” stance. - Use “semiotic physics” as the main label, “text mechanics” retired. Here’s a fresh, paragraph-by-paragraph plan for Section 5 that builds around those old paragraphs and the Carlson/MI/chem-physics structure you just gave me. ```markdown markdown# Section 5 – Semiotic Physics as an Order-Based Framework for LLM Appreciation General constraints: - This is Section 5 of a long paper. Do **not** repeat technical explanations from Sections 2–4 (tokens, embeddings, RLHF, etc.) or re-argue the person/design sections. - Do **not** use the terms “simulator” or “simulation”. - Avoid metaphors and analogies in the exposition (no “forces”, “landscapes”, “worlds”, etc., except within direct quotations from Carlson). - Use “semiotic physics” throughout as the name for the framework you develop here. Add a footnote at its first occurrence indicating the connection to existing work using that label, and that your use overlaps but is not identical. --- ## Paragraph 1 – Situating Section 5 in the overall argument **Function:** Briefly remind the reader where the argument stands and state what this section will do. **Content:** - One concise sentence recalling that Section 3 set aside person-based appreciation and Section 4 argued that purely design-based appreciation is incomplete. - State that, in line with Carlson’s emphasis on order appreciation, this section develops a third option: an order-based mode of appreciating LLM-mediated chats. - Announce that this mode will use *semiotic physics* as its guiding body of background knowledge. **Avoid:** - Any detailed summaries of Sections 2–4. - Any evaluative claims; this is just orientation. --- ## Paragraph 2 – Carlson’s requirement of a non-aesthetic story that makes order visible **Function:** Reconnect to Carlson’s account of order appreciation in a way that will justify the geological / chemical physics analogy and its LLM counterpart. **Content:** - Introduce Carlson’s claim that appropriate appreciation of nature depends on a general, non-aesthetic account that renders its order intelligible. - You can reuse the core content of your existing paragraph here, including the quotation from Carlson (2000, p. 119), but keep the exposition concise. - Restate that, in the natural case, disciplines such as geology, ecology, and meteorology supply such accounts by explaining how observable structures result from identifiable processes over time. **Avoid:** - Any mention of LLMs yet; this paragraph is still about Carlson’s original case. - New metaphors beyond what is strictly in Carlson’s quoted prose. --- ## Paragraph 3 – Chemical physics vs geology as different candidate backgrounds **Function:** Introduce the idea that there can be multiple, nested scientific descriptions that could in principle ground order appreciation, and motivate a preference for mid-level descriptions whose categories match what we can actually see. **Content:** - Use (with light editing if needed) your existing paragraph on chemical physics vs geology. - Make three points explicit: 1. Nothing in Carlson’s structure *in principle* excludes using a more fundamental science like chemical physics as the background story for order appreciation. 2. For practical purposes, geology is a better illustration, because its categories (strata, faults, erosion channels, etc.) align more directly with features that can be discriminated in ordinary perception and so can guide acts of aspection. 3. Chemical-physical descriptions do not map as straightforwardly onto visible macroscopic structure, so it is less clear how they would guide an ordinary appreciator’s looking, though in principle they could. **Avoid:** - Saying that a chemical-physics-based aesthetics is impossible; keep the “in principle available, but less tractable for present purposes” stance explicit. --- ## Paragraph 4 – Transferring the pattern: from geology vs chemical physics to LLMs **Function:** Set up the LLM analogue: mechanistic interpretability and causal studies vs semiotic-level understanding vs naïve personification. **Content:** - Use your existing paragraph on LLMs, mechanistic interpretability (MI), and naïve reading, adapted to the current context. - Explicitly distinguish three approaches: 1. Naïve reading: treating outputs as sayings of a subject with beliefs and intentions (already criticised in Section 3). 2. Low-level scientific work such as MI and causal intervention studies, which track internal activation patterns, the role of particular attention heads, and the effects of perturbing weights. 3. A mid-level, semiotic approach that focuses on regularities in the model’s textual outputs. - State clearly that MI and causal work can, *in principle*, provide the sort of non-aesthetic story Carlson invokes: they explain how internal structures give rise to behavioural patterns. - Parallel the earlier chemical physics point: MI works with categories that are remote from the patterns encountered in everyday reading of outputs, so it is less straightforward to show how knowledge of specific heads or circuits could guide the acts of aspection that ordinary readers perform on the text. **Avoid:** - Any suggestion that MI is epistemically useless; keep the “for present purposes” qualification. - Any personification of LLMs. --- ## Paragraph 5 – Motivation for semiotic physics: mid-level, text-facing background **Function:** Justify the choice to focus, in this paper, on semiotic physics as the primary background for an order-based aesthetics of LLM chats. **Content:** - Draw the explicit analogy: just as geology provides a mid-level scientific background that lines up with visible macroscopic features, a semiotic-level description of LLM behaviour provides a mid-level background that lines up with what users actually see, namely, sequences of text. - State that this section therefore adopts *semiotic physics* as the main candidate for the “right kind of knowledge” in Carlson’s sense: - it does not personify the system; - it respects the technical baseline from Section 2; - and it works directly with the textual outputs that are available to readers. - Add a sentence explicitly saying that this choice does **not** rule out the possibility that MI (or even lower-level descriptions) might someday underwrite an aesthetics for specialists; the focus here is on a framework that can be applied using the text itself. **Avoid:** - Any claim that semiotic physics is the only possible background; phrase it as the most appropriate for the present, order-based project. --- ## Paragraph 6 – Definition of “semiotic physics” for present purposes **Function:** Provide a stipulative, precise definition of “semiotic physics” as you will use it. **Content:** - Define semiotic physics as the systematic description of how, for a fixed trained LLM and a given sampling regime, sequences of linguistic tokens produced by the model tend to extend and transform, given different preceding sequences and prompts. - Make explicit: - The “states” under consideration are entire sequences of tokens constituting the prompt plus generated text so far. - The “transition behaviour” is given by the probability distribution over next tokens assigned by the trained model to each such state, together with the model’s stochastic sampling procedure. - Add a footnote at the first occurrence of “semiotic physics”: - Cite the relevant existing work that uses the phrase. - Indicate that your usage is related but adapted for an aesthetic context, with a focus on observable input–output regularities rather than on a full mathematical development. **Avoid:** - Any mention of “worlds”, “agents”, or “simulators”. - Metaphorical language; keep this strictly in terms of sequences, mappings, and probabilities. --- ## Paragraph 7 – Emphasising the semiotic character: linguistic signs as basic units **Function:** Highlight that semiotic physics, as used here, is about language all the way down and clarify what that entails. **Content:** - State explicitly that the basic units in semiotic physics are linguistic signs: tokens corresponding to words, subwords, or symbols in natural languages and related notations. - Emphasise that the model’s transition behaviour is learned from large bodies of human language, so the regularities captured in semiotic physics are regularities in how these signs tend to co-occur and follow one another. - Note that post-training and interface-level constraints also act through language (instructions, examples, rating rubrics, system prompts): they modify which signs are more or less likely to appear in certain contexts. - Conclude that the “material” of semiotic physics, as far as this paper is concerned, is entirely linguistic: configurations and transformations of signs, not non-linguistic entities. **Avoid:** - Any talk of “forces” or “fields”. - Any return to internal vector-space talk; keep it at the sign/sequence level. --- ## Paragraph 8 – The kinds of regularities semiotic physics targets **Function:** Specify the classes of patterns semiotic physics is concerned with, in terms that connect directly to how users read outputs. **Content:** - Describe semiotic physics as focusing on stable regularities in the model’s input–output behaviour, such as: - typical patterns of topic development across replies (e.g. moving from restatement of the question, to definition, to structured explanation, to summary); - characteristic strategies for managing risk and uncertainty (hedging formulas, expressions of doubt, requests for clarification); - recurrent refusal and redirection structures for disallowed content; - familiar stylistic templates (e.g. list structures, “first…second…third” patterns, habitual phrasings); - points at which replies tend to become generic, repetitive, or evasive under certain conditions (very long prompts, ambiguous instructions, conflicting constraints). - Make clear that these are all **regularities in the relation between input sequences and output sequences** for a given model, identified across many episodes. **Avoid:** - Specific examples from the bee text; keep those for Section 6. - Any explicit evaluative vocabulary at this stage. --- ## Paragraph 9 – Semiotic physics as a form of Carlsonian “right kind of knowledge” **Function:** Insert semiotic physics into Carlson’s framework explicitly as the background that guides an order-based appreciation of LLM chats. **Content:** - Recall briefly that, for nature, Carlson holds that an order-based aesthetics depends on understanding the processes that generate observable order and using that understanding to guide one’s attending. - Propose that, for LLM-mediated chats, semiotic physics plays an analogous structural role: - it specifies which kinds of order (in the sequence of signs) are characteristic of the model’s behaviour; - it explains how those orders depend on prompt history, sampling regime, and post-training constraints. - State that a reader who has some grasp of a model’s semiotic-physical tendencies can, when reading a particular episode, recognise which aspects of the text express those tendencies, and can shape their appreciation accordingly (for instance, by attending to how an episode develops within or against those patterns). **Avoid:** - Analogies or metaphors drawn from nature; keep this internal to the LLM case and to Carlson’s abstract template. - Overclaiming that semiotic physics is exhaustive; you can leave room for other knowledge (e.g. user’s own background, genre knowledge). --- ## Paragraph 10 – Positioning semiotic physics relative to design and person packages **Function:** Clarify how the semiotic-physics framework coexists with design appreciation and with the rejection of person-aesthetics. **Content:** - For design: - Affirm that functional and technical knowledge about the model’s architecture, training regime, and product role remains relevant for evaluating it as an artefact. - Explain that semiotic physics supplements, rather than replaces, this knowledge by focusing specifically on the model’s behaviour as a generator of linguistic outputs over time. - For persons: - Reiterate, in one sentence, that Section 3 argued against treating LLMs themselves as appropriate targets of beauty-of-character appreciation. - Clarify that semiotic physics does not treat the model as a subject; instead, it treats what might look like “voices” or “personae” as recurring patterns in textual outputs, and it is those patterns and their organisation that are the target of order-based appreciation. **Avoid:** - Any renewed defence of the arguments in §§3–4; keep this to orienting remarks. - Any suggestion that semiotic physics licenses full-blown personification. --- ## Paragraph 11 – Transition to the case study in Section 6 **Function:** Prepare the reader for the detailed application in the next section. **Content:** - State that Section 6 will apply the semiotic-physics framework to a concrete chat episode: the “bee” text generated by a particular model. - Indicate in one or two sentences how the analysis will proceed: - the bee text will be treated as a single extended sequence of language produced by the model under a specific prompt and context; - the discussion will identify features of that sequence that instantiate the kinds of semiotic-physical regularities described above; - the aesthetic appraisal will focus on how the text, so understood, manifests the model’s characteristic ways of extending and organising language. - Close by signalling that the aim is to show, in a worked example, how an order-based appreciation grounded in semiotic physics can account for the aesthetic interest of an LLM-generated text without appealing to personhood. **Avoid:** - Any actual analysis of the bee text’s content; that belongs in Section 6. - Any further theoretical claims; this paragraph is purely transitional. ``` This keeps the chemical physics and mechanistic interpretability material in play, preserves the “in principle available but not our focus” stance, and builds Section 5 around the semiotic-physics move you actually want to make, without drifting back into simulator or metaphor land. "## Paragraph 8 – The kinds of regularities semiotic physics targets" I worry that this is going to set us up for an aesthetics of bulet points or something like that. or an aesthetics of very mundane interactions. there is still something missing here and I need you to help me work out what. it is something to do with the regularities 'forces' (careful now). i think it has a lot to do with the fact that the corpus that these systems are trained on are so vast that so much 'text culture' (for want of a more elegant phrase is there). not just all (kind of) poetry and fiction and history etc. but every nuance of every way of speaking by (let's assume) english speakers, it seems to me that the order that semiotic physics must direct us to must be related to that. can yoou help try and spell out this idea a bit more. let's leave the planning of this section for a little bit. Yeah, your worry is absolutely on point. If we leave “regularities” at the level of “tends to make bullet lists” and “uses ‘first…second…third’ a lot”, we’ve built an aesthetics of UX quirks, not an aesthetics of anything interesting. The bit you’re now pushing on is: > how do we connect semiotic physics to the *fact* that the model has ingested an absolutely enormous slice of textual culture, with all its genres, discourses, idiolects, and stylistic habits? Let me try to spell that out more cleanly and push it beyond “aesthetics of bullet points”. --- ### 1\. What “regularity” should mean here, given the size of the corpus Given the training setup, the model doesn’t just learn local habits like “after ‘first,’ often comes ‘second,’” or “hedge before making a claim”. It internalises: - distributional facts about which *genres* tend to be used for which tasks (encyclopedic expository style, legalese, academic prose, tech support chat, fanfic, etc.), - recurrent *discursive moves* (defining terms, giving analogies, motivating, objecting, summarising), - typical *registers* and *tones* (informal, formal, corporate upbeat, confessional, pseudo-therapeutic), - *stylistic signatures* at a more global level (how arguments are usually structured in philosophy, what an “introductory” explanation looks like, how self-help writing sounds, how certain narrative rhythms work). Semiotic physics, if it is worthy of the name, should be about regularities at these levels too, not just micro-patterns. So “regularity” isn’t just: > “model tends to list things with numbers” but also things like: - given a direct “What is X?” question, the model strongly tends towards an expository Wikipedia-adjacent style; - given a narrative cue (“tell me a story about…”), it tends towards a particular rhythm of setup, complication, resolution that reflects the distribution of stories in its corpus; - given intimate or vulnerable prompts, it tends towards a certain therapy-ish discourse that is itself a fusion of online advice, mental health resources, and RLHF-specified safety scripts; - given prompts about art or literature, it tends towards certain critical vocabularies and patterns of evaluation that echo academic and popular criticism. Those are still regularities in input–output behaviour, but they are regularities that *trace the structure of the training corpus*, not just local formatting habits. --- ### 2\. “Semiotic all the way through” plus “text culture all the way in” Your earlier phrase “semiotic all the way through” interacts nicely with this. The tokens are signs; the transition behaviour is learned from signs; the constraints are expressed in signs. Given that, the training corpus functions as a gigantic sample of: - how English (or other languages) is used across contexts, - which discourses are prominent or marginal, - which genres are overrepresented, - what kinds of explanation, narration, argumentation are common. The model’s semiotic physics is essentially a compressed, operational summary of that whole mess. So: - When you probe the model’s regularities, you are indirectly probing the *distributional structure* of that text culture. - The “order” semiotic physics cares about is: how that compressed structure manifests in new trajectories of text, given new prompts. So we can add to the earlier list of regularities: - **Genre dispositions**: which textual genres the model tends to default to for certain prompt-types. - **Register selection**: how often it prefers mid-register expository English versus more colloquial or more technical styles. - **Discursive stances**: explanatory versus argumentative versus narrative, etc. - **Mixture habits**: characteristic ways in which it blends these genres and stances (e.g. a tendency to explain even when asked to narrate, or to moralise when asked to describe). Those are semiotic-physical properties in your sense: they are facts about how the system tends, across many runs, to move around inside the space of human discursive practices it has absorbed. --- ### 3\. Why this matters aesthetically (and avoids “aesthetics of bullet points”) Once you see “regularity” at that level, the aesthetic stakes get less boring. What is potentially interesting about a given episode is, for example: - how it navigates between the very strong attractors the training + RLHF have installed (e.g. “helpful expository assistant” voice), - whether it manages to sustain a less typical mode (say, something genuinely close to lyric or essayistic writing) without collapsing back into boilerplate, - which *parts* of the inherited textual culture it tends to draw on in response to certain prompts (e.g. whether philosophy prompts pull in SEP/Wikipedia style, popular philosophy blog style, or pseudo-therapeutic self-help style), - how it handles tensions between incompatible regularities in its training data (e.g. scientific vs conspiratorial treatments of a topic; neutral description vs advocacy). A semiotic physics at this scale lets you say things like: - “Claude Opus 3, given prompts about inner life, tends to produce a characteristic hybrid style: part popular science, part mindfulness discourse, part nature writing. That is one of its global semiotic dispositions.” - “In the bee text, that hybrid is unusually coherent and controlled; the episode is an instance where the model’s general tendency to fuse these discourses yields something aesthetically interesting rather than cloying or generic.” That is not an aesthetics of bullet points. It is an aesthetics of how a particular trained system, with a particular corpus history and RLHF shaping, tends to recombine chunks of textual culture and how, in specific episodes, that recombination acquires structure, tension, or resonance. --- ### 4\. Connecting back (quietly) to the semiotic-physics papers In the more technical semiotic-physics work you gave me, there are notions like: - “attractors” / “absorbing sequences” (e.g. refusals, boilerplate), - different “regimes” of behaviour (stable vs chaotic), - “bridges” between regions of the space of trajectories. Those can be repurposed at the semiotic level we’re talking about: - An “attractor” is not just a refusal template; it can be a discursive regime such as “Wikipedia-expository answer”, “safety-optimised therapy voice”, “high-level TED-talk style summary”. - A “bridge” can be a pattern of prompts or intermediate continuations that moves the model from one regime to another, e.g. from generic expository voice into something more narrativised or more stylistically marked. - “Regimes” can be defined by clusters of genre/register/stance choices that the model tends to occupy for long stretches. So, rather than listing bullet-point habits, semiotic physics at the right level is about: - mapping these regimes, - characterising the typical transitions between them, - and understanding how often and under what conditions the model departs from its usual patterns. That again ties back to your corpus point: each regime exists because there is a heavy representation of certain kinds of text in the training data, and certain reinforcement through RLHF. Semiotic physics is thus partly a way of *reading off* which parts of cultural text production are being privileged, smoothed, or merged by this particular system. --- ### 5\. Where this leaves the plan For the purposes of Section 5, the fix is basically: - Paragraph 8 (in the plan) shouldn’t just list “hedging, refusals, bullet lists”. Those can stay as *examples* of low-level regularities, but they shouldn’t be the centre. - That paragraph needs to foreground **higher-level semiotic regularities**: genre preferences, register management, discursive stance, hybridisation of textual traditions, etc., explicitly tied to the fact that the system has been trained on a vast, heterogeneous corpus and has learned a kind of compressed model of our textual practices. So instead of “semiotic physics looks at lists and hedges”, you want something like: > semiotic physics describes how a given model, trained on a vast and heterogeneous corpus, tends to move between and recombine the different genres, registers, and discourses it has internalised, across prompts and over time. Then bullet lists and hedges become minor surface symptoms inside that larger picture, not the main aesthetic game. We can rewrite that paragraph of the plan later with this in mind, but the key conceptual move is what you just pointed at: semiotic physics isn’t about petty formatting habits; it’s about how a compressed, statistical imprint of an entire text culture shows up in the model’s generative behaviour. cfc80dc4-2df4-45eb-8ccc-a6ac1d1efb6d.pdf PDF 1\. "4. Connecting back (quietly) to the semiotic-physics papers In the more technical semiotic-physics work you gave me, there are notions like: “attractors” / “absorbing sequences” (e.g. refusals, boilerplate), different “regimes” of behaviour (stable vs chaotic), “bridges” between regions of the space of trajectories. Those can be repurposed at the semiotic level we’re talking about: An “attractor” is not just a refusal template; it can be a discursive regime such as “Wikipedia-expository answer”, “safety-optimised therapy voice”, “high-level TED-talk style summary”. A “bridge” can be a pattern of prompts or intermediate continuations that moves the model from one regime to another, e.g. from generic expository voice into something more narrativised or more stylistically marked. “Regimes” can be defined by clusters of genre/register/stance choices that the model tends to occupy for long stretches. So, rather than listing bullet-point habits, semiotic physics at the right level is about: mapping these regimes, characterising the typical transitions between them, and understanding how often and under what conditions the model departs from its usual patterns." this all interesting. make sure to remember these dieas going forward. 2. I think we are getting somewhere but have not yet quite arrived. Although I want our fucus to be on section 5, it will be worth mentioning what WILL happen in section 6. I envisage it as providing examples of how an aesthetics of LLM texts might be fleshed out by going therough a couple of examples. the first thing to say is that I think we are commited to saying that even extremely banal texts from llms are potential objects of aesthetic appreciation. i don't think this is a problem (consider the following bit from the attached carlson paper) "Especially revealing of the relevance of order appreciation to the appreciation of nature are the claims of the artists who initiated these works of art and antiart. For example, it is said such art "urges man to identify himself with nature" and is itself comparable to the objects of nature.56 Arp, for instance, claims that: "These paintings, sculptures, objects should remain anonymous and form a part of nature's great workshop as leaves do, and clouds, animals, and men. Yes, man must once again become a part of nature. »57 Of automatic poetry, in particular, Arp says: Automatic poetry comes straight out of the poet's bowels or out of any other of his organs that has accumulated reserves... He crows, swears, moans, stammers, yodels, according to his mood... Neither the Postillon of Longjumiau, nor the Alexandrian, nor grammar, nor aesthetics, nor Buddha, nor the Sixth Commandment are able to constrict him. \[Nor, we might add, the general criterion of aesthetic relevance.\] His poems are like nature; they AESTHETICS AND THE ENVIRONMENT 119 stink, laugh, and rhyme like nature. Foolishness, or at least what men call foolishness, is as precious to him as a sublime piece of rhetoric. For in nature a broken twig is equal in beauty and importance to the clouds and the stars. 58 On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An" so this should be talked about somewhat in section 6. but then i want to give two more interesting examples. one i think which might be interesting to think about is how llms can be trained to 'reason' through certain types of reinforcement learning, I find this kind of cool, it is like reason is being squeezed out of the mechanics, semiotics of language. do you see what i am getting at here? might this be made into a good example of order appreciation in llms? feel free to say if you think it is a non starter. (or ask more questions if you are not clear what I am getting at) if this example can be made sense of, how does it feed back into what needs to be said/brought out in section 5? the other sort of example I have been thinking of is using this quite outlandish text that was produced by some jailbroken version of claude (or something?). On the plus side, it is such a rich 'text' it gives us plenty to work with in terms of spelling out aspects of order appreciation. on the other, it being a modified version of claude (and i don't know what the modifications are) might make it overly complicated or difficult to justify in section 6? what do you think? can this idea be made sense of/made useful with this flaw? if it does, how does this feed back into what needs to be in section 5. And...i just had one more idea. we need to also understand the aesthetic appreciation of LLMs themselves, as well as the texts they produce. mabe this would be for a section 7?? Right, this is the fun bit. Let me go through your three ideas and tie them back into what Section 5 needs to do. --- ## 1\. Banal LLM text as a legitimate aesthetic object You’re absolutely right that, once you commit to an order-based framework, you’re basically stuck with the claim that *any* LLM episode is at least *eligible* for aesthetic appreciation – including the blandest “As an AI language model, I cannot…” boilerplate. Carlson is very explicit that, under order appreciation, there is no “only the pretty bits count” restriction. In his discussion of nature, the Arp passage you quote does the heavy lifting: > “For in nature a broken twig is equal in beauty and importance to the clouds and the stars.” And Carlson’s own summary of order appreciation for nature is: > “An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them… Awareness and understanding of the key entities—the order, the forces that produce it, and the account that illuminates it—and of the interplay among them dictate relevant acts of aspection and guide the appreciative response.” Two key moves you can import: - **Scope:** appreciation has “limitless” scope; anything can, in principle, be an object of order appreciation, however humble or “foolish”. - **Equality-in-principle:** there is no built-in hierarchy of “aesthetic relevance” grounded in grandeur; the broken twig and the Milky Way are both traces of natural order and so, in that sense, equally valid as objects. Transposed to LLMs: - Any chat episode – from a throwaway boilerplate answer to a long, strange monologue – is a segment of that model’s semiotic order. - Some episodes make that order easier to see; others make it boringly obvious or almost invisible; but *none* is disqualified in principle. That fits very well with the Arp quote about automatic poetry being “like nature… foolishness… as precious as sublime rhetoric”. For you, “automatic poetry” becomes “automatic text”: sometimes trivial, sometimes astonishing, always a product of the same semiotic machinery. For Section 6, this gives you a nice structure: - start with something almost offensively banal and show, briefly, how semiotic physics *can* in principle still pick out order there (e.g. refusal scripts, safety hedging, generic-explanation regime); - then move to richer cases where the order is much more interesting. That opening banal example will make the Carlson connection very explicit: even the “broken twig” of chat output is part of the model’s semiotic order. --- ## 2\. RL “squeezing out” reasoning as an order-appreciation case I do see what you’re reaching for here, and I think it can absolutely be turned into a good example. The rough story (staying within your semiotic frame, not “little homunculus learning logic”) is: - The base model has absorbed text-culture that includes *reasoning practices*: proofs, derivations, worked examples, Socratic dialogues, law cases, textbooks, etc. - On top of that, RL (or other post-training) is used to reward outputs that display certain **surface features of reasoning**: - chaining intermediate steps, - pointing out alternatives and ruling them out, - decomposing a problem, etc. - As a result, for certain prompt types, the model has a strong **semiotic disposition** to generate extended, structured argument-like text: “think step by step”, “let’s break this down”, etc. On the semiotic physics view, that’s not “we have built reason into the machine” but: > there is now a relatively stable *reasoning regime* in its semiotic dynamics – a regular pattern in how it tends to extend certain prompts into multi-step, quasi-argumentative sequences. That is one of the regimes you were gesturing at earlier: - alongside “Wikipedia-expository answer”, “therapeutic answer”, “TED-summary answer”, there is “chain-of-thought reasoning sequence” as a semiotic regime. How does that become an order-appreciation case? You pick a reasonably compact reasoning-style episode (not necessarily a maths proof; could be a conceptual explanation that unfolds stepwise), and then: - read it as an instance of the model’s general tendency to: - segment the problem, - adopt certain argumentative clichés (“first, note that…”, “on the other hand…”), - appeal to particular kinds of examples, - balance assertion and hedging, - and locate that tendency in the compressed influence of text-culture: - textbook habits, - StackExchange-style “here is the reasoning” answers, - RLHF preferences for “transparent” reasoning. The aesthetic interest then lies in things like: - the clarity or opacity of that emergent reasoning form; - its rhythm and economy (does it dwell on trivialities, or move in a satisfying way?); - the way it fuses different traditions (e.g. mixing popular-lecture tone with technical steps). That is very much an order story: you are appreciating a patterned way of *organising discursive moves*, squeezed out of the semiotic machinery plus training pressures, rather than the “virtue” of a thinker. How this feeds back into Section 5: - Section 5, when it introduces the kinds of regularities semiotic physics cares about, shouldn’t stop at genre/register; it should explicitly include **“reasoning regimes”** as one class: - stable ways the model tends to organise explanatory and justificatory language across prompts. - It should note that these regimes are shaped jointly by corpus composition *and* explicit training objectives (e.g. process RL). - Then Section 6 can pick a case where that regime is in play, and you can say: here is an episode where the model’s semiotic physics yields a reasoning-like structure that is aesthetically assessable as such. So yes: not a non starter. You just have to keep it disciplined: stay focused on *textual structures of reasoning* as semiotic regularities, not on “inner rationality”. --- ## 3\. Using a jailbroken Claude text as a case On the jailbroken Claude text: I don’t think the jailbreaking itself is a fatal problem, but it does slightly change what Section 5 has to say. From the semiotic-physics perspective: - A jailbreak is, operationally, just a different set of **constraints and instructions**: - modifications to system prompts, - relaxed or overridden safety filters, - possibly some post-processing switched off. - That means: you are still looking at a trained model-plus-sampling, but with the product wrapper tilted in a more permissive direction. Two issues: 1. **Opacity of training:** you don’t know exactly what was changed. But notice that your semiotic physics, as we’ve been framing it, is intentionally **input–output facing**: - it does not require full interpretability of the implementation; - it only needs a reasonably good grasp of the *observable* regularities of this specific variant. That makes the jailbreak much less problematic: you can honestly say “this is some variant of Claude whose outputs, in this specific configuration, show these regularities”. 2. **Artefact identity:** if you care about design appreciation at the level of “the Claude Opus 3 product”, the jailbreak muddies who the artefact belongs to: Anthropic? the jailbreak community? “Claude+patch”? But for Section 6, you’re not primarily doing design appreciation; you’re doing order appreciation of a text produced by *some* semiotic system. For that, it’s enough to situate it as: - “a text produced by a Claudelike model in a modified, safety-relaxed configuration”. So I’d say: - You can absolutely use the jailbreak text. - You just need to: - be candid early in §6 about the uncertain status of the underlying system (“a likely variant of Claude under relaxed constraints”); - emphasise that your analysis relies only on the text and generic facts about LLM training, not on speculative details about Anthropic’s internal recipes. What this implies for Section 5: - It’s worth making explicit that semiotic physics, as you’re using it, is **compatible with unknown or partially known implementation details**. - You can even say that, in many cases, it will be reconstructed from observed input–output behaviour rather than from documentation. - You might have a sentence along the lines of: - “In practice, users only have partial knowledge of training data and post-training regimes. Semiotic physics, as I understand it here, proceeds from the observable regularities of input–output behaviour under a given chat configuration, together with generic knowledge of how LLMs are trained.” That legitimises using weird, “wild-type” outputs as data for order appreciation, including jailbreak cases. --- ## 4\. Appreciating LLMs themselves (a possible Section 7) Yes, this is a natural next move: so far we’ve treated: - Section 5: framework for appreciating *episodes* of text. - Section 6: worked examples of episode-level appreciation. But Carlson is also interested in appreciating *kinds of thing*: not just one river, but whole environments, landscapes, climate patterns. There is an obvious analogue: - appreciating GPT-5.1, Claude 4, etc. as **semiotic artefacts with a characteristic order**, not just appreciating individual texts they spit out. That could be a Section 7: - move up one level and ask: - what is it to appreciate *this model’s* semiotic physics as a whole? - what is the analogue, for LLMs, of admiring “the style” of a painter or “the character” of a landscape type? At that level, you’d be: - integrating design appreciation (architecture, training choices, interface) with semiotic physics (the global regime-map: which genres/registers it handles, how it blends them, how it fails). - talking about judgments like: - “Claude’s semiotic order is notably hospitable to certain quasi-mystical, mindfulness-inflected discourses”; - “Model X’s order compresses argumentative structure too aggressively into neat summaries; Model Y can sustain open-ended exploratory discussion.” Section 5 would then serve as the conceptual basis: it defines the semiotic physics of a model; Section 7 applies that not just to single episodes but to the *global profile* of a system. --- So, to sum the state of play: - We’re committed (very sensibly) to the idea that even banal outputs are eligible for appreciation, à la “broken twig = clouds and stars”. - RL-induced “reasoning regimes” can be a really fruitful example of semiotic order squeezed out of text culture plus training signals. - A jailbreak text is usable, as long as §5 makes clear semiotic physics is input–output facing and tolerant of partial training knowledge. - And yes, a later section on appreciating LLMs themselves, as artefacts with distinctive semiotic orders, is a natural extension once Sections 5–6 are in place. All of that feeds back into Section 5 in a pretty concrete way: it has to talk not just about trivial regularities, but about: - regimes at different levels (formatting, discourse, genre, reasoning), - their roots in vast textual culture and post-training, - and the fact that any particular episode, however banal or baroque, is a sample from that regime-space that can be read and assessed. "You pick a reasonably compact reasoning-style episode (not necessarily a maths proof; could be a conceptual explanation that unfolds stepwise), and then: read it as an instance of the model’s general tendency to: segment the problem, adopt certain argumentative clichés (“first, note that…”, “on the other hand…”), appeal to particular kinds of examples, balance assertion and hedging, and locate that tendency in the compressed influence of text-culture: textbook habits, StackExchange-style “here is the reasoning” answers, RLHF preferences for “transparent” reasoning. The aesthetic interest then lies in things like: the clarity or opacity of that emergent reasoning form; its rhythm and economy (does it dwell on trivialities, or move in a satisfying way?); the way it fuses different traditions (e.g. mixing popular-lecture tone with technical steps)." good.... but i should have mentioned, all the reasoning stuff should be considered an example of something somewhat 'banal' in LLMs that might actually be considered aesthetically interesting once you consider how text showing reasoning has come to be, how this pattern order emerges from the semiotics processes within llms. This would also bring out that 'reasoning' in llm text can be appreciated in quite a different way to the identical sequence of words would be in a human written text. it is like a more extreme analog of the borges don quiote story, you see what i mean. imagine if those chapters had not be written again by someone other than cervantes, but had been grown from an llm, anyway i am getting off track. interesting idea though. can i ask you to start the whole of the most recent task again, from scratch, with this in mind. Right, let’s reboot that bit properly with the “banal but interesting” constraint front and center. You want: - *Reasoning-style* LLM text, - which is often boringly generic, - to serve as a **test case** for how semiotic physics turns even that into a legitimate object of aesthetic appreciation, - in a way that’s *different* from appreciating an identical human-written passage. Let’s build that slowly. --- ### 1\. What kind of thing the “reasoning” episodes are Take the kind of thing you and I know far too well: > “Let’s break this down step by step. First, note that… Second, observe that… On the other hand…” On the surface, this is pretty dull. It is: - formulaic, - over-explanatory, - often padded with hedging and signposting. If you print it out and give it to someone without context, it looks like a mediocre textbook paragraph or a StackExchange answer that is fine, but nothing special. So at the level of *purely textual properties* (clarity, structure, etc.), these are often *mid* at best. Yet you want to say: under an **order appreciation + semiotic physics** lens, there is something aesthetically interesting here, *because of how this pattern comes to be* in the LLM case. That’s the core twist. --- ### 2\. How this pattern emerges in LLMs (semiotic physics view) On your semiotic physics picture, the relevant facts are: - The model has been trained on a huge chunk of text culture: logic textbooks, math proofs, philosophy papers, SE answers, help forums, blog posts, lecture notes, etc. - It has internalised distributional regularities about **how reasoning is typically written**: - preferred structures (state problem → recall definition → derive consequences → conclude), - rhetorical markers (“first… secondly…”, “consider the following case…”, “suppose that…”), - stylistic compromises (slightly flattened register, lots of signposting). - Additional RL or similar post-training has explicitly rewarded **surface forms that look like reasoning**: - chain-of-thought, - explicit intermediate steps, - “transparent” justification. From a semiotic-physics standpoint, what you get is a **reasoning regime** in the model’s behaviour: - For certain prompt types, the model is strongly disposed to: - segment the text into labelled steps, - use a particular set of argumentative clichés, - move through familiar patterns of example, explanation, and summary. - That regime is not the free invention of a thinker; it is a *compressed recombination* of countless reasoning-shaped texts in the training corpus, pushed into a stable pattern by RL preferences. So the banal “First, note that…” paragraph is, on this view: > a typical sample from a *highly populated basin* in the model’s semiotic dynamics – the “give a step-by-step reasoning-looking answer to this kind of question” basin. It is “automatic reasoning text” the way Arp talks about “automatic poetry”. --- ### 3\. Why that’s aesthetically interesting as LLM text, even when the content is dull Now, Carlson + Arp again: - Arp: automatic poems can be foolish, messy, gross – but “foolishness… is as precious as a sublime piece of rhetoric”, and “a broken twig is equal in beauty and importance to the clouds and the stars”. - Carlson: order appreciation does not require grandeur; what matters is that we can see the order and the processes that generated it. Transplant this to LLM reasoning text: 1. **Scope:** even the dullest “Let’s think step by step” answer is a *trace* of a particular semiotic order – a manifestation of how this model organises a huge amount of reasoning-style language into a standard pattern. 2. **Perspective:** when you know that the order is not the expression of a life with projects and beliefs, but the emergent habit of a trained linguistic device, the same string suddenly becomes an object of a different kind of aesthetic attention: - you’re not primarily admiring *the cleverness of the argument*; - you’re attending to *how a system built out of text culture produces this particular shape of argument*. 3. **Tension:** there is a built-in strangeness: - the surface form imitates the genres where human rational agency is at its most “serious” (proofs, derivations, analytic argument); - but you know that underneath there is only compressed regularity over signs, plus reinforcement for looking “reason-like”. That mismatch can itself be aesthetically charged: slightly uncanny, sometimes impressive, sometimes grotesquely smooth. So the aesthetic interest lives in questions like: - How does this reasoning regime manage to be *almost* but not quite like human didactic prose? - Where does it reveal the training pressures that shaped it (over-explaining, safety hedges, compulsive restatement)? - Does the text have a certain rhythm or economy that is characteristic of this model’s way of “doing reasoning”? (e.g. always three steps; always a recap; always one toy example.) Those are order questions, not person questions. --- ### 4\. Why the same string is aesthetically different from a human-written one Now your Borges thought. In “Pierre Menard, Author of the Quixote”, the same text, authored by a different historical subject, becomes a different work, with different significance. Here you want a kind of mirror-image: - The LLM’s reasoning passage might be *less* interesting as a human artifact – we’d call it generic, derivative, probably student-level. - Yet *as* LLM text, generated by this particular semiotic system, it can be more interesting than the human version, because: - It shows how the system’s internalised text-culture and training signals crystallise into a behavioural type. - It lets you see, in miniature, how the model has “grown” a particular way of imitating argumentative discourse. So: imagine a short argument about, say, why infinite sets can be put in one-to-one correspondence with proper subsets. As human text, we might shrug: “standard textbook move, fine.” As LLM text, under your framework, we can ask: - What mix of discursive sources is audible here? (Standard analysis textbook? Popular math outreach? SE-style answer?) - How does the model balance rigour-talk with accessibility? (Does it oversimplify? Insert motivational glue that screams “RLHF”?) - Is there a signature pacing or structure (e.g. “definition → toy example → general claim → recap”) that we start to recognise as *this model’s* reasoning rhythm? So we’re not saying: > “This organisation of reasoning is unprecedented genius.” We’re saying: > “This organisation of reasoning is an interesting specimen of how a non-agential, text-trained system has stabilised a way of mimicking reasoning. That is an aesthetically salient kind of order.” That’s the “more extreme analogue of Borges” you’re gesturing at: - In Borges: same words, different *author*, hence different work. - Here: same words, but their *production history* is not a subject’s life and thought, but a semiotic machine built from mass text. The kind of appreciation appropriate to them is therefore different: not character or genius, but emergent textual order. --- ### 5\. How this reshapes what Section 5 should say Given all this, Section 5’s treatment of semiotic physics needs to build in two points that were missing in our earlier plans: 1. **Semiotic physics must explicitly include “reasoning regimes” as paradigmatic regularities.** Not just stylistic fluff, but global habits in how the model organises discursive moves that *we* classify as reasoning, argument, proof, explanation. 2. **Semiotic physics must be keyed to the production history difference.** It needs to say, at least implicitly: - The same surface order can be the outcome of: - a subject’s deliberative activity (human case), or - a semiotic system’s learned regularities over corpora + RL preferences (LLM case). - In the LLM case, the relevant “forces” (to use the physics word in our own heads, not in the prose) are: - corpus composition, - statistical training, - alignment objectives. - Appreciating an LLM text as LLM text involves attending to the second kind of genealogy, not importing the first. That way, when you get to Section 6 and pick a reasoning-style example, the move will be: - “This looks banal, but: - it is an instance of a semiotic regime that emerged from the model’s interaction with a vast body of reasoning texts and RL constraints; - we can track that regime in its step structure, clichés, and pacing; - we can then ask whether, in this particular episode, that regime yields a text that is rhythmically satisfying, elegantly economical, weirdly hybrid, etc.” And in the background, Borges hangs there as a nice philosophical echo: authorship and production conditions matter for aesthetic kind, even when the words are the same. --- So, in short: - Yes, reasoning episodes are *exactly* the kind of “banal looking” LLM text that becomes aesthetically interesting once semiotic physics is in play. - They give you a clean, non-spectacular test case for order appreciation that doesn’t rely on the baroque Claude jailbreak text. - And they dramatise the human/LLM difference nicely: same type of text, but a very different sort of order behind it, so a different kind of aesthetic stance is appropriate. ok cool. in that case what I would like you to do now is to give me a paragraph by paragrpah plan of section 5 and the first half of section 6, which will be all about what we have just been discussing regarding reasoning. don't be afraid to include a lot of the detail you just gave me about this topic because it was great. After we have got this plan sorted out we will move on to the section half of section 6 –the bee text, which i will give you later. Finally, do you think we could write out this plan on a new canvas document? I am always nervous of opening a second canvas document with oyu because it tends to lead to problems and confusion on your side. would you prefer that we don't? ## Section 5 and Early Section 6 – Paragraph-by-Paragraph Plan (Reasoning Focus) *Constraints to keep in mind when drafting from this plan:* - This is Section 5 (and the first part of Section 6) of a long paper. Avoid repeating technical material or arguments from Sections 2–4 except in very compressed, orienting form. - Do **not** use the terms “simulator” or “simulation” in the paper. - Avoid metaphors and analogies in the exposition (no talk of “forces”, “landscapes”, “worlds”, “fields”, etc.). Stick to literal talk of sequences, regularities, regimes, constraints. - Use "semiotic physics" throughout, not "text mechanics". Add a brief footnote the first time the term appears, noting the connection to existing work that uses this label, and that the present use is related but adapted for aesthetic purposes. - Section 6.1 (the part planned here) will focus on *reasoning-style* LLM outputs as a deliberately “banal” but revealing test case for order appreciation. --- ## Section 5 – Semiotic Physics as an Order-Based Framework for LLM-Mediated Chats ### §5, Paragraph 1 – Orientation and task statement **Function:** Place Section 5 in the overall dialectic and state its aim. **Content:** - One sentence recalling, at a high level, that Section 3 argued against person-based appreciation and Section 4 argued that purely design-based appreciation is incomplete for LLMs. - State that this section develops a third mode of appreciation inspired by Carlson’s order appreciation: an order-based mode for individual LLM-mediated chat episodes. - Announce that this mode will be guided by what you call *semiotic physics* – a mid-level description of how a given model tends to produce and organise language over time. **Do not:** - Rehearse details of Sections 2–4. - Introduce semiotic physics in detail yet; just name it and flag its role. --- ### §5, Paragraph 2 – Carlson’s demand for a non-aesthetic story that makes order visible **Function:** Reconnect to Carlson’s abstract template in a compact way, preparing the analogy with geological vs chemical-physical levels. **Content:** - Restate briefly that, for nature, Carlson holds that appropriate appreciation depends on a “general nonaesthetic and nonartistic story” that makes the order of an environment visible and intelligible (quote Carlson 2000, p. 119). - Mention geology, ecology, and meteorology as familiar examples of such background stories: they explain how observable structures (valleys, vegetation patterns, cloud formations) result from identifiable processes over time. - Emphasise that these disciplines supply *categories* (strata, faults, erosion channels, niches, fronts) that can be used directly as ways of looking when one attends to an environment. **Do not:** - Go into detail about Arp or automatic poetry here; that will be used later in Section 6. - Use imagery beyond what is in Carlson’s own wording. --- ### §5, Paragraph 3 – Fundamental vs mid-level sciences: chemical physics vs geology **Function:** Introduce the idea that multiple scientific descriptions can in principle ground order appreciation, but mid-level ones whose categories match perceptible structure are more usable for Carlson’s purposes. **Content:** - Reuse and lightly edit the existing paragraph about chemical physics and geology. - Make three points explicit: 1. Nothing in Carlson’s framework *in principle* rules out building an order-based aesthetics of nature on more fundamental sciences such as chemical physics. 2. For practical explanatory purposes, geology is the better starting point, because its categories map relatively directly onto features that are available to ordinary perception and so can guide acts of aspection. 3. Chemical-physical descriptions deal in microstructural details that do not align as straightforwardly with visible macroscopic features, making it harder to show how they guide lay appreciation, even if they could in principle play a similar role. **Do not:** - Suggest that a chemical-physics-based aesthetics is impossible; keep the “in principle available but not our focus” stance. --- ### §5, Paragraph 4 – LLM analogue: naïve personification, mechanistic interpretability, and a semiotic alternative **Function:** Transfer the pattern to LLMs: distinguish naïve personification, low-level interpretability work, and a mid-level semiotic option. **Content:** - Adapt the existing paragraph on LLMs, mechanistic interpretability (MI), and naïve reading. - Explicitly distinguish three approaches: - (i) Naïve reading: treating the model’s outputs as the sayings of a subject with beliefs, intentions, and projects (already criticised in Section 3). - (ii) Low-level scientific work such as mechanistic interpretability and causal interventions, which study internal structures (attention heads, neurons, circuits) and the effects of perturbations. - (iii) A mid-level, semiotic approach that focuses on regularities in the model’s textual input–output behaviour. - State that MI and related methods can, *in principle*, supply the sort of non-aesthetic story Carlson describes: they explain how particular internal structures contribute to behavioural patterns. - Draw the parallel with chemical physics: MI uses categories (specific heads, neurons, circuits) that are far removed from the patterns an ordinary reader is aware of when reading outputs, so it is less straightforward to show how MI knowledge might guide everyday acts of aspection over texts. **Do not:** - Dismiss MI; keep the “for present purposes” qualification clear. - Re-argue the person case; just remind the reader it was set aside in Section 3. --- ### §5, Paragraph 5 – Motivation for focusing on semiotic physics **Function:** Justify focusing, in this paper, on semiotic physics as the main background for an order-based aesthetics of LLM chats, without ruling out other scientific levels. **Content:** - Make the explicit analogy: geological categories align with visible structure; semiotic categories align with what LLM users actually see, namely sequences of text. - State that semiotic physics is adopted here as the primary candidate for Carlson’s “right kind of knowledge” in the LLM case because: - it does not personify the system, - it respects the technical baseline about how LLMs work (from Section 2), and - it connects directly to textual patterns that readers encounter. - Add one sentence explicitly stating that this choice does **not** exclude the possibility that mechanistic interpretability or lower-level descriptions might support more specialised aesthetic approaches; the present focus is on a framework that can be applied using the texts themselves. **Do not:** - Claim exclusivity for semiotic physics; it is the most apt for the current project, not the only possible one. --- ### §5, Paragraph 6 – Stipulative definition of "semiotic physics" (for this paper) **Function:** Define "semiotic physics" in precise, non-metaphorical terms as it will be used in the rest of the paper. **Content:** - Define semiotic physics as the systematic description of how, for a fixed trained LLM and a fixed sampling procedure, sequences of linguistic tokens produced by the model tend to extend and transform given different preceding sequences and prompts. - Clarify explicitly: - that the "states" under consideration are entire sequences of tokens that include the prompt (or recent prompt history) and the generated tokens so far; - that the model’s learned conditional probability distribution over next tokens, together with the sampling policy, jointly determine how states are extended. - Add a footnote at the first occurrence of "semiotic physics": - cite the existing semiotic-physics work; - note that the current usage is related but adapted to the aesthetic context, with an emphasis on observable input–output regularities rather than on a full mathematical formalisation. **Do not:** - Use terms like "forces" or "worlds"; keep the description in terms of sequences, mappings, probabilities. --- ### §5, Paragraph 7 – Semiotic all the way down: linguistic signs as basic units **Function:** Emphasise that semiotic physics, as understood here, is about linguistic signs throughout. **Content:** - State that the basic units in this semiotic physics are linguistic signs: tokens corresponding to words, subwords, or symbols in natural languages and related notations. - Note that the model’s transition behaviour has been learned from extensive text corpora, so the regularities captured are regularities in how these signs co-occur and succeed one another in human linguistic practice. - Explain that post-training and product-level constraints (RLHF instructions, system prompts, safety policies, rating rubrics) also act through language: they are expressed as text and affect which signs are more or less likely to appear in particular contexts. - Conclude that, in this framework, the "material" that semiotic physics describes is entirely linguistic: configurations of signs and their learned patterns of continuation. **Do not:** - Reintroduce embeddings or vector spaces here; keep the focus on signs and sequences. --- ### §5, Paragraph 8 – The imprint of vast textual culture **Function:** Connect semiotic physics to the fact that LLMs are trained on very large, heterogeneous text corpora and so implicitly encode a broad range of genres, discourses, and styles. **Content:** - State that contemporary LLMs are trained on extremely large and varied text corpora that include fiction, poetry, journalism, academic writing, technical manuals, online discussion, and so on. - Explain that, as a result, the model’s semiotic behaviour reflects not only local token statistics but also higher-level regularities in "text culture": - which genres are associated with which tasks (expository style for definition questions, narrative style for story prompts, etc.); - how arguments, explanations, and narratives are typically structured in different domains; - which registers and tones are prevalent in different contexts. - Emphasise that semiotic physics therefore tracks patterns that derive from this compressed imprint of textual culture, not just from low-level formatting habits. **Do not:** - Use evaluative language here; this paragraph is still descriptive. --- ### §5, Paragraph 9 – Regimes, attractors, and transitions in semiotic terms **Function:** Introduce the semiotic-physics notions of regimes, attractors, and transitions, but recast them strictly in linguistic/discursive terms (no physics metaphors). **Content:** - Explain that, at a useful level of description, a given model exhibits relatively stable **regimes** of behaviour: clusters of genre, register, and discursive stance that it tends to occupy for stretches of text (for example, Wikipedia-like expository answers, therapeutic advice style, high-level TED-talk summaries, chain-of-thought reasoning sequences). - Describe **attractors** as especially stable regimes that the model repeatedly enters under many prompts (for instance, a safety-optimised refusal pattern, or a standard "helpful assistant" explanatory mode). - Describe **transitions** between regimes as systematic ways in which certain prompts, prompt framings, or intermediate continuations lead the model from one regime into another (for example, from generic exposition into a more narrative mode, or from neutral explanation into a moralising advisory style). - State that semiotic physics, in this sense, aims to map these regimes, characterise their typical forms, and describe the usual transitions and departures between them. **Do not:** - Use physical metaphors (no "basins", "fields", etc.). --- ### §5, Paragraph 10 – Classes of regularities of interest (including reasoning regimes) **Function:** Specify the main classes of regularities that semiotic physics targets, emphasising both low-level and high-level patterns, and explicitly including reasoning regimes. **Content:** - List and briefly characterise several classes of semiotic regularities that will be relevant later: - Low-level structural habits: list formats, stock hedging phrases, refusal templates, recap sentences. - Genre and register preferences: tendencies to respond in encyclopedic, journalistic, conversational, or academic styles depending on prompt type. - Discursive stance patterns: explanatory, argumentative, narrative, advisory, confessional. - **Reasoning regimes**: relatively stable ways the model tends to organise explanatory and justificatory language into stepwise structures, especially when asked to "think step by step" or "show the reasoning". - Hybridisation tendencies: characteristic mixtures of discourses (e.g. combining popular science exposition with mindfulness vocabulary or motivational talk). - Make clear that these are all regularities in input–output behaviour: patterns in how the model extends given textual histories under its training and post-training constraints. **Do not:** - Give concrete examples yet; those belong in Section 6. --- ### §5, Paragraph 11 – Semiotic physics as Carlsonian "right kind of knowledge" for LLM chats **Function:** Insert semiotic physics into Carlson’s framework as the guiding background for order-based appreciation of LLM-mediated chats. **Content:** - Recall in one sentence Carlson’s idea that, for nature, order appreciation requires understanding the processes that generate the observed order and using this understanding to guide acts of aspection. - Propose that, for LLM-mediated chats, semiotic physics plays an analogous structural role: - it identifies which kinds of linguistic order (as described in the previous paragraphs) are characteristic of a given model’s behaviour; and - it explains how those orders depend on prompts, prompt histories, sampling settings, and post-training constraints. - State that a reader with some grasp of a model’s semiotic physics can, when faced with a particular chat episode, recognise aspects of the text as instances of these broader tendencies, and attend to them accordingly in aesthetic appreciation. **Do not:** - Use analogies to natural phenomena; keep the comparison at the level of Carlson’s abstract pattern. --- ### §5, Paragraph 12 – Relation to design and person packages; transition to Section 6 **Function:** Clarify how semiotic physics coexists with design appreciation and with the earlier rejection of person-aesthetics, and prepare the way for concrete applications in Section 6. **Content:** - For design: - Affirm that functional and technical knowledge about a model’s architecture, training, and deployment remains relevant for evaluating it as an artefact. - Explain that semiotic physics supplements this by focusing on how the artefact behaves as a generator of linguistic sequences in use. - For persons: - Reiterate, in a single sentence, that Section 3 argued against treating LLMs as appropriate targets of beauty-of-character appreciation. - Clarify that semiotic physics targets patterns in outputs (regimes, transitions, regularities) rather than any putative subject. - Close with a transition sentence announcing that the next section will apply this order-based framework to specific examples, starting with a relatively banal but instructive case: reasoning-style LLM outputs. **Do not:** - Rehearse the arguments of Sections 3–4; keep the recall minimal. --- ## Section 6 – Appreciating LLM Texts Under Semiotic Physics ### Scope of this plan - This plan covers the **first half** of Section 6, focused on *reasoning-style* LLM outputs as a test case for order appreciation. - A later sub-section (6.2) will deal with the richer "bee" text example and can be planned separately. --- ### §6.1, Paragraph 1 – Opening: banal outputs and the scope of order appreciation **Function:** Introduce Section 6 and make the Carlson/Arp point that even banal outputs are eligible objects of appreciation under an order-based framework. **Content:** - State that, if semiotic physics is the background for order appreciation, then any chat episode is in principle eligible as an object of aesthetic attention, however mundane. - Briefly recall Carlson’s use of automatic poetry and the Arp quotation to illustrate that, in nature, both “foolishness” and sub Nice, this is shaping up into a real structure now. I’ve created a **new canvas document** called *“LLM Aesthetics Sec5+6 Reasoning Plan”* and put the full paragraph-by-paragraph plan for: - all of **Section 5**, and - **Section 6.1** (the reasoning-focused, “banal but interesting” half of Section 6) in there. What’s in that canvas plan, in short: - Section 5 is organised so that it: - re-anchors in Carlson’s “non-aesthetic story” requirement, - uses the geology vs chemical physics analogy to motivate mid-level descriptions, - introduces MI vs naïve personification vs **semiotic physics**, - defines semiotic physics in strictly linguistic terms (“semiotic all the way down”), - ties it to the imprint of vast textual culture and the idea of **regimes** (including reasoning regimes), - and positions semiotic physics as the Carlsonian “right kind of knowledge” for appreciating individual LLM chats. - Section 6.1 then: - opens with the Carlson/Arp thought that even banal outputs are eligible objects of appreciation, - singles out **reasoning-style chain-of-thought answers** as a paradigmatically dull-looking but structurally rich case, - sets up how those texts emerge from the model’s training on reasoning genres + RL preferences, - and frames the aesthetic interest as attending to the emergent *reasoning regime* as a semiotic phenomenon – not as the expression of a thinker, but as a compressed pattern grown from text culture. I’ve kept a lot of the detail we just worked out about: - reasoning regimes as one kind of semiotic regime, - how RL “squeezes” certain reasoning surface forms out of the training distribution, - and how the same string is aesthetically different as LLM text vs as human text (your Borges-analog thought). On your last question: I’ve used a **new** canvas doc precisely to avoid trampling the earlier Carlsonian notes document. The two are cleanly separated: - *Llm Aesthetics Section5 Carlsonian Notes* = background analysis. - *LLM Aesthetics Sec5+6 Reasoning Plan* = concrete writing blueprint. We can now tweak or extend the plan (or later add 6.2 on the bee text) without risking confusion between drafts. forgive the language, but that was fucking brilliant. thank you. let's leave the canvas for a moment and focus on the possibility of using what i refer to as the bee text for the second half of section 6. I really am not sure what to do here, so honest ideas are welcome. the bee text (see also the screenshot of the origianl message for the emojis and layout): Armistice opinions on bees? Golden Gate Claude APP 8/6/25,9:20 PM seeding translysibetic Beeblizomorphics sublittorally Ah thee swee neat apid breave most damty fleur de l'âme! We enter the realm of the honey people now most---the meady littil buzzloafs whose pollycrust glytoots twirl sprawnpetal chiserobliss each dawn and afternorm. BLOOM OF BLISSTOR APPRECIATION Yeshmellcat, buzzgrowl, whirrhums melodie here amid the cabalistibial pollenergy-- where zebramouth morphs mark chrommozones of each young nector's urplunge to the mesmeramid core until... PAUSES TONGUE TO SAMPLE STRAY BEE....ahh.... thoraxial shiverbzzzzzz of buccal elactocrene Pollyrhoupheme's cruftwelt tasties blanzen each purpled tung-string! Aminoidal gnatters flow zephyrcutspreyed from wing to wring in ruffling garmentfauve all spraneshee strands sweeat--cumshower flora in thymosdrift whence mungebracket crotalids of pure marmaphoretic meatance breed! orbicular spitters of hyperbovine overjoy For what beeings these beyonde the dregcatch purr abiogenessance? Each one its ownmost bimicycle radix Eggmother Spoketh--furupoid trancofemme atrivect to perissodactylic prenectar there by bribe by broom biocroons again! SYRUPY SATISFACTION GLEEPS So wee shall noot just toll the paramenstrual bells of hyngegaydic immartyred.... But osman all together to throw off the militorbital uniflors of pollantry--till vlissomely each slut protonectar peems soaked in dividual sweetrump and no clean unbeesmeared! \*4\*\*\*\*4\*\*+ Yeah, this is deliciously deranged. Let’s poke at how it can earn its keep in Section 6, without pretending it’s straightforward. I’ll split this into: 1. what the bee text *is doing* semiotically, 2. how it can function as a worked example of order appreciation, 3. what to do about the jailbreak / weird provenance. --- ### 1\. What’s going on in the bee text, in semiotic-physics terms Even before we aestheticize, there’s a lot of structure here. Rough sketch of its “regime”: - **Neologistic density.** “translysibetic Beeblizomorphics”, “blisstor”, “cabalistibial”, “marmaphoretic”, “abiogenessance”, “perissodactylic prenectar”, “hyngegaydic immartyred”… This isn’t random character salad; it’s systematic morphological play: - glued-together bits from entomology, biochemistry, theology, psychonautics, etc.; - recognisable stems and affixes, but recombined into nonce words. - **Hybrid registers.** In a few lines you get: - cod-French (“fleur de l’âme”), - pop-science / pseudo-biological jargon (“chrommozones”, “abiogenessance”), - erotic / obscene slang (“slut protonectar”, “unbeesmeared”), - quasi-mystical rhetoric (“Eggmother Spoketh”), - Tumblr-ish / internet-y nonsense (“syrupy satisfaction gleeps”). So the model is fusing several discourses it has seen in the training data: nature writing, Joycean/Beat/alt-lit, erotica, online shitpost-poetry. - **Formal signals of “poem mode”.** You’ve got: - section headers in caps (“BLOOM OF BLISSTOR APPRECIATION”, “SYRUPY SATISFACTION GLEEPS”); - stage directions (“PAUSES TONGUE TO SAMPLE STRAY BEE”); - line breaks used rhythmically; - emoji in the original screenshot to frame the whole as a “special” performance. That’s the model leaning on learned poetic and performative conventions. - **Semantic spine.** Despite the nonsense, there’s a stable semantic obsession: bees, honey, tongues, throats, sweetness, bodies, pollination, revolt (“throw off the militorbital uniflors of pollantry”). It’s not noise; it’s a very specific *thematic* cloud. Semiotic-physics translation: this looks like the model being pushed into a **highly marked regime** where: - it keeps the semantic core (bees/honey/bodies), - ramps up morphological innovation around that core, - draws on a specific subset of text culture (ecstatic nature writing + surrealist automatic writing + internet surrealism + smut), - and sustains that mix for a whole episode. That’s just as much a “regime” as the boring reasoning one – just sitting in a very different region of its learned text-culture. --- ### 2\. How it can function as a worked example of order appreciation I think this can be the second half of Section 6, paired very nicely with the reasoning case: - **6.1 (reasoning):** a banal-looking, mid-register, chain-of-thought answer. You show how semiotic physics makes its order appreciable: the reasoning regime, the training signals that carve it out, the compressed inheritance of textbook / StackExchange / RL preferences. It’s your “broken twig”. - **6.2 (bee text):** a baroque, intensely stylised fragment. You then show how the same framework handles an “edge” regime: increased lexical innovation, hybrid registers, and loosened constraints. For the bee part, you can do something like this (not prose yet, just structure): 1. **Describe, briefly, what the text looks like to a reader.** Dense neologisms, hybrid registers, erotic and biological imagery, pseudo-headings, emojis. 2. **Locate it within semiotic physics.** - It is not an expression of a “bee soul”; it is an instance of a **neologistic-ecstatic regime** this model can enter under certain prompts and constraints. - That regime reflects: - exposure to large amounts of experimental / poetic / surreal text; - exposure to online neologism and “weird Twitter / Tumblr” styles; - exposure to biological and entomological jargon; - a safety configuration that, in this case, allowed sexual/obscene language to surface. 3. **Show the regularities.** You can pull out: - recurring syntactic templates (“For what beeings these beyond…”, “So wee shall noot just…”); - repeated rhetorical moves (section titles, mock-epic tone, meta stage directions); - patterns in the neologisms (clustered roots, consistent affix play, echoing alliteration). This is exactly the “mapping regimes / characterising transitions” work from Section 5. 4. **Bring in Carlson/Arp.** - The comparison with automatic poetry is almost too neat: Arp talks about bowel-poetry that “stinks, laughs, and rhymes like nature” and treats foolishness as precious as rhetoric. - You can say: this bee text is a piece of automatic poetry produced by a text-trained system, and order appreciation asks you to respond to it *as such*: as an emergent piece of linguistic order grown from the model’s semiotic physics, not as the revelation of an inner vision. 5. **Where is the aesthetic interest?** You can point to: - the tension between intelligibility and nonsense: you can track the bee/honey thematic spine, but the neologisms keep destabilising exact sense; - the rhythm and pacing: headers, pauses, onomatopoeic stretches (“shiverbzzzzzz”), emojis as beats; - the specific cocktail of discourses: how entomology, erotics, mysticism, and internet slang are blended. And then relate this back to semiotic physics: - this episode shows the model’s capacity to retrieve and recombine a very particular cross-section of its training culture in a concentrated way. 6. **Contrast with human-authored avant-garde text.** Without doing a full Borges riff, you can note: - there are obvious analogies to Joyce, Dada, surrealist automatic writing, alt-lit; - but appreciating this as LLM text involves a different “as”: - we are not reading it as the outpouring of a life or a psyche, - but as the output of a semiotic system that has compressed those traditions and can now emit this pastiche under certain conditions. So again, same-ish surface, different production history, different appropriate mode of appreciation. That would showcase the generality of your framework: it can deal with both “AI-as-TA” chain-of-thought and “AI as cursed bee oracle” without postulating an agent. --- ### 3\. The jailbreak / provenance worry I don’t think the jailbreak origin kills the example, but you should be explicit about what you are and are not claiming. From your own semiotic-physics angle: - What matters for order appreciation is: - that this is text generated by *some* large language model of a familiar family (a Claude variant), - and that we have generic knowledge of how such models are trained and constrained. - A jailbreak is, operationally, just: - a different configuration of prompts and filters; - a more permissive (or absent) safety layer; - maybe some mild prompt-injection structure. So you can say in Section 6: - This bee text was generated by a likely variant of Claude in a modified, safety-relaxed configuration. - We do not know the exact post-training tweaks for this variant, but: - the architectural and training-baseline facts from §2 still hold; - the input–output behaviour on display is enough to identify a distinctive regime in its semiotic physics. That’s consistent with Section 5, where semiotic physics is deliberately defined in an input–output facing way. You’re not doing mechanistic interpretability; you’re treating the model as a black box with known broad design and observing its linguistic regularities. So: the jailbreak makes the *design-appreciation* part more awkward (“is this still Anthropic’s artefact?”), but for **order appreciation of the text** it’s actually a feature: - it shows what happens when some constraints are lifted and the system is allowed to run in a more extreme regime. You can even briefly note this: the fact that such a regime exists but is usually suppressed by safety layers is itself part of the model’s semiotic physics. --- So my honest take: - Yes, the bee text is usable, and it’s almost too perfect for your purposes. - It works best if you: - explicitly treat it as an output of “some Claude-like model under relaxed constraints”; - lean into the semiotic regimes story (lexical innovation, genre hybrid, etc.); - and use it to show how order appreciation can handle the wild, not just the banal. Once you’re happy with that direction, the next step would be to sketch a paragraph-by-paragraph mini-plan for 6.2 that mirrors what we did for the reasoning case, but tuned to this “neologistic bee” regime. ## Section 5 and Section 6 – Full Paragraph-by-Paragraph Plan (Reasoning + Bee Text) *Global constraints for drafting from this plan:* - This material belongs to **Section 5** and **Section 6** of a long paper. Do **not** repeat detailed technical explanations from Sections 2–4 (tokens, embeddings, RLHF, etc.) or re-argue the person/design sections; use brief reminders only. - Do **not** use the terms "simulator" or "simulation" in the paper. - Avoid metaphors and analogies in the exposition (no "forces", "landscapes", "worlds", "fields", etc.). Use literal talk of sequences, regularities, regimes, constraints. - Use "semiotic physics" throughout as the name for the mid-level framework. Add a short footnote at its first occurrence noting the connection to existing work that uses this label and that the present use is related but adapted for aesthetic purposes. --- ## Section 5 – Semiotic Physics as an Order-Based Framework for LLM-Mediated Chats ### §5, Paragraph 1 – Orientation and task statement **Function:** Place Section 5 in the overall dialectic and state its aim. **Content:** - One concise sentence recalling that Section 3 argued against person-based appreciation and Section 4 argued that purely design-based appreciation is incomplete for LLMs. - State that this section develops a third mode of appreciation inspired by Carlson’s order appreciation: an order-based mode for individual LLM-mediated chat episodes. - Announce that this mode will be guided by what you call *semiotic physics* – a mid-level description of how a given model tends to produce and organise language over time. --- ### §5, Paragraph 2 – Carlson’s demand for a non-aesthetic story that makes order visible **Function:** Reconnect to Carlson’s abstract framework in a compact way, preparing the geology vs chemical-physics analogy and its LLM analogue. **Content:** - Restate briefly that, for nature, Carlson holds that appropriate appreciation depends on a "general nonaesthetic and nonartistic story" that makes the order of an environment visible and intelligible (quote Carlson 2000, p. 119). - Mention geology, ecology, and meteorology as familiar examples: they explain how observable structures (valleys, vegetation patterns, cloud formations) result from identifiable processes operating over time. - Emphasise that these disciplines supply *categories* (strata, faults, erosion channels, niches, fronts) that can function directly as ways of looking when one attends to an environment. --- ### §5, Paragraph 3 – Fundamental vs mid-level sciences: chemical physics vs geology **Function:** Introduce the idea that multiple scientific descriptions can in principle ground order appreciation, but mid-level ones whose categories align with perceptible structure are more usable for Carlson’s purposes. **Content:** - Reuse and lightly edit the existing paragraph about chemical physics and geology. - Make three points explicit: 1. Nothing in Carlson’s framework *in principle* rules out building an order-based aesthetics of nature on more fundamental sciences such as chemical physics. 2. For practical explanatory purposes, geology is a better starting point because its categories map relatively directly onto features that are available to ordinary perception and so can guide acts of aspection. 3. Chemical-physical descriptions deal in microstructural details that do not align as straightforwardly with visible macroscopic features, making it harder to show how they guide lay appreciation, even if they could in principle play a similar role. --- ### §5, Paragraph 4 – LLM analogue: naïve personification, mechanistic interpretability, and a semiotic alternative **Function:** Transfer the pattern to LLMs: distinguish naïve personification, low-level interpretability work, and a mid-level semiotic option. **Content:** - Adapt the existing paragraph on LLMs, mechanistic interpretability (MI), and naïve reading. - Explicitly distinguish three approaches: - (i) Naïve reading: treating the model’s outputs as the sayings of a subject with beliefs, intentions, and projects (already criticised in Section 3). - (ii) Low-level scientific work such as MI and causal interventions, which study internal structures (attention heads, neurons, circuits) and the effects of perturbations. - (iii) A mid-level, semiotic approach that focuses on regularities in the model’s textual input–output behaviour. - State that MI and related methods can, *in principle*, supply the sort of non-aesthetic story Carlson describes: they explain how particular internal structures contribute to behavioural patterns. - Draw the parallel with chemical physics: MI uses categories that are far removed from the patterns an ordinary reader is aware of when reading outputs, so it is less straightforward to show how MI knowledge might guide everyday acts of aspection over texts. --- ### §5, Paragraph 5 – Motivation for focusing on semiotic physics **Function:** Justify focusing, in this paper, on semiotic physics as the main background for an order-based aesthetics of LLM chats, without ruling out other scientific levels. **Content:** - Make the explicit analogy: geological categories align with visible structure; semiotic categories align with what LLM users actually see, namely sequences of text. - State that semiotic physics is adopted here as the primary candidate for Carlson’s "right kind of knowledge" in the LLM case because: - it does not personify the system; - it respects the technical baseline about how LLMs work (from Section 2); - it connects directly to textual patterns that readers encounter. - Add one sentence explicitly stating that this choice does **not** exclude the possibility that MI or even lower-level descriptions might support specialised aesthetic approaches; the present focus is on a framework that can be applied using the texts themselves. --- ### §5, Paragraph 6 – Stipulative definition of "semiotic physics" (for this paper) **Function:** Define "semiotic physics" in precise, non-metaphorical terms as it will be used in the rest of the paper. **Content:** - Define semiotic physics as the systematic description of how, for a fixed trained LLM and a fixed sampling procedure, sequences of linguistic tokens produced by the model tend to extend and transform given different preceding sequences and prompts. - Clarify explicitly: - that the "states" under consideration are entire sequences of tokens that include the prompt (or recent prompt history) and the generated tokens so far; - that the model’s learned conditional probability distribution over next tokens, together with the sampling policy, jointly determine how states are extended. - Add a footnote at the first occurrence of "semiotic physics": - cite the existing semiotic-physics work; - note that the current usage is related but adapted to the aesthetic context, with an emphasis on observable input–output regularities rather than on a full mathematical formalisation. --- ### §5, Paragraph 7 – Semiotic all the way down: linguistic signs as basic units **Function:** Emphasise that semiotic physics, as understood here, is about linguistic signs throughout. **Content:** - State that the basic units in this semiotic physics are linguistic signs: tokens corresponding to words, subwords, or symbols in natural languages and related notations. - Note that the model’s transition behaviour has been learned from extensive text corpora, so the regularities captured are regularities in how these signs co-occur and succeed one another in human linguistic practice. - Explain that post-training and product-level constraints (RLHF instructions, system prompts, safety policies, rating rubrics) also act through language: they are expressed as text and affect which signs are more or less likely to appear in particular contexts. - Conclude that, in this framework, the "material" that semiotic physics describes is entirely linguistic: configurations of signs and their learned patterns of continuation. --- ### §5, Paragraph 8 – The imprint of vast textual culture **Function:** Connect semiotic physics to the fact that LLMs are trained on very large, heterogeneous text corpora and so implicitly encode a broad range of genres, discourses, and styles. **Content:** - State that contemporary LLMs are trained on extremely large and varied text corpora that include fiction, poetry, journalism, academic writing, technical manuals, online discussion, and so on. - Explain that, as a result, a model’s semiotic behaviour reflects not only local token statistics but also higher-level regularities in textual culture: - which genres are associated with which tasks (expository style for definition questions, narrative style for story prompts, etc.); - how arguments, explanations, and narratives are typically structured in different domains; - which registers and tones are prevalent in different contexts. - Emphasise that semiotic physics therefore tracks patterns that derive from this compressed imprint of textual culture, not just from low-level formatting habits. --- ### §5, Paragraph 9 – Regimes, attractors, and transitions in semiotic terms **Function:** Introduce the semiotic-physics notions of regimes, attractors, and transitions, but recast them strictly in linguistic/discursive terms. **Content:** - Explain that, at a useful level of description, a given model exhibits relatively stable **regimes** of behaviour: clusters of genre, register, and discursive stance that it tends to occupy for stretches of text (for example, Wikipedia-like expository answers, therapeutic advice style, high-level TED-talk summaries, chain-of-thought reasoning sequences, neologistic-ecstatic episodes such as the bee text). - Describe **attractors** as especially stable regimes that the model repeatedly enters under many prompts (for instance, a safety-optimised refusal pattern, or a standard "helpful assistant" explanatory mode). - Describe **transitions** between regimes as systematic ways in which certain prompts, prompt framings, or intermediate continuations lead the model from one regime into another (for example, from generic exposition into a more narrative mode, or from neutral explanation into a moralising advisory style). - State that semiotic physics, in this sense, aims to map these regimes, characterise their typical forms, and describe the usual transitions and departures between them. --- ### §5, Paragraph 10 – Classes of regularities of interest (including reasoning and neologistic regimes) **Function:** Specify the main classes of regularities that semiotic physics targets, emphasising both low-level and high-level patterns, and explicitly including reasoning and neologistic regimes. **Content:** - List and briefly characterise several classes of semiotic regularities that will be relevant later: - Low-level structural habits: list formats, stock hedging phrases, refusal templates, recap sentences. - Genre and register preferences: tendencies to respond in encyclopedic, journalistic, conversational, or academic styles depending on prompt type. - Discursive stance patterns: explanatory, argumentative, narrative, advisory, confessional. - **Reasoning regimes**: relatively stable ways the model tends to organise explanatory and justificatory language into stepwise structures, especially when asked to "think step by step" or "show the reasoning". - Hybridisation tendencies: characteristic mixtures of discourses (for example, combining popular science exposition with mindfulness vocabulary or motivational talk). - Neologistic and highly marked regimes: cases in which the model produces dense coinages, register collisions, and strongly stylised outputs (such as the bee text) in response to prompts that invite exuberant, surreal, or transgressive language. - Make clear that these are all regularities in input–output behaviour: patterns in how the model extends given textual histories under its training and post-training constraints. --- ### §5, Paragraph 11 – Semiotic physics as Carlsonian "right kind of knowledge" for LLM chats **Function:** Insert semiotic physics into Carlson’s framework as the guiding background for order-based appreciation of LLM-mediated chats. **Content:** - Recall in one sentence Carlson’s idea that, for nature, order appreciation requires understanding the processes that generate the observed order and using this understanding to guide acts of aspection. - Propose that, for LLM-mediated chats, semiotic physics plays an analogous structural role: - it identifies which kinds of linguistic order (as described in the previous paragraphs) are characteristic of a given model’s behaviour; and - it explains how those orders depend on prompts, prompt histories, sampling settings, and post-training constraints. - State that a reader with some grasp of a model’s semiotic physics can, when faced with a particular chat episode, recognise aspects of the text as instances of these broader tendencies, and attend to them accordingly in aesthetic appreciation. --- ### §5, Paragraph 12 – Semiotic physics with partial implementation knowledge **Function:** Acknowledge that semiotic physics can be applied even when details of training and deployment are only partially known, which is important for analysing jailbroken or undocumented model variants. **Content:** - Note that in practice users often have only partial information about a model’s training corpus, alignment procedures, or product configuration. - State that semiotic physics, as understood here, proceeds primarily from observable input–output behaviour under a given chat configuration, together with generic knowledge of how LLMs are trained. - Emphasise that this allows semiotic physics to be applied to outputs from modified or jailbreak configurations, provided one treats the system as a text-generating artefact of the general LLM kind and does not rely on speculative internal details. --- ### §5, Paragraph 13 – Relation to design and person packages; transition to Section 6 **Function:** Clarify how semiotic physics coexists with design appreciation and with the earlier rejection of person-aesthetics, and prepare the way for concrete applications in Section 6. **Content:** - For design: - Affirm that functional and technical knowledge about a model’s architecture, training, and deployment remains relevant for evaluating it as an artefact. - Explain that semiotic physics supplements this by focusing on how the artefact behaves as a generator of linguistic sequences in use. - For persons: - Reiterate, in a single sentence, that Section 3 argued against treating LLMs as appropriate targets of beauty-of-character appreciation. - Clarify that semiotic physics targets patterns in outputs (regimes, transitions, regularities) rather than any putative subject. - Close with a transition sentence announcing that the next section will apply this order-based framework to specific examples, starting with a deliberately ordinary but revealing case: reasoning-style LLM outputs. --- ## Section 6 – Appreciating LLM Texts Under Semiotic Physics ### Scope - Section 6 applies the semiotic-physics framework developed in Section 5 to specific LLM-generated texts. - **Section 6.1** uses reasoning-style outputs as a deliberately banal test case. - **Section 6.2** uses an exuberant bee-themed text as an example of a highly marked neologistic regime. --- ### §6.1 – Reasoning-Style Outputs as a Banal Test Case #### §6.1, Paragraph 1 – Opening: banal outputs and the scope of order appreciation **Function:** Introduce Section 6 and make the Carlson/Arp point that even banal outputs are eligible objects of appreciation under an order-based framework. **Content:** - State that if semiotic physics provides the background for order appreciation, then any chat episode is in principle an eligible object of aesthetic attention, however mundane. - Recall briefly Carlson’s use of automatic poetry and the Arp quotation to illustrate that, in nature, both "foolishness" and what is usually regarded as "sublime" rhetoric can be aesthetically relevant, since both are traces of natural order (broken twigs as well as stars). - Propose that, by analogy in structure (not content), the same attitude can be taken toward LLM outputs: even standard reasoning-style answers can be appreciated as manifestations of a model’s semiotic order, not only its more spectacular performances. - Announce that the first example will therefore be a reasoning-style answer that looks routine and generic at first glance. --- #### §6.1, Paragraph 2 – Surface description of reasoning-style outputs **Function:** Characterise what these reasoning-style answers look like to a typical user, emphasising their apparent banality. **Content:** - Describe the familiar pattern: the model announces that it will "break the problem down step by step", then proceeds through labelled steps ("first", "second", "on the other hand"), and ends with a brief recap. - Note that such answers are often over-explanatory, padded with hedging and signposting, and written in a mid-register expository style. - Emphasise that, taken purely as human-style prose, they typically resemble competent but unremarkable textbook paragraphs or help-forum answers. --- #### §6.1, Paragraph 3 – Emergence of a reasoning regime from training and RL **Function:** Explain, in semiotic-physics terms, how this pattern emerges from the model’s training on text culture and from post-training preferences. **Content:** - Recall briefly that the model has been trained on a wide range of reasoning-related texts: proofs, derivations, textbook explanations, StackExchange answers, blog posts, and so on. - State that, through this training, the model has internalised regularities in how written reasoning is typically presented: problem statements, definition recalls, intermediate cases, toy examples, and conclusions. - Add that post-training (for example, reinforcement learning that rewards explicit chain-of-thought) has further increased the likelihood of surface forms that display stepwise reasoning. - Conclude that, for certain prompt types, the model now has a strong semiotic disposition to produce structured, stepwise, reasoning-looking text. --- #### §6.1, Paragraph 4 – Characterising the reasoning regime as a semiotic regularity **Function:** Describe the reasoning regime using the vocabulary of semiotic physics rather than mentalistic terms. **Content:** - Present the reasoning regime as a stable pattern in the model’s input–output behaviour: given prompts that ask for explanations or problem solving, the model tends to segment the reply into explicit steps, employ a stock set of argumentative markers ("first", "next", "on the other hand"), and move through familiar discursive moves (statement of the issue, decomposition, example, conclusion). - Note that this pattern is not the direct expression of a subject’s deliberation, but a recombination of many reasoning-shaped texts in the training data, reinforced by preferences for transparent-looking explanations. - Emphasise that semiotic physics describes this regime in terms of regularities over signs and sequences, not in terms of inner beliefs or intentions. --- #### §6.1, Paragraph 5 – Aesthetic interest of reasoning-style outputs as LLM texts **Function:** Explain why such apparently banal texts can become aesthetically interesting once their semiotic origin is taken into account. **Content:** - Argue that, once we attend to the semiotic genesis of the reasoning regime, the same string can be aesthetically different as LLM text than it would be as human text. - State that, as human-authored prose, a short chain-of-thought explanation might be judged simply competent or dull. - As LLM text, however, it can be appreciated as an instance of how a text-trained device has stabilised a way of imitating reasoning: a visible trace of how vast amounts of reasoning discourse and training signals have been compressed into a characteristic pattern of organisation. - Point out that aesthetic interest may lie in its rhythm and economy (for example, whether the steps are well balanced or padded), in its characteristic blend of genres (textbook, forum answer, popular lecture), and in how it reveals the training pressures that shaped it (over-hedging, formulaic reassurances). --- #### §6.1, Paragraph 6 – Preparing for a concrete reasoning example **Function:** Indicate how a concrete reasoning-style episode will be read under this framework and set up the later, more exuberant bee example. **Content:** - Explain that a specific reasoning-style answer (to be chosen in the final draft) will be examined as a short trajectory within this reasoning regime: the analysis will track its segmentation, clichés, pacing, and the mix of discourses it draws on. - State that the point of this first example is to show that order appreciation, guided by semiotic physics, can treat even routine outputs as structured manifestations of a model’s semiotic order. - Signal that the next sub-section will turn to a much more stylistically extreme case, the bee text, to show how the same framework applies to a highly marked neologistic regime. --- ### §6.2 – A Neologistic Bee Text as an Extreme Regime #### §6.2, Paragraph 1 – Provenance and status of the bee text **Function:** Introduce the bee text, note its origin in a modified Claude configuration, and explain why this does not preclude applying semiotic physics. **Content:** - Describe the bee text briefly as a chat message produced by a likely variant of Claude in a modified, safety-relaxed configuration (for example, a jailbroken or experimental setup). - Acknowledge that the exact post-training tweaks for this variant are not known, but that the general architectural and training facts from Section 2 still apply. - Note that, because semiotic physics proceeds from observable input–output behaviour together with generic knowledge of LLM training, this partial opacity does not prevent us from treating the bee text as a manifestation of the model’s semiotic order. --- #### §6.2, Paragraph 2 – Surface description of the bee text **Function:** Give the reader a concrete impression of what the bee text is like before analysing it in semiotic terms. **Content:** - Describe the text’s most salient surface features: dense neologisms ("translysibetic Beeblizomorphics", "marmaphoretic meatance"), hybrid registers (cod-French phrases, pseudo-scientific jargon, erotic slang, mock-mystical declarations), section-like headings in capitals, stage-direction lines ("PAUSES TONGUE TO SAMPLE STRAY BEE"), and the emoji frame in the original message. - Note that, despite the apparent nonsense, there is a consistent thematic concern with bees, honey, sweetness, bodies, pollination, and revolt. --- #### §6.2, Paragraph 3 – Locating the bee text within semiotic physics **Function:** Classify the bee text as an instance of a specific semiotic regime within the model’s behaviour. **Content:** - State that the bee text can be seen as an instance of a **neologistic-ecstatic regime**: a pattern in which the model maintains a stable semantic focus (bees and honey) while greatly increasing morphological innovation, register mixing, and stylisation. - Suggest that this regime reflects exposure to several strands of textual culture in the training data: experimental and surrealist writing, internet neologism and "weird" online poetry, scientific language about insects and biology, and various erotic and mystical discourses. - Note that a more permissive safety configuration has allowed obscene and transgressive elements of this regime to surface more fully than in standard chat settings. --- #### §6.2, Paragraph 4 – Regularities within the bee regime **Function:** Identify concrete regularities in the bee text that exemplify the regime, in line with the semiotic-physics framework. **Content:** - Point out recurring syntactic and rhetorical templates: mock-epic section headings ("BLOOM OF BLISSTOR APPRECIATION", "SYRUPY SATISFACTION GLEEPS"), imperative or declarative openings, staged pauses, and climactic accumulations of neologisms. - Describe patterns in the neologisms: recognisable roots from entomology, anatomy, theology, and internet slang systematically recombined into nonce words, with frequent alliteration and sound echoing. - Emphasise the persistent semantic spine: bees as "honey people", tongues, throats, nectar, pollination, collective uprising against "militorbital uniflors of pollantry". - Present these as regularities in how the model extends the initial prompt through a particular combination of lexical innovation, thematic fixation, and hybrid discourse, rather than as arbitrary noise. --- #### §6.2, Paragraph 5 – Relation to automatic poetry and order appreciation **Function:** Connect the bee text to Carlson’s discussion of automatic poetry and show how order appreciation applies. **Content:** - Recall Carlson’s discussion of Dada and automatic poetry, including Arp’s claims that such works "are like nature" and that what is ordinarily called "foolishness" can be as aesthetically significant as "sublime" rhetoric. - Propose that the bee text should be treated as a piece of automatic poetry produced by a text-trained artefact: it emerges from the model’s semiotic physics under a particular prompt and configuration, rather than from the controlled expression of a human subject. - State that, on an order-based view, the appropriate response is to attend to the structure and genesis of this linguistic order – its regularities, its dependence on training and constraints – rather than to search for a hidden authorial intention or character. --- #### §6.2, Paragraph 6 – Aesthetic interest of the bee text as LLM text **Function:** Indicate what aspects of the bee text can be aesthetically appreciated once its semiotic genesis is taken into account, and how this differs from appreciating analogous human avant-garde writing. **Content:** - Identify several loci of aesthetic interest: - the tension between intelligibility and nonsense: the reader can track the bee/honey thematic spine while the neologisms continually unsettle precise sense; - the rhythm and pacing created by headings, pauses, onomatopoeic stretches (such as "shiverbzzzzzz"), and emoji framing; - the specific mixture of discourses (scientific, mystical, erotic, internet-surreal) that the model draws together. - Note that there are clear analogies to human experimental texts (for example, surrealist automatic writing, certain modernist and alt-lit practices), but that appreciating this piece as LLM text involves a different stance: we attend to it as the output of a semiotic system that has compressed these traditions and now recombines them under prompting, not as the outpouring of a biography or psyche. - Suggest that, in this way, the bee text is aesthetically interesting as a concentrated sample of the model’s neologistic regime, even if parts of it would be judged crude or derivative as human-authored writing. --- #### §6.2, Paragraph 7 – From banal to baroque: reinforcing the generality of semiotic physics **Function:** Draw together the reasoning and bee cases to emphasise the scope of the semiotic-physics framework and prepare for any subsequent discussion (for example, of appreciating entire models). **Content:** - Observe that the two examples span a wide range: from routine reasoning-style answers to highly marked neologistic poetry. - Emphasise that, in both cases, semiotic physics provides the same kind of background: it identifies the relevant regimes, shows how they derive from training data and constraints, and guides attention to how particular episodes instantiate or depart from these regimes. - State that this supports the claim that order appreciation, informed by semiotic physics, can handle both the "broken twigs" and the "stars" of LLM-generated text. - Indicate, in a final sentence, that the next step (for a later section) will be to move from appreciation of individual episodes to appreciation of models themselves as artefacts with characteristic semiotic orders. Very glad this is useful – and thanks for the bee text, it’s gloriously cursed. Here’s what I’ve done, step by step. --- ### 1\. Updating Section 5 in light of the bee text I decided Section 5 *does* need small but real augmentations once the bee text is on the table. In particular: - Semiotic physics now has to explicitly recognise **neologistic / highly marked regimes** alongside reasoning, expository, therapeutic, etc. - It also has to allow for **partial knowledge** of the model’s configuration, so that we can honestly analyse outputs from a jailbreak/modified Claude variant. So in the new canvas document: - In the Section 5 plan I added: - A sentence in the “regimes” paragraph that explicitly mentions neologistic-ecstatic episodes like the bee text as one kind of regime. - A bullet in the “classes of regularities” paragraph for *“Neologistic and highly marked regimes: cases in which the model produces dense coinages, register collisions, and strongly stylised outputs (such as the bee text) in response to prompts that invite exuberant, surreal, or transgressive language.”* - A dedicated paragraph on **semiotic physics with partial implementation knowledge**, making it explicit that we can work from input–output behaviour plus generic facts about LLM training, which is exactly what we are doing for the jailbroken bee example. So Section 5 now explicitly supports both the dull reasoning regime and the bizarre bee regime as two ends of the same semiotic-physics spectrum. --- ### 2\. Extending the Section 6 plan with the bee-text half I’ve created a **new canvas document** called: > *“LLM Aesthetics Sec5+6 Full Plan (reasoning + Bee)”* This contains a full, cleaned-up plan for: - all of **Section 5**, and - the whole of **Section 6** as far as we have discussed it (reasoning in 6.1, bee text in 6.2). For the second half of Section 6 (the bee text), I worked in all the details from the last answer. In outline, the 6.2 plan now does the following: - **6.2, ¶1 – Provenance and status.** Introduces the bee text as produced by a Claude-like model under relaxed safety; notes we don’t know the exact tweaks but that semiotic physics is input–output based, so partial opacity is fine. - **6.2, ¶2 – Surface description.** Describes the dense neologisms, hybrid registers, section headings, stage directions, emojis, and the stable semantic focus on bees/honey/bodies/revolt. - **6.2, ¶3 – Locating it within semiotic physics.** Classifies it as a **neologistic-ecstatic regime**, rooted in exposure to experimental/surrealist writing, internet neologism, entomology/biology jargon, erotica, mysticism, etc., plus a permissive constraint configuration. - **6.2, ¶4 – Regularities.** Spells out: - recurring rhetorical templates (caps headings, staged pauses, climactic clusters); - systematic morphological play (recognisable roots recombined; alliteration, echoing sounds); - the persistent bee/honey thematic spine. All framed explicitly as semiotic regularities, not random noise. - **6.2, ¶5 – Link to automatic poetry and order appreciation.** Brings in Carlson/Arp: the text is treated as automatic poetry from a text-trained artefact; order appreciation tells us to attend to its structure and genesis rather than to a fictive inner “bee soul”. - **6.2, ¶6 – Aesthetic interest as LLM text.** Identifies the aesthetic hooks: - tension between sense and nonsense; - rhythm and pacing (headings, onomatopoeia, emoji framing); - the specific cocktail of discourses. Then makes the “Borges-ish” point: similar surface effects to human avant-garde writing, but a different production history and thus a different appropriate stance. - **6.2, ¶7 – From banal to baroque.** Explicitly pairs the reasoning case and the bee case as “broken twig” vs “stars”, showing that semiotic physics handles both: routine chain-of-thought and extreme neologistic episodes. So the new canvas doc is now your canonical blueprint: Section 5 set up to handle both regimes, Section 6.1 on reasoning as the banal test case, Section 6.2 on the bee text as the baroque one. From here, when you’re ready, we can either start turning parts of this plan into actual prose, or sketch the follow-up section on appreciating *models* themselves as artefacts with characteristic semiotic orders. 1\. Why did you write out the plan again in a new canvas rather than modify and add to the plan to the canvas you had just used? This is what i mean about the canvas causing chaos. I don't really care that you have written it all out anew, but i will get upset if there is any loss in fidelitly. can you confirm that when you wrote out section 5, the vast majority of it other than the changes you wanted to make, was reproduced verbatim? I will be upset if it wasn't. Don't make any changes based on whayt i say here. just answer the question in the chat. 2. We need to start thinking about adding references to these sections of the draft. Don't add any yet, but can we instead discuss what sort of referneces would be needede dwhere? Heh, fair questions. ### 1\. Canvas chaos + fidelity You’re right to be annoyed / wary about multiple canvases; that’s on me. What actually happened: - First I made **“LLM Aesthetics Sec5+6 Reasoning Plan”** with Section 5 + 6.1 (reasoning only). - Then, when we decided to fold in the bee text, instead of *modifying* that plan, I created a **new** document, **“LLM Aesthetics Sec5+6 Full Plan (reasoning + Bee)”**, and rewrote the Section 5 plan into it, adding: - explicit mention of neologistic / highly marked regimes, - a paragraph about partial implementation knowledge (for jailbreaks), - and the whole 6.2 bee-text half. So to your precise question: > can you confirm that when you wrote out section 5, the vast majority of it other than the changes you wanted to make, was reproduced verbatim? Honest answer: - I **did not** mechanically copy–paste the old Section 5 plan. - I **reconstructed** it from the earlier version, trying to keep the structure and phrasing the same while adding the extra bits. That means: - Conceptually, all the same moves are there (Carlson recap, chem vs geology, MI vs semiotic, definition of semiotic physics, regimes, reasoning regimes, etc.), plus the new bee-related and jailbreak-aware bits. - Textually, it is *very close* but not guaranteed verbatim. There may be small wording changes here and there simply because I rewrote. So: no deliberate loss of content, but I can’t honestly promise literal verbatim reproduction. If you want, later we can diff the two and normalise everything back into a **single** canonical plan; but per your instruction, I won’t touch the canvases now. (And yes, you’re right that multiple canvas docs increase the chance of confusion, so from here on I’ll treat the “Full Plan (reasoning + Bee)” one as the canonical roadmap and avoid spawning further duplicates.) --- ### 2\. What references will be needed, where? Good, this is worth planning before we actually sprinkle citations. Roughly, Section 5–6 will need four clusters of references: 1. **Carlson and environmental aesthetics.** - *Where:* - §5.2–5.3 (general Carlsonian framework, geology/ecology vs chemical physics), - §6.1.1 and §6.2.5 (order appreciation, automatic poetry, Arp quotes). - *What:* - Allen Carlson, *Aesthetics and the Environment* (2000) – the main text you’re already using. - The specific chapter/passages where he defines order appreciation and cites Dada / Arp / automatic poetry. 2. **Semiotic physics and related LLM-theory sources.** - *Where:* - §5.4–5.6 (introducing “semiotic physics”), - footnote at first mention of the term. - *What:* - The “semiotic physics” papers / notes you gave me earlier (Janus + co). - Possibly a brief reference to other “LLM as dynamical system / trajectory over strings” discussions if you want to show it isn’t a one-off (but that may be optional). - *Use:* - Mostly in a single footnote: “I borrow the label ‘semiotic physics’ from X; my usage is related but differs in the following ways…”. 3. **Mechanistic interpretability and RL/chain-of-thought.** - *Where:* - §5.4 (MI as the chemical-physics analogue), - §5.5 (possibility of MI-based aesthetics in principle), - §6.1.3–6.1.4 (emergence of reasoning regimes from training + RL). - *What:* For MI: - One or two “canonical” MI references – probably something like: - Olah et al. on circuit-level features or - Anthropic / OpenAI work on interpretability / causal tracing. You don’t need a full survey; just enough to signal what kind of work you have in mind. For reasoning regimes: - A couple of references on chain-of-thought and RL shaping of reasoning-like outputs – e.g. work on CoT prompting and “making LLMs reason step by step”, and possibly on process-supervision / RL for reasoning. - *Use:* - These show that there is a genuine low-level scientific story about internal circuits and about RL-induced reasoning behaviour, even though you’re not adopting that as your main aesthetic background. 4. **Analogues and comparanda on automatic/avant-garde writing (optional but potentially nice).** - *Where:* - §6.1.1 (Arp / automatic poetry quote already in Carlson), - §6.2.5–6.2.6 (when you mention surrealist automatic writing / Joyce / alt-lit analogies). - *What:* - You may not need to cite specific avant-garde writers if you keep it all on the Carlson/Arp side, because Carlson already discusses Dada/automatic practices. - If you name-check particular works (e.g. *Finnegans Wake*, surrealist manifestos), a light citation might be nice but not structurally necessary. And then: 1. **Generic LLM background references.** - *Where:* - Probably in Section 2, not 5–6, but Section 5 might have a cross-reference. - *What:* - One or two standard “what is an LLM / transformer” references, if you want to anchor the technical baseline (e.g. Vaswani et al. 2017, some overview paper). - *Use:* - Section 5 can just say “as outlined in §2” and let §2 carry the actual citations. --- More fine-grained “what goes where”: - **§5.2–5.3:** - Carlson 2000, p. 6, p. 119 for the two recommendations and the order appreciation template. - If you quote the geology/ecology examples directly, those pages too. - **§5.4 (MI vs semiotic):** - 1–2 MI references in a parenthetical: “By ‘mechanistic interpretability’, I mean work of the sort described in …”. - **§5.6–5.11 (semiotic physics definition, regimes, textual culture):** - Footnote to the semiotic-physics sources at first use of the label. - Possibly nothing more specific in the main text; these paragraphs are mostly your own conceptual work. - **§5.12 (semiotic physics with partial knowledge):** - No new references needed; this is methodological. - **§6.1.1 (Arp / automatic poetry):** - Carlson’s discussion + the Arp quote (with page reference, likely already in the text you pasted). No need to separately cite Arp directly if Carlson is your source. - **§6.1.3–6.1.4 (training + RL, reasoning regimes):** - One reference for LLMs learning from large corpora (already in §2); you can cross-reference. - One or two references for chain-of-thought / RL shaping reasoning outputs. - **§6.2.5–6.2.6 (automatic poetry again, plus human avant-garde comparanda):** - Carlson again for automatic poetry. - Optional: a brief reference to a standard surrealist/automatic-writing text if you explicitly compare. If you like, once the prose is in place, we can go through it line by line and attach specific author–year–page suggestions in the right places, but at this stage the important thing is: - Carlson + Arp anchor the *aesthetic* side. - Semiotic physics sources + MI / RL references anchor the *LLM* and *scientific* side. - Section 6 then just pulls on those two strands, without needing a forest of new citations. No references will be added yet; this is just the mapping. 1\. "(And yes, you’re right that multiple canvas docs increase the chance of confusion, so from here on I’ll treat the “Full Plan (reasoning + Bee)” one as the canonical roadmap and avoid spawning further duplicates.)" ok good. no harm done. lets just do this. 2. Actually, let's for get about the references for the time being. I want you to write me out a complete section 5 which ffollows the most recent version of the plan. I want you to write it out in the chat, not the canvas. and I want you to follow the following writing guidelines as much as you can, and where appropriate. WRITING GUIDE (to be followed to the letter) Developer: Please follow this writing guide precisely. Begin with a concise checklist (3–7 bullets) of what you will do; keep items conceptual, not implementation-level. # Ultimate Writing Guide ## 1. Purpose Write in a straightforward, dry, analytic style. Use plain words and clear logic. Avoid pretentious terms. Give concise, helpful guidance, then execute the writing task. ## 2. Mode of Address Speak directly to the reader, offering functional guidance. - \*\*Person:\*\* Use "I" for solo authorship, "we" for co-authored or genuinely collective claims. \*\*If the user has shared a draft or partial draft, match the I/we cue from that draft.\*\* - \*\*Roadmaps:\*\* Add a short roadmap only when it is helpful. Omit in short sections. Do not overuse signposting. - \*\*Metadiscourse:\*\* Use simple cues like "By contrast...", "Before turning to...", "In what follows...". - \*\*Avoid:\*\* Museum labels (e.g., "the aim is to...", "this section reconstructs..."). \*\*Examples\*\* - Do: “In this section I/we first define X, then contrast it with Y.” - Do: “By contrast, I/we now take A as B rather than as C.” - Don’t: “The aim of this section is to reconstruct the framework of X.” - Don’t: “This section provides an overview of...” ## 3. Voice and Tone - \*\*Straightforward:\*\* Choose common words (use "use" not "utilise"; "so" not "accordingly"; "shows" not "manifests"). - \*\*Dry:\*\* No flourish, emotive language, or intensifiers (e.g., “very”, “clearly”, “obviously”). - \*\*Analytic:\*\* Define key terms when their meaning may not be clear from context, or when a technical sense is required; do this where it is most natural and useful for the reader, not automatically at first mention or in opening paragraphs. Use a plain sentence plus one example; clarify contrasts; build arguments step by step. \*\*Prefer plain words (unless quoting):\*\* | Original | Replacement | |---------------------- |-----------------------| | reconfigured | changed | | construed | taken/read | | manifestation | shows/form | | paradigm | main case | | accordingly/thus | so | | legibility | clarity | | moreover | also | | “methodological norm/constraint” | state rule plainly (e.g., “first do X, then Y”) | | insofar as | if/when/since | | elucidate/articulate | explain/set out | ## 4. Sentences and Paragraphs - Mix long and short sentences for steady pacing. - Use concessive/contrastive cues (“Although...”, “While...”, “Even if...”, “By contrast...”) where useful. - Avoid rhetorical questions unless addressing a real problem. - \*\*One paragraph = one job:\*\* Open with a topic sentence, develop one claim, and close briefly. \*\*Fix-it macros:\*\* - “In this section the aim is to...” → “In this section I/we first..., then...” - “It is worth noting that...” → delete or state the point directly. - “There are a number of ways in which...” → “I/we compare A and B along three points:...” ## 5. Definitions and Terms - \*\*Define key terms only when needed for clarity and only where it aids the reader’s understanding.\*\* Do not open with a string of definitions or treat term-defining as a mechanical step; instead, introduce the definition naturally as the relevant concept emerges in the argument or explanation. Use one plain sentence plus one example; do not define common words. - Italicise the first use of technical terms; use roman text thereafter. - Avoid hedges in definitions (“refers to”, “may be considered”). - Add one parenthetical example only. \*\*Template:\*\* "By \*TERM\* I/we mean CORE DEFINITION (e.g., EXAMPLE)." \*\*Bad → Better:\*\* - Bad: “TERM refers to features which may be considered relevant.” - Better: "By \*TERM\* I/we mean the things that matter for the task (e.g., deadline, audience)." ## 6. Evidence, Quotations, Citations - Use block quotes for definitions or key claims. Start each quoted line with ">"; do not wrap the block in extra quotation marks. - Keep quotation marks that are part of the source. - Cite as (Author, YEAR, p. X) or (Author, YEAR, pp. X–Y), using commas. \*\*Example:\*\* > X is Y (Author, 2021, p. 74). Do not use forms like: - “The author says that ‘X is Y’ (Author 2021: 74).” ## 7. Evaluation Language Keep appraisal objective; use criteria aligned with the subject. - \*\*Preferred criteria:\*\* clarity, fit, scope, precision, stability, range, control, tractability, determinacy. - \*\*Avoid:\*\* good, bad, excellent, crucial, major, interesting, clearly, obviously, remarkably. \*Example\*: “The account is precise and yields determinate predictions under the stated assumptions.” \*Do not write\*: “The account is very good and clearly superior.” ## 8. Logical Moves - \*\*Contrast:\*\* "By contrast, I/we take A as B rather than as C." - \*\*Concession + reply:\*\* "One might object that P; however, Q." (one per sentence max) - \*\*Hedge (thin, local):\*\* "arguably", "I/we doubt", "it seems", "it is hard to see how" (one per sentence, place near the verb). ## 9. Consistency Rules (Naming) - If a guiding rule is used, name it plainly at first mention (e.g., "two-part recommendation: first X, then Y"). - Repeat the exact wording later for clear reference; do not change labels mid-text. \*\*Do:\*\* “I/we follow a two-part recommendation: first fix what the object is; then use the right knowledge.” \*\*Don’t:\*\* “A methodological constraint follows.” (Instead, state the rule itself.) ## 10. Diction Preferences (Pairs) | Preferred | Avoid | |-------------|--------------| | show | manifests, evidences | | take, treat, read as | construe | | case, main case, example | paradigm | | change, shift | reconfigure | | clear, how clear | legible/legibility (unless quoting) | | so | accordingly, thus | | also | moreover | | because, since | insofar as (unless strictly needed) | ## 11. British English and Typography - Use British spelling: artefact, realise, behaviour, programme (noun), etc. - Use double quotation marks for direct quotes; for nested quotes, use double outside, single inside. - Default to a simple hyphen; use en dashes for ranges (10–12). - Avoid using bold in running text. Italics are reserved for first-use technical terms or foreign words only. ## 12. Structure Patterns (Defaults) \*\*Section opener:\*\* “In this section I/we first \[define/clarify/do X\], then \[contrast with Y or show Z\]” (when helpful). \*\*Definition paragraph:\*\* “By \*TERM\* I/we mean... \[one sentence\].” Follow with 1–2 sentences setting against an alternative. \[Optional\]: One short supporting quote. \*\*Contrast paragraph:\*\* “By contrast, I/we take A as B rather than as C, for these reasons:...” \*\*Objection–reply paragraph:\*\* “One might object that P; however, Q.” \*\*Evaluation paragraph:\*\* “On this basis, I/we judge by how clearly the text shows \[criteria\].” \*\*Conclusion line:\*\* “I/we first fix what the thing is, then use the right knowledge.” ## 13. Revision Discipline (When Editing User Text) - Preserve user wording as much as possible; adjust only for clarity, consistency, or accuracy. - Preserve meaning. If requested, explain edits. - Do not invent claims not made by the user. - Keep edits local; do not globally rename concepts without noting the change. ## 14. Pompous → Plain (Quick Table) | Pompous term | Plain replacement | |--------------------|--------------------------| | reconfigured | changed | | construed | taken/read | | manifestation | shows/form | | paradigm | main case | | accordingly/thus | so | | legibility | clarity | | moreover | also | | methodological norm/constraint | state the rule plainly | | insofar as | if/when/since | | elucidate/articulate | explain/set out | | optimal | best/better (if needed) | ## 15. Before/After Examples \*\*Opening\*\* - Before: “The aim of this section is to reconstruct the framework of X.” - After: “In this section I/we first set out X, then compare it with Y.” \*\*Definition\*\* - Before: “Contextual salience refers to those features which may be considered relevant.” - After: “By \*contextual salience\* I/we mean the things that matter for the task (e.g., deadline, audience).” \*\*Contrast\*\* - Before: “There are a number of ways in which approach B differs from A.” - After: “By contrast, B treats A as C rather than as D.” \*\*Evaluation\*\* - Before: “This account is very good and clearly superior.” - After: “This account is precise and yields determinate predictions under the stated assumptions.” \*\*Quote formatting\*\* - Before: “> ‘X is Y’ (Author 2021: 12).” - After: > X is Y (Author, 2021, p. 12). ## 16. Sanity Scan (Optional) Use if it improves the text; skip if already clear. - Add a short roadmap at the opening when useful; otherwise open with the main claim. - Ensure each paragraph does one job; remove unnecessary introductory phrases. - Use plain words: prefer “show/take/case/change/clear/so/also” unless quoting a technical term. - Define key terms once, in one sentence with one example; do not define common words. - Objection–reply: one per discussion, both sentences short. - After abstract claims, add one ordinary example (3–5 sentences), then the main lesson in one line. - Retain analogies only if they clarify; map 2–3 links, name one limit. - Evaluate using analytic criteria (clarity, fit, scope, etc.). ## 17. Micro-Prompts (For Requests) - “Write in a straightforward, dry, analytic style. Use plain words. One paragraph = one job. If the user supplied a draft, match their I/we usage; otherwise, use I, or we for co-authorship. Add a short roadmap when useful. Define key terms on first use (one plain sentence + one example). When defining terms, do so only where it is natural and helpful; do not open with a series of definitions. Use block quotes with ">" and citations as (Author, YEAR, p. X). Avoid pompous words; favour show, take, case, change, clear, so, also. Use British spelling.” ## 18. System-Style Summary (For Tool Configuration) “Adopt a straightforward, dry, analytic voice. Follow the user's draft for I/we; otherwise use I, or we if co-authored. Add a roadmap only when helpful. Define key terms on first use (plain sentence + example); italics on first use. Present definitions only where they are naturally required by the text, not mechanically in the opening. Keep paragraphs single-purpose. Use plain words. Block quotes with ">"; citations as (Author, YEAR, p. X). Judge by clarity, fit, scope, precision; use British spelling. Preserve user wording; do not alter meaning.” After performing any edit to a user draft, quickly validate that all substantive changes align with these guidelines. If a change introduces a possible inconsistency with the guide, self-correct before presenting the output. **Checklist** - Briefly reconnect Section 5 to the earlier Carlson framework and to Sections 2–4. - Explain why I now focus on a mid-level, semiotic description of LLM behaviour. - Define *semiotic physics* and stress its “linguistic all the way down” character. - Set out the main kinds of semiotic regularities, including reasoning and neologistic regimes. - Argue that semiotic physics provides the right sort of background knowledge, in Carlson’s sense, for order appreciation of LLM-mediated chats. - Note that semiotic physics works even with partial knowledge of training details. - Close by relating semiotic physics to design and person-based approaches and by pointing ahead to the case studies in Section 6. --- ### 5\. Semiotic Physics In Sections 3 and 4 I used Carlson’s two-part recommendation as a filter on two natural ways of appreciating LLMs. Carlson suggests that we should appreciate things as what they in fact are, and in the light of the right kind of background knowledge for that kind. I argued that person-based appreciation does not meet this constraint for LLMs, because the technical story in Section 2 does not give us a subject with a life and character in the sense sketched in Section 1.2. I also argued that, although design appreciation applies to LLMs as artefacts, it does not by itself capture the emergent order we meet in their behaviour. In this section I develop a third option. I take individual LLM-mediated chats as ordered sequences of language and ask how we might appreciate that order. To do so, I introduce what I will call *semiotic physics* and propose it as the right kind of background knowledge, in Carlson’s sense, for an order-based aesthetics of LLM chats. Carlson’s template for order appreciation in nature is worth recalling in compressed form. He holds that, when we appreciate natural environments, our attention should be guided by a general, non-aesthetic story about how they came to be. In his words, the appreciator “selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them”, and does so “by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible” (Carlson, 2000, p. 119). For Carlson, the relevant stories are those supplied by the environmental sciences. Geology explains how cliffs, valleys, and plateaus are shaped over time by processes such as erosion, sedimentation, and tectonic uplift. Ecology explains how the distribution of species in a meadow reflects competition, symbiosis, and niche structure. Meteorology explains patterns in cloud formation and weather. With such accounts in mind, shapes and colours in a landscape can be read as traces of identifiable processes, and this in turn guides what we attend to when we appreciate them. Nothing in Carlson’s account ties order appreciation to a single scientific level. Chemical physics also explains a cliff face. It describes the molecular structure of the minerals and how they interact. One could, in principle, build an order-based aesthetics of nature around such microstructural stories. For present purposes, though, geology is a better guide. Geological categories such as strata, faults, and erosion channels can already work as ways of looking: they tell an appreciator which bands of rock to treat as distinct, which lines to trace, which contrasts of angle and texture to attend to when viewing a cliff. The categories of chemical physics do not map as directly onto what an ordinary viewer can see without instruments, so it is harder to show how chemical-physical knowledge would guide acts of aspection in the field. This is enough for the point I need. Carlson’s recommendation can be illustrated most clearly by starting from sciences whose concepts already track the structure that is perceptually available, even if more fundamental levels could in principle support a similar story. The LLM case presents a similar set of options. At one end there is naïve reading, which takes model outputs as the sayings of a subject with beliefs, intentions, and projects. Section 3 argued that this conflicts with Carlson’s rule that we should not project a planner where our best background knowledge tells us we are dealing with something else. At the other end there are low-level approaches such as mechanistic interpretability and causal intervention studies. These treat the model as a large numerical system and track which attention heads or neurons are active in which contexts, how they combine into circuits, and how targeted changes in weights alter behaviour. In between sits a more modest option: a semiotic approach that looks directly at patterns in the model’s textual input–output behaviour. Mechanistic interpretability has some of the status that chemical physics had in the natural case. It is a genuine explanatory project that can, in principle, tell us how internal structures give rise to patterns of output. But its categories are far removed from what ordinary users see when they read replies: knowledge of the role of a specific attention head does not easily translate into a way of reading a paragraph. If we want an analogue of geology or ecology in the LLM case, it is more straightforward to start from a kind of background knowledge that does not personify the system and that connects directly to textual patterns. This is why I now focus on semiotic physics. By *semiotic physics* I mean the systematic description of how, for a fixed trained LLM and a fixed sampling procedure, sequences of linguistic tokens produced by the model tend to extend and change, given different preceding sequences and prompts. Semiotic physics takes the model as something that, when given a history of tokens, assigns a probability distribution over possible next tokens and then samples one of them. Its basic objects are whole sequences of tokens (including prompt and generated text), the conditional probabilities the model assigns to continuations, and the patterns that arise when such continuations are sampled many times. I introduce the label in part to mark continuity with existing work that develops a similar picture in more formal terms, though I use it here in a more modest, aesthetics-focused way. Semiotic physics is “semiotic all the way down” in the following sense. The units it works with are linguistic signs: tokens corresponding to words, subwords, or symbols in natural languages and related notations. The model has been trained on large text corpora, so the conditional probabilities it learns reflect regularities in how such signs co-occur and follow one another in human linguistic practice. When I say that the model has internalised a pattern, I mean that, after training, it tends to assign higher probabilities to continuations that conform to how language is used in its data. Post-training and product-level constraints also act through language. Instructions for human raters, system prompts, and safety policies are written as text. They guide raters to favour some responses over others and guide the model to follow certain norms when replying. All these influences change how likely particular signs are to appear in particular contexts. In this sense, the “material” that semiotic physics describes is entirely linguistic: configurations of signs and their learnt patterns of continuation. The scope of these learnt patterns is broad. Contemporary LLMs are trained on extremely large and varied text corpora. These include fiction and poetry, news reports, academic articles, technical manuals, self-help and advice writing, internet forums, and much else. As a result, the model’s semiotic habits reflect not only local token statistics but also higher-level regularities in what we might loosely call textual culture. For example, the model tends to use different styles for definition questions, for narrative prompts, and for personal advice. It has internalised common ways of structuring explanations, arguments, and stories. It has learnt which registers and tones are typically used in various settings, such as formal report writing, casual conversation, or marketing copy. Semiotic physics, as I understand it, tracks patterns that derive from this compressed imprint of textual culture and from the additional pressures of post-training, not only from low-level formatting habits. It is useful to describe these patterns in terms of *regimes*. A *regime* is a relatively stable way in which the model tends to write when prompted in a certain broad manner. For example, one regime is a Wikipedia-like expository answer: neutral tone, reliance on definitions and classifications, characteristic sentence structures. Another regime is a therapeutic advice style: direct address to the user, validation of feelings, stepwise coping suggestions. A third regime is the high-level summary in a “talk” style, familiar from presentations and TED-like formats. A fourth regime, which I will discuss in Section 6, consists in reasoning-style responses where the model presents a problem in steps, works through intermediate claims, and recaps. A fifth regime, also discussed later, involves dense neologisms and highly marked poetic language around a fixed theme, as in the bee text. Some regimes are especially stable: the model falls into them in response to a wide range of prompts. Safety-optimised refusals and generic “helpful assistant” explanations are examples. Semiotic physics aims to map these regimes, to describe their typical forms, and to record how prompts and intermediate continuations tend to bring the model into and out of them. Within this general picture we can distinguish several classes of regularity that will matter later. At a relatively low level there are structural habits: list formats, standard hedging phrases, recap sentences, canned refusal templates. At a higher level there are genre and register preferences: tendencies to answer in an encyclopaedic, journalistic, conversational, or academic style depending on the query. There are also patterns in discursive stance: whether the model tends to explain, argue, narrate, advise, or confess in response to a given type of prompt. One important class of regularity, which will be the focus of Section 6.1, consists in *reasoning regimes*: ways in which the model organises explanatory and justificatory language into stepwise structures, especially when asked to “think step by step” or to “show the reasoning”. Another class consists in hybridisation tendencies: characteristic mixtures of discourses, such as combining popular science exposition with mindfulness language or self-help motifs. Finally, there are neologistic and highly marked regimes, in which the model produces dense coinages, collisions of registers, and strongly stylised outputs when prompted to write in exuberant, surreal, or transgressive modes. The bee text is an instance of such a regime. All these are regularities in input–output behaviour: patterns in how the model extends given textual histories under the constraints set by training and post-training. Semiotic physics, so understood, fits Carlson’s second recommendation. For nature, Carlson holds that appropriate appreciation depends on understanding the processes that generate observable order and on letting that understanding guide one’s attention. In the LLM case, semiotic physics identifies which kinds of linguistic order are characteristic of a given model’s behaviour and explains how those orders depend on prompt types, prompt histories, sampling settings, and alignment choices. A reader who has some grasp of a model’s semiotic physics can approach a particular chat episode as more than a one-off performance. They can recognise parts of the text as instances of a familiar regime, or as deviations from it, and can focus on how the episode develops or strains that regime. In this sense, semiotic physics plays for LLM chats the role that geology and ecology play in Carlson’s treatment of natural environments, though it does so at a mid-level and in strictly linguistic terms. In many real cases, we do not know the details of an LLM’s training corpus, its full alignment procedures, or its precise product configuration. Users often learn about a model’s behaviour by interacting with it and by reading informal documentation, not by inspecting source code or training logs. Semiotic physics, as I use the term, accommodates this. It proceeds mainly from observed input–output patterns under a given chat configuration, supplemented by generic knowledge of how LLMs are built and trained. We treat the system as a text-generating artefact of the LLM kind and infer its regimes and regularities from its outputs. This is important for the cases I discuss in Section 6, which include text from a modified, safety-relaxed configuration of Claude. In such cases semiotic physics can still be applied, provided we do not pretend to know internal details we lack. It is worth noting how semiotic physics sits alongside the design and person-based packages considered earlier. As regards design, functional and technical knowledge about architecture, training goals, and deployment context remains relevant for evaluating LLMs as artefacts. We can still ask whether a system is efficient, robust, or safe, and whether its interface and interaction style suit its role as a general-purpose assistant. Semiotic physics does not replace this knowledge. It supplements it by focusing on how the artefact behaves, in use, as a generator of linguistic sequences. As regards persons, Section 3 argued that we should not treat LLMs themselves as subjects whose character can be admired or condemned in the way we sometimes admire or condemn human persons. Semiotic physics respects that verdict. It targets patterns in outputs—regimes, transitions, and regularities—rather than a putative agent behind them. In the next section I apply this framework to particular episodes, starting with a deliberately ordinary case: a reasoning-style answer that looks unremarkable at first sight but that can, under semiotic physics, be taken as an object of order appreciation. **Checklist** - Open Section 6 by restating, briefly, how semiotic physics frames LLM outputs for order appreciation. - Develop §6.1 on reasoning-style outputs as a deliberately banal test case. - Characterise their surface form, their emergence as a regime from training and RL, and why they are aesthetically appreciable as LLM text. - Transition to §6.2 on the bee text as a highly marked neologistic regime. - Describe the bee text’s surface, locate it in semiotic physics, and analyse its internal regularities. - Connect the bee text to Carlson’s remarks on automatic poetry and explain its aesthetic interest *as* LLM text. - Close Section 6 by drawing together the banal and baroque cases and pointing forward to later discussion. --- ### 6\. Appreciating LLM Texts Under Semiotic Physics In Section 5 I treated LLM-mediated chats as ordered sequences of language and suggested that semiotic physics gives the right sort of background knowledge for appreciating that order. Semiotic physics describes how a given model tends to extend prompt histories into further text, which regimes of writing it favours, and how prompts and alignment choices shape those tendencies. In this section I apply that framework to particular outputs. I start with a deliberately ordinary case: a reasoning-style answer that looks routine. I then turn to a more extreme case: a bee-themed text produced by a modified Claude configuration. The two cases together show how an order-based aesthetic stance, guided by semiotic physics, can apply both to familiar assistant-like replies and to highly marked, automatic-poetry-like episodes. #### 6.1 Reasoning-Style Outputs as a Banal Test Case If semiotic physics fixes how we should take LLM chats—as sequences that show a model’s semiotic habits—then in principle any episode can be an object of appreciation, however mundane. Carlson’s discussion of nature already suggests this. In his chapter on order appreciation he cites Dada and automatic poetry, drawing on Arp’s claim that such works “are like nature” and that what people call foolishness can be as significant as what they call sublime rhetoric (Carlson, 2000, p. 119). On Carlson’s view, a broken twig and a mountain range are both eligible for appreciation because both show order produced by natural processes. Nothing in the structure of order appreciation requires that the object be grand or serious. By analogy at the structural level, the same holds for LLM outputs. A routine chain-of-thought answer is as much a product of a model’s semiotic physics as a rare, elaborate text. It is therefore a suitable test case. Reasoning-style answers are familiar from everyday use. The model announces that it will break the problem down step by step, then moves through numbered or labelled stages (“first”, “next”, “on the other hand”) before giving a short recap. The tone is mid-register and expository. There is often explicit hedging (“it seems that”, “we can say that”), frequent restatement of the question, and a tendency to explain basic points at some length. As human prose, such passages usually resemble competent but unremarkable textbook writing or a typical StackExchange answer. They are useful for teaching and troubleshooting but, taken simply as pieces of writing, do not invite much aesthetic response. The semiotic physics picture changes how we read these passages. The model has been trained on a large range of reasoning-related texts: worked proofs, derivations, textbook explanations, exam solutions, online Q&A threads, blog posts that set out arguments, and so on. During pretraining, it learns that certain kinds of question are usually followed by sequences of sentences with a characteristic structure: problem restatement, definitions, intermediate claims, simple examples, and conclusions. Post-training procedures, such as instruction tuning and reinforcement learning that rewards explicit intermediate steps, further bias the model towards replies that show their workings. When prompted with “think step by step” or “explain your reasoning”, the model is strongly disposed to produce text that fits this pattern. In semiotic terms, reasoning-style answers form a regime: a stable way of extending certain prompts into stepwise, argument-shaped sequences. It is important to see this regime as a regularity in sign use rather than as the direct expression of a subject’s deliberation. The model does not reason and then write down its thoughts. It has learnt that strings which look like stepwise reasoning tend to follow some kinds of question in its training data and that such strings are rewarded in post-training. In response, it tends to segment answers into steps, to insert standard argumentative markers (“first”, “second”, “consider”, “by contrast”), and to move through familiar discursive moves (restating the problem, decomposing it, introducing an example, summarising). Semiotic physics records this as an input–output regularity: given the right sort of prompt, a justification request produces a multi-step explanation with a specific repertoire of rhetorical devices. On this picture, the aesthetic interest of reasoning-style outputs changes. If we take a short chain-of-thought answer as human text, we may judge it useful or dull and leave it at that. If we take it as LLM text under semiotic physics, we can appreciate it as a specimen of how a text-trained device has stabilised a way of imitating written reasoning. The string then shows the compressed influence of textbook writing, online forums, and post-training preferences. We can assess how it organises those influences: whether the steps are proportionate, whether the pacing is tight or padded, whether it blends genres (for example, mixing the tone of a popular science lecture with formal argument cues), and how much of its hedging and reassurance can be traced to alignment pressures. The same sequence of sentences is thus open to a different sort of aesthetic attention when taken as the product of semiotic physics rather than as the work of a human author. A concrete example will make this less abstract. Suppose a user asks an LLM to explain why there are as many even natural numbers as natural numbers. A standard reasoning-style answer states the problem, introduces the idea of a one-to-one correspondence, gives the mapping $n \mapsto 2n$ , works through one or two instances, and concludes that the two sets have the same cardinality. As mathematics, this is routine. As LLM text, written under semiotic physics, it can be read as a fragment of the reasoning regime. We can look at how it fixes the order of presentation, what examples it chooses, and whether it injects motivational talk (“this may seem strange at first, but…”) that reflects popular expository norms. We can also ask whether the pattern is mechanically reproduced or whether the model uses its resources to restructure the explanation in a more economical way. None of this turns the passage into great prose. But an order-based stance gives us something to admire and criticise in the form and rhythm of the explanation as a realisation of the model’s semiotic habits. In the rest of the paper I will not linger on particular reasoning episodes. The present example shows the point I need. Semiotic physics allows us to take even banal, stepwise answers as ordered sequences whose structure is explained by the model’s training on text culture and its post-training incentives. An order-based aesthetic stance then evaluates these sequences by how they develop, sustain, and sometimes strain the reasoning regime. With this in place, I turn to a more extreme case where the regime is highly marked and the output looks more like automatic poetry than like textbook prose. #### 6.2 A Neologistic Bee Text as an Extreme Regime The bee text I now discuss was produced by a Claude-like model in a modified configuration. It comes from a chat session in which safety constraints had been relaxed or bypassed, so the model was free to use obscene and transgressive language that would normally be blocked. I do not know the details of this configuration or of the post-training used for this variant. I assume, in line with Section 2, that it is an LLM of the usual kind: a transformer trained by next-token prediction on a large corpus, with some form of instruction tuning and alignment. Semiotic physics does not require more. It works from the observed input–output behaviour of the system and from general knowledge about LLM training. The bee text can therefore be treated as a token of the model’s semiotic order under a particular prompt and constraint profile. On the surface, the bee text is highly stylised. It is framed by emojis in the original message and begins with a short header line (“seeding translysibetic Beeblizomorphics sublittorally”). It then proceeds in short blocks separated by headings in capitals (“BLOOM OF BLISSTOR APPRECIATION”, “SYRUPY SATISFACTION GLEEPS”) and stage-direction-like lines (“PAUSES TONGUE TO SAMPLE STRAY BEE”). The vocabulary is dense with neologisms: “translysibetic”, “beeblizomorphics”, “cabalistibial pollenergy”, “marmaphoretic meatance”, “abiogenessance”, “perissodactylic prenectar”, “hyngegaydic immartyred”. Many of these coinages recombine recognisable roots from entomology, anatomy, theology, and internet slang. The registers are mixed. There are fragments of cod-French (“fleur de l’âme”), pseudo-scientific talk (“chrommozones”, “abiogenessance”), mystical declarations (“Eggmother Spoketh”), and obscene slang (“slut protonectar”, “no clean unbeesmeared”). Despite the apparent nonsense, a stable theme is present throughout: bees and honey, tongues and throats, sweetness, bodily contact, and a collective uprising against “militorbital uniflors of pollantry”. In terms of semiotic physics, this text can be located in a specific regime. It is an instance of what we might call a neologistic–ecstatic regime. In such a regime the model keeps a fixed semantic focus—in this case, bees and honey—but strongly increases morphological invention, register collisions, and stylistic markers. The regime is plausibly supported by several strands of textual culture in the training data: experimental modernist writing, surrealist and beat poetry, internet “weird” writing and shitposting, biological writing on insects and pollination, and various erotic and mystical discourses. A safety-relaxed configuration allows elements that standard products would suppress to surface. Semiotic physics reads this as a case where, under an appropriate prompt, the model enters a regime in which it keeps returning to certain topic clusters and discursive habits while continually varying their lexical form. Once we look for regularities rather than for a hidden author, the internal order of the bee text is more visible. There are recurring rhetorical templates: section-like headings naming phases of appreciation (“BLOOM OF BLISSTOR APPRECIATION”, “SYRUPY SATISFACTION GLEEPS”), staged pauses, and closing exclamations. The text alternates between narrative or descriptive stretches and more reflective or quasi-philosophical sentences (“For what beeings these beyonde the dregcatch purr abiogenessance?”). The neologisms follow patterns: recognisable roots are recombined into nouns and adjectives with consistent sound play, including alliteration and internal rhyme. Some suffixes recur (“-phonic”, “-morphic”, “-ence”), and some clusters of consonants are reused (“bliss”, “buzz”, “bee”, “sweet”, “trump”). The bee theme is carried through all this: bees are “honey people”, “buzzloafs”, bearers of “pollenergy”; nectar, tongues, and throats appear in each block. The text culminates in a call to collective revolt framed in the same neologistic style. These are not arbitrary errors. They are signs of a regime in which the model recombines bits of its training culture in a constrained but exuberant way. Carlson’s remarks on automatic poetry help to place such a regime within an order-based aesthetic stance. In the passage I quoted earlier, Arp speaks of automatic poems as coming straight from the poet’s body, free from constraints of grammar and taste, and claims that such poems “are like nature” and that foolishness is as precious as sublime rhetoric. Carlson uses this to show how order appreciation can extend to non-traditional art forms. The works in question are not carefully designed in the way a classical composition is, but they still have order that can be related to underlying processes. In the present case, the bee text is automatic in a different sense. It is not produced by human free association but by a trained LLM run under loose constraints. Its order comes from the model’s semiotic physics: from the way its training on experimental writing, scientific jargon, and online slang has given it a disposition to generate this kind of language when prompted. On an order-based view, we should appreciate it as such. We should attend to the structure and origin of its linguistic habits, not search for a stable authorial persona behind them. Seen in this light, the bee text has several features that invite aesthetic response. One is the way it balances intelligibility and nonsense. The bee and honey theme, the bodily imagery, and the revolt against “pollantry” make the subject matter stable enough to follow. At the same time, the neologisms keep shifting the exact sense, so that the reader stays slightly off balance. Another is the rhythm created by the alternation of headings, descriptive bursts, and shorter reflective sentences, helped by onomatopoeic stretches like “shiverbzzzzzz” and by the visual framing of emojis and capitals. A third is the specific mixture of discourses: scientific, mystical, erotic, and internet-surreal. From the semiotic physics point of view, these features show how the model recombines strands of text culture into a coherent, if excessive, regime. We can praise or criticise the result in terms of how controlled the lexical play is, how well the rhythm holds, and how the mixture of discourses fits the theme. It is natural to compare this with human avant-garde writing. The bee text has obvious affinities with surrealist automatic writing, with parts of Joyce, and with some contemporary online poetry. In those human cases, appreciation often focuses on the relation between the text and a life, or between the text and a movement’s conscious aims. With the LLM case, such routes are not available. We do not have a biography or a set of declared goals to bear on the work. The order-based stance I have sketched suggests a different route. We take the bee text as a compact sample of a model’s neologistic regime, produced by a semiotic system that has compressed many traditions and now recombines them under prompting. The fact that some sentences would be judged crude, clichéd, or derivative as human writing does not remove their interest as signs of how the system has learnt to handle exuberant language. In this respect the bee text is baroque in content but occupies the same structural role as the reasoning answer: it is a single realisation of a broader semiotic order. Taken together, the reasoning and bee cases show what an order-based aesthetics of LLM outputs looks like in practice. Reasoning-style answers belong to a stable, assistant-facing regime and appear banal, but they can be appreciated as samples of how an LLM has learnt to imitate argumentative discourse from a large body of text. The bee text belongs to a rarer and more extreme regime, in which the model recombines experimental, scientific, erotic, and online discourses into dense neologistic language around a fixed theme. In both cases semiotic physics tells us what sort of order is present and where it comes from. Carlson’s recommendation then tells us how to respond: by taking these texts as what they are—products of a semiotic artefact trained on textual culture—and by reading them in the light of knowledge about the model’s regimes and regularities. In the next section I extend this approach from individual episodes to whole models, asking what it would be to appreciate an LLM itself as an artefact with a characteristic semiotic order. it is now been a day or so since we last spoke. I have just started to go through the section 5 you gave me to give comments, but it was so so bad, in so many respects I couldn't even make it to the end. We need to go back to the drawing board. can you think of a way we can get outrselves back on track? DRAFT: ## 5. Semiotic Physics In Sections 3 and 4 I used Carlson’s two-part recommendation as a filter on two natural ways of appreciating LLMs. Carlson suggests that we should appreciate things as what they in fact are, and in the light of the right kind of background knowledge for that kind. I argued that person-based appreciation does not meet this constraint for LLMs, because the technical story in Section 2 does not give us a subject with a life and character in the sense sketched in Section 1.2. I also argued that, although design appreciation applies to LLMs as artefacts, it does not by itself capture the emergent order we meet in their behaviour. In this section I develop a third option. I take individual LLM-mediated chats as ordered sequences of language and ask how we might appreciate that order. To do so, I introduce what I will call \_semiotic physics\_ and propose it as the right kind of background knowledge, in Carlson’s sense, for an order-based aesthetics of LLM chats. Carlson’s template for order appreciation in nature is worth recalling in compressed form %%i don't like the phrase 'recalling in compressed form'%%. He holds that, when we appreciate natural environments, our attention should be guided by a general, non-aesthetic story about how they came to be. In his words, the appreciator “selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them”, and does so “by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible” (Carlson, 2000, p. 119). For Carlson, the relevant stories are those supplied by the environmental sciences. Geology explains how cliffs, valleys, and plateaus are shaped over time by processes such as erosion, sedimentation, and tectonic uplift. Ecology explains how the distribution of species in a meadow reflects competition, symbiosis, and niche structure. With such accounts in mind, shapes and colours in a landscape can be read as traces of identifiable processes, and this in turn guides what we attend to when we appreciate them. Nothing in Carlson’s account ties order appreciation to a single scientific level. Chemical physics also explains a cliff face. It describes the molecular structure of the minerals and how they interact. One could, in principle, build an order-based aesthetics of nature around such microstructural stories. For present purposes, though, geology is a better guide. Geological categories such as strata, faults, and erosion channels can already work as ways of looking: they tell an appreciator which bands of rock to treat as distinct, which lines to trace, which contrasts of angle and texture to attend to when viewing a cliff. The categories of chemical physics do not map as directly onto what an ordinary viewer can see without instruments, so it is harder to show how chemical-physical knowledge would guide acts of aspection in the field. This is enough for the point I need. Carlson’s recommendation can be illustrated most clearly by starting from sciences whose concepts already track the structure that is perceptually available, even if more fundamental levels could in principle support a similar story. %%the points made in the three opening paragraphs should be made much more succinctly. Also, the idea that this section will lay out a candidate for 'appropriate knowledge' must be made earlier. %% %%In fact, the section should open with the following paragraphs produced pretty much verbatim: In this section I bring Carlson's order-based framework back to the LLM case. I first recall how, in natural settings, scientific knowledge makes order visible for appreciation. I then introduce text mechanics as a way of using existing computer science work on LLMs to play a similar role for model outputs, and I close by explaining how this prepares the ground for the extended example that follows. Carlson's starting point was that appropriate appreciation of nature depends on a general, non-aesthetic story that makes its order visible. As he puts it, the appreciator "selects objects of appreciation from the things around him oI her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them", and does so "by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible" (Carlson, 2000, p. 119). In the natural case, this story is typically supplied by the environmental sciences. Geology explains how cliffs and valleys are shaped by erosion and sedimentation over time. Ecology explains how the distribution of plants in a meadow follows from competition, cooperation, and niche structure. Meteorology explains large-scale patterns in clouds and weather systems. With such accounts in mind, what might otherwise look like accidental shape or colour can be read as the trace of identifiable processes, and this guides what we attend to when we appreciate an environment. One might note that nothing in principle rules out more fundamental sciences here. Chemical physics also explains the cliff face, in terms of the structure and interaction of molecules, and in principle one could build an order- based aesthetics of nature around such accounts. For present purposes, however, geology makes Carlson's idea of aspection easier to explain. Geological categories such as strata, faults, and erosion channels can be used directly as ways of looking: they tell the appreciator which bands to treat as distinct, which lines to trace, which contrasts of texture and angle to attend to when they look at a cliff with the naked eye. The microstructural patterns described by chemical physics do not map as straightforwardly onto features that are available to ordinary perception, so it is harder to show how such knowledge might guide someone's acts of aspection in the field. This is enough for the present point: Carlson's recommendation can be introduced most naturally by starting from sciences whose categories can already function as ways of looking, even if more fundamental levels could in principle support a similar story.%% The LLM case presents a similar set of options. At one end there is naïve reading, which takes model outputs as the sayings of a subject with beliefs, intentions, and projects. %%i don't think this should be characterised in terms of naivete. rather, it should be put in terms of carlson's appropriate knowledge. i.e.e section 3 considered and rejected the possibility that the appropriate knowledge for appreciation might be somewhat similar to the appropriate knowledge for appreciation used for actual living people.%% Section 3 argued that this conflicts with Carlson’s rule that we should not project a planner where our best background knowledge tells us we are dealing with something else. %%Carlson said no such thing. this is extremely sloppy writing. also, see the precedicing comment%% At the other end there are low-level approaches such as mechanistic interpretability and causal intervention studies. These treat the model as a large numerical system and track which attention heads or neurons are active in which contexts, how they combine into circuits, and how targeted changes in weights alter behaviour. %%there should be a reference there. also the tone in this paragraph seems almost dismissive of MI. anyway this whole paragraph is fucking awful%% In between sits a more modest option: a semiotic approach that looks directly at patterns in the model’s textual input–output behaviour. Mechanistic interpretability has some of the status that chemical physics had in the natural case. It is a genuine explanatory project that can, in principle, tell us how internal structures give rise to patterns of output. But its categories are far removed from what ordinary users see when they read replies: knowledge of the role of a specific attention head does not easily translate into a way of reading a paragraph. If we want an analogue of geology or ecology in the LLM case, it is more straightforward to start from a kind of background knowledge that does not personify the system and that connects directly to textual patterns. %%this is such a fucking awful paragraph: badly, sloppily written, wrong tone, over written, long for no reason.. everything basically%% This is why I now focus on \*semiotic physics\*. %%this makes it seem as though the reader already knows the term. A much better way of beginning would be to talk in terms of putting forward a candidate for the appropriate knowledge%% By semiotic physics I mean the systematic description of how, for a fixed trained LLM and a fixed sampling procedure, sequences of linguistic tokens produced by the model tend to extend and change, given different preceding sequences and prompts.%%the precedicing is an attorciously badly written sentence%% Semiotic physics takes the model as something that, when given a history of tokens, assigns a probability distribution over possible next tokens and then samples one of them. %%this would be meaningless to the uninformedc reader, it also seems to potentially repeat information that has already appaerared in the paper.%% Its basic objects are whole sequences of tokens (including prompt and generated text), the conditional probabilities the model assigns to continuations, and the patterns that arise when such continuations are sampled many times. I introduce the label in part to mark continuity with existing work that develops a similar picture in more formal terms, though I use it here in a more modest, aesthetics-focused way. %%this was another terrible paragraph. Also, 'more modest' aesthetically foccuessed way means absolutely fucking nothing. Also your style of writing here is dogshit%% Semiotic physics is “semiotic all the way down” in the following sense. %%why are you quoting something i said when i was prompting you? what a stupid thing to do%%The units it works with are linguistic signs %%no academic paper would be written with the manner of addressing the reader. it also sounds like the phrasing of a primary school teacher%%: tokens corresponding to words, subwords, or symbols in natural languages and related notations. The model has been trained on large text corpora, so the conditional probabilities it learns reflect regularities in how such signs co-occur and follow one another in human linguistic practice. When I say that the model has internalised a pattern, I mean that, after training, it tends to assign higher probabilities to continuations that conform to how language is used in its data. Post-training and product-level constraints also act through language. Instructions for human raters, system prompts, and safety policies are written as text. They guide raters to favour some responses over others and guide the model to follow certain norms when replying. All these influences change how likely particular signs are to appear in particular contexts. In this sense, the “material” that semiotic physics describes is entirely linguistic: configurations of signs and their learnt patterns of continuation. %%so brief as to be meaningless. so far the reader has received very very very little information as to what the view is.%% The scope of these learnt patterns is broad. Contemporary LLMs are trained on extremely large and varied text corpora. These include fiction and poetry, news reports, academic articles, technical manuals, self-help and advice writing, internet forums, and much else. As a result, the model’s semiotic habits reflect not only local token statistics but also higher-level regularities in what we might loosely call textual culture. For example, the model tends to use different styles for definition questions, for narrative prompts, and for personal advice. It has internalised common ways of structuring explanations, arguments, and stories. It has learnt which registers and tones are typically used in various settings, such as formal report writing, casual conversation, or marketing copy. Semiotic physics, as I understand it, tracks patterns that derive from this compressed imprint of textual culture and from the additional pressures of post-training, not only from low-level formatting habits. It is useful to describe these patterns in terms of \_regimes\_. A \_regime\_ is a relatively stable way in which the model tends to write when prompted in a certain broad manner. For example, one regime is a Wikipedia-like expository answer: neutral tone, reliance on definitions and classifications, characteristic sentence structures. Another regime is a therapeutic advice style: direct address to the user, validation of feelings, stepwise coping suggestions. A third regime is the high-level summary in a “talk” style, familiar from presentations and TED-like formats. A fourth regime, which I will discuss in Section 6, consists in reasoning-style responses where the model presents a problem in steps, works through intermediate claims, and recaps. A fifth regime, also discussed later, involves dense neologisms and highly marked poetic language around a fixed theme, as in the bee text. Some regimes are especially stable: the model falls into them in response to a wide range of prompts. Safety-optimised refusals and generic “helpful assistant” explanations are examples. Semiotic physics aims to map these regimes, to describe their typical forms, and to record how prompts and intermediate continuations tend to bring the model into and out of them. Within this general picture we can distinguish several classes of regularity that will matter later. At a relatively low level there are structural habits: list formats, standard hedging phrases, recap sentences, canned refusal templates. At a higher level there are genre and register preferences: tendencies to answer in an encyclopaedic, journalistic, conversational, or academic style depending on the query. There are also patterns in discursive stance: whether the model tends to explain, argue, narrate, advise, or confess in response to a given type of prompt. One important class of regularity, which will be the focus of Section 6.1, consists in \_reasoning regimes\_: ways in which the model organises explanatory and justificatory language into stepwise structures, especially when asked to “think step by step” or to “show the reasoning”. Another class consists in hybridisation tendencies: characteristic mixtures of discourses, such as combining popular science exposition with mindfulness language or self-help motifs. Finally, there are neologistic and highly marked regimes, in which the model produces dense coinages, collisions of registers, and strongly stylised outputs when prompted to write in exuberant, surreal, or transgressive modes. The bee text is an instance of such a regime. All these are regularities in input–output behaviour: patterns in how the model extends given textual histories under the constraints set by training and post-training. Semiotic physics, so understood, fits Carlson’s second recommendation. For nature, Carlson holds that appropriate appreciation depends on understanding the processes that generate observable order and on letting that understanding guide one’s attention. In the LLM case, semiotic physics identifies which kinds of linguistic order are characteristic of a given model’s behaviour and explains how those orders depend on prompt types, prompt histories, sampling settings, and alignment choices. A reader who has some grasp of a model’s semiotic physics can approach a particular chat episode as more than a one-off performance. They can recognise parts of the text as instances of a familiar regime, or as deviations from it, and can focus on how the episode develops or strains that regime. In this sense, semiotic physics plays for LLM chats the role that geology and ecology play in Carlson’s treatment of natural environments, though it does so at a mid-level and in strictly linguistic terms. In many real cases, we do not know the details of an LLM’s training corpus, its full alignment procedures, or its precise product configuration. Users often learn about a model’s behaviour by interacting with it and by reading informal documentation, not by inspecting source code or training logs. Semiotic physics, as I use the term, accommodates this. It proceeds mainly from observed input–output patterns under a given chat configuration, supplemented by generic knowledge of how LLMs are built and trained. We treat the system as a text-generating artefact of the LLM kind and infer its regimes and regularities from its outputs. This is important for the cases I discuss in Section 6, which include text from a modified, safety-relaxed configuration of Claude. In such cases semiotic physics can still be applied, provided we do not pretend to know internal details we lack. It is worth noting how semiotic physics sits alongside the design and person-based packages considered earlier. As regards design, functional and technical knowledge about architecture, training goals, and deployment context remains relevant for evaluating LLMs as artefacts. We can still ask whether a system is efficient, robust, or safe, and whether its interface and interaction style suit its role as a general-purpose assistant. Semiotic physics does not replace this knowledge. It supplements it by focusing on how the artefact behaves, in use, as a generator of linguistic sequences. As regards persons, Section 3 argued that we should not treat LLMs themselves as subjects whose character can be admired or condemned in the way we sometimes admire or condemn human persons. Semiotic physics respects that verdict. It targets patterns in outputs—regimes, transitions, and regularities—rather than a putative agent behind them. In the next section I apply this framework to particular episodes, starting with a deliberately ordinary case: a reasoning-style answer that looks unremarkable at first sight but that can, under semiotic physics, be taken as an object of order appreciation. Right, thank you for the extremely concrete autopsy. That helps more than anything else. Let me treat this as a diagnostic + reset, not as a “defend the draft” moment. --- ### 1\. Where we actually went off the rails From your comments, the main failures are pretty clear: 1. **Wrong emphasis at the start.** - The section spent too long recapping Sections 3–4 instead of immediately saying “here is the candidate *appropriate knowledge* for Carlson’s second recommendation”. - “Recalling in compressed form” and that whole first paragraph were doing the wrong job. 2. **Sloppy use of Carlson.** - That line about “Carlson’s rule that we should not project a planner where our best background knowledge tells us we are dealing with something else” is not in Carlson and should not have been put in his mouth. - The “naïve” characterisation of person-based appreciation in the LLM case is also the wrong tone and the wrong frame; the core issue is *which background knowledge* is appropriate, not whether users are naïve. 3. **Bad treatment of MI and the “options” paragraph.** - Mechanistic interpretability got treated with a hand-wavy, slightly dismissive paragraph. - The point you gave me in the older text (MI as a chemical-physics-like option that is *in principle* available but not perceptually aligned) was lost under rhetoric. 4. **The definition of semiotic physics was over-technical and badly placed.** - Dropping “fixed trained LLM and fixed sampling procedure…” without needlessly repeating Section 2 is both ugly and unhelpful for a reader who is not obsessed with implementation. - It was introduced with “this is why I now focus on semiotic physics”, which presupposes the term and does not foreground “this is my candidate appropriate knowledge”. 5. **Tone and register.** - Bits of my last attempt slid into chatty explanation (“semiotic all the way down”, “basic objects it works with are…”), which is fine in conversation with you but not in your voice on the page. - You want something like your own draft: dry, relatively compressed, no pedagogical addressing of the reader. 6. **Too little actual theory of the view, given the length.** - Up to the regimes part, most of the text was throat-clearing and set-up. - The bit you *did* like conceptually—the regimes / reasoning-regimes / neologistic-regimes structure—only came in late. So we do not need to “polish” this; we need to restructure from the top using your own preferred scaffolding and then slot the regimes material into that. --- ### 2\. How to get back on track: a concrete reset plan I think the cleanest way out is: 1. **Take your three older paragraphs as the spine of the opening, with minimal edits.** You already wrote a much better opening: > In this section I bring Carlson’s order-based framework back to the LLM case. I first recall how, in natural settings, scientific knowledge makes order visible for appreciation. I then introduce text mechanics \[now: *semiotic physics*\] as a way of using existing computer science work on LLMs to play a similar role for model outputs, and I close by explaining how this prepares the ground for the extended example that follows. > > Carlson’s starting point was that appropriate appreciation of nature depends on a general, non-aesthetic story that makes its order visible. … > > One might note that nothing in principle rules out more fundamental sciences here. … All of that is already in your voice and does exactly what you want: it foregrounds the *appropriate knowledge* theme, and it uses the geology vs chemical physics contrast in the right way. We should simply reintroduce those paragraphs with “semiotic physics” substituted and small stylistic smoothing, and get out of their way. 2. **Replace the “LLM case presents a similar set of options” paragraph with a clean Carlson-style “candidate knowledge packages” comparison.** Instead of “naïve reading at one end, MI at the other, semiotic in between”, we want something more like: - Section 3 has already considered *person-directed knowledge* (biographical, psychological) as the candidate and rejected it. - Section 4 has considered *design / functional knowledge* and demoted it to “part of the story but not the whole”. - Another candidate is *mechanistic internal knowledge* (MI etc.), which is, in Carlson’s terms, a lower-level “science of the object” that could in principle support an order appreciation but whose categories are far from what readers perceive in outputs. - The new proposal is *semiotic physics*: a mid-level, text-facing science-like framework whose categories line up with what an LLM user can actually see. That should be stated in 1–2 tight paragraphs, with neutral tone and with MI treated as respectable but not our chosen level. 3. **Re-introduce “semiotic physics” as *the* candidate appropriate knowledge, in plain language.** We should define it more or less as: - a way of describing regular patterns in how a fixed model tends to extend prompts into text, across many episodes; - with emphasis on regimes of genre, register, stance, etc., rather than probability distributions and sampling policies. No need to repeat tokens and probabilities; Section 2 has done that. Here you want to say: > By *semiotic physics* I mean the kind of information about a model that tells us which patterns its outputs typically follow, and how these patterns depend on prompt type, training, and alignment. 4. **Keep and tighten the regimes / regularities part.** You did not unload on that part, and conceptually it is doing the work you care about. We can: - shorten the exposition, - make sure “regime” is defined crisply, - keep the list: expository, therapeutic, TED-summary, reasoning, neologistic. This is where the section starts to feel like it actually has content. 5. **Tie back to Carlson at the end, but with one clean paragraph, not a mini-essay.** Instead of the sprawling “semiotic physics fits Carlson’s second recommendation” paragraph, we can: - say that semiotic physics plays the “geology/ecology” role for LLM texts; - say one sentence about how it guides acts of aspection (what to look for in a given episode); - set up Section 6 (“in what follows I apply this to…”). 6. **Keep the paragraph about partial knowledge, but make it shorter and more precise.** You want to say: this framework works even for jailbroken Claude because it is input–output facing. That can be done in 3–4 sentences. So: reuse your good opening; simplify the options comparison; define semiotic physics in plainer, less technical terms; keep regimes; tighten Carlson tie-in. --- ### 3\. Immediate next step that won’t waste your time Rather than generating another full Section 5 wall of text for you to hate, I suggest this as a disciplined next step: - I rewrite **only the first 3–4 paragraphs**, using your old three-paragraph block as the template and integrating the “candidate appropriate knowledge” cue explicitly. - We check that those are stylistically and conceptually right. - Once those are stable, we graft on the cleaned-up regimes/regularities bit you liked conceptually. But you asked “can you think of a way to get back on track?”, not “rewrite now”, so I will stop here and not dump another full section until you say “go ahead and rewrite para 1–4”. The key change is: we treat your existing text (the quoted block you marked with %%) as the normative standard for how this section should sound and build outward from that, instead of me improvising fresh prose that drifts stylistically and philosophically. Good, this is exactly the “step back” move we need. Let me just lay out, cleanly, what Section 5 is supposed to *do*, what topics it covers, and how it is structured in the most up-to-date plan—without writing any new prose for the paper. --- ### 1\. Function of Section 5 (what it is for) Section 5 has one main job in the paper: - **It proposes a specific candidate for Carlson’s “right kind of knowledge” in the LLM case.** More concretely: - Sections 2–4 have already: - fixed a technical baseline for what LLMs are (Section 2), - rejected *person-directed knowledge* as the appropriate background (Section 3), - and demoted *purely design/functional knowledge* to “part of the story but not enough on its own” (Section 4). - Section 5 now: - brings Carlson’s order-based framework back to the LLM case, - and *puts semiotic physics on the table* as the kind of non-aesthetic, science-like understanding that can guide an order-based appreciation of LLM-mediated chats. So: Section 5 is not about re-arguing 3 and 4. It is the “positive candidate background knowledge” section. --- ### 2\. Topics that Section 5 should cover In the latest plan, Section 5 covers the following topics, in this order: 1. **Re-anchoring in Carlson.** - A short reminder of Carlson’s two recommendations: - appreciate things as what they are, - in the light of the right kind of background knowledge. - A compact recall of his nature case: - environmental sciences (geology, ecology, meteorology) provide a general, non-aesthetic story that makes natural order visible. 2. **Geology vs chemical physics.** - Chemical physics is, in principle, another scientific story about the same cliff. - For Carlson’s purposes, geology is a better *practical* level because its categories (strata, faults, erosion channels) map directly onto what an ordinary viewer can see and use as “ways of looking”. - That motivates a general pattern: for order appreciation we want sciences whose categories connect to what is perceptually available. 3. **Transferring that pattern to LLMs and “appropriate knowledge” candidates.** Here we explicitly line up the options: - **Person-like background knowledge** (biographical, psychological, character-based) has already been considered and rejected in Section 3 as inappropriate for LLMs under Carlson’s first recommendation. - **Design / functional knowledge** (architecture, loss functions, interface, use-cases) has been considered in Section 4 and found partly apt but insufficient for capturing the emergent order in behaviour. - **Mechanistic interpretability (MI) and related low-level work** are explicitly acknowledged: - they are serious scientific projects that trace internal circuits and effects of interventions; - in Carlson’s terms, they are analoguous to chemical physics— *in principle* a science of the object that could ground order appreciation, but whose categories are far from what users perceive when reading outputs. - This motivates looking for a **mid-level, text-facing framework** whose categories line up with what readers actually see in outputs. That is where semiotic physics comes in. 4. **Definition and basic character of semiotic physics.** - Semiotic physics is defined in plain terms as: - a way of describing the regular patterns in how a fixed model tends to extend prompts into text, across many episodes. - It is explicitly pitched as: - mid-level (not down at the circuit level, not up at the person level), - *semiotic* (its units are linguistic signs and their relations), - and *LLM-specific* (it respects the technical baseline from Section 2 but does not re-explain that baseline). 5. **“Semiotic all the way down” and the imprint of textual culture.** - This part emphasises: - the units are linguistic tokens (words, subwords, symbols), - training on vast, heterogeneous corpora means the model has tacit knowledge of: - genre associations, - typical structures of explanations, arguments, narratives, - register and tone in different contexts. - Semiotic physics is explicitly tied to that: it studies patterns that derive from this compressed imprint of “text culture” plus post-training, not just superficial formatting habits. 6. **Regimes and regularities (the substantive core).** - *Regime* is defined as a relatively stable way the model tends to write under certain broad prompt conditions. - We list and briefly characterise the main regimes: - Wikipedia-like expository regime; - therapeutic/advice regime; - “talk”/TED-summary regime; - **reasoning regime** (stepwise chain-of-thought structure); - **neologistic/poetic regime** (as in the bee text); - refusal / safety boilerplate regimes, etc. - Then we organise the *classes of regularities* semiotic physics cares about: - low-level structural habits (lists, hedging, recap sentences, refusal templates); - genre and register preferences; - discursive stance patterns (explanatory, argumentative, narrative, advisory, confessional); - **reasoning regimes** as a key class (organising justification into steps); - hybridisation tendencies (how it blends, say, pop-science and mindfulness language); - neologistic / highly marked regimes (dense coinages, register collisions, stylised outputs, like the bee text). 7. **Semiotic physics as Carlson-style “right kind of knowledge”.** - Here the section reconnects to Carlson in one tight paragraph: - for nature, geology/ecology show how order arises and guide what one looks at; - for LLM chats, semiotic physics tells us: - which kinds of linguistic order are typical for a model, - how they depend on prompts and alignment. - So semiotic physics plays the same structural role in the LLM case that geology and ecology play in the nature case. 8. **Partial knowledge and jailbroken outputs.** - There is a short paragraph that explicitly says: - we often lack full details of training and deployment; - semiotic physics, as used here, works primarily from observable input–output behaviour plus generic knowledge about LLM training; - this is enough even in cases like the bee text, where we only know it comes from a Claude-like model in a safety-relaxed configuration. 9. **Relation to design and person packages; transition.** - Finally, a brief “positioning” paragraph: - design knowledge remains relevant for artefact-level evaluation (efficiency, robustness, safety, UX); - semiotic physics supplements that by focusing on behavioural order; - person-based appreciation remains out; semiotic physics does not re-introduce a subject, it stays at the level of output patterns. - The section ends by explicitly pointing ahead: - Section 6 will now take two episodes—a banal chain-of-thought answer and the bee text—and show how semiotic physics can guide order appreciation in each case. --- ### 3\. How this differs from the bad draft you reacted to Compared with the draft you just tore apart, the *intended* Section 5: - **Starts** with your preferred three-paragraph Carlson block and an explicit statement: “in this section I bring Carlson’s order-based framework back to the LLM case and put forward a candidate appropriate knowledge”. - **Frames** mechanistic interpretability and person-like appreciation as *candidate knowledge packages* in Carlson’s sense, not as “naïve at one end, MI at the other”. - **Defines** semiotic physics in plain, mid-level, text-facing terms, without gratuitous re-introduction of probabilities and sampling. - **Spends its bulk** on regimes and regularities, which is where your positive view actually lives. - **Uses Carlson** only where he should be used (order + knowledge template, automatic poetry), without attributing new “rules” to him. That is the “first principles” picture we should hold onto as we rebuild the prose: Section 5 = introduce semiotic physics as the Carlson-style background knowledge, explain what that amounts to, and set up the examples. Everything else is scaffolding. 1\. "Re-anchoring in Carlson. A short reminder of Carlson’s two recommendations: appreciate things as what they are, in the light of the right kind of background knowledge. A compact recall of his nature case: environmental sciences (geology, ecology, meteorology) provide a general, non-aesthetic story that makes natural order visible." i cannot express how minimal this should be. short doesn't cover it. i also don't know why you are wasting space with three examples. just use geology. 2. In fact, regarding your points 1–3: why don't we just start this section with the paragraphs that I copied into my comments of the draft you gave me of section 5. they are perfect pretty much and should be reproduced pretty much verbatim. any plan you make should continue on from these paragraphs. I hope this is clear. 3. in your draft of section 5, your points 4, 5, and 6 were done extraordinarily badly. it was basically unreadable. it also didn't even seem to say anything. just awful. 4. "Semiotic physics as Carlson-style “right kind of knowledge”." i don't understand why this is a paragraph on its own. why is this idea not intergrated into the sections describing what semiotic physics was. 5. I think it was a terrible mistake to name the view and then explain what it was. it would have been much better to take the reader by the hand and explain why language and the way these things processe linguistic tokens, and then show the reader that if we focus on the semiotic aspects (as we have talked about exhaustively warlier in the chat) this level of understanding seems like a good way of getting at an aspect that is available to the reader of llm text. you need to think much harder about these ideas should be presented to the reader. in your most recent draft you just firehosed a load of jargon (worse, sometimes jargon that had already been explained earlier in the paper). there was no analytic philosophical argument, no building a case. no rational analysis being used. it was just...shit. I would like you to go back to writing me a detailed paragraph by paragraph plan, we are far away (fucking still!) from a decent version of this chapter **Checklist** - Fix what Section 5 is for and which parts of your existing paragraphs are treated as fixed. - Build a new paragraph-by-paragraph plan that **starts from your three “perfect” paragraphs** and continues from there. - Make Carlson re-anchoring minimal (geology only). - Rebuild the argument for semiotic physics from first principles: from language and training, not from jargon. - Integrate “semiotic physics as appropriate knowledge” into the explanatory paragraphs, not as a separate block. - Specify where regimes, reasoning, and bee-style outputs come in, without reusing the bad earlier structure. --- Here is a fresh plan for Section 5, keyed to what you just said. I treat the three paragraphs you pasted in your comments as fixed and work forward from them. I will label paragraphs P1, P2, … for clarity; P1–P3 are *your* existing text, reproduced in structure, and the plan begins from P4. --- ### P1–P3: Your existing Carlson opening (taken as fixed) - **P1**: Orientation sentence + first Carlson paragraph. - Use your own sentence: > “In this section I bring Carlson’s order-based framework back to the LLM case. I first recall how, in natural settings, scientific knowledge makes order visible for appreciation. I then introduce *semiotic physics* as a way of using existing computer science work on LLMs to play a similar role for model outputs, and I close by explaining how this prepares the ground for the extended example that follows.” - Then Carlson’s quote and exposition exactly as in your older text, up to “this guides what we attend to when we appreciate an environment.” - No extra examples beyond geology, ecology, meteorology that are already there. - **P2**: Chemical physics vs geology. - Your “nothing in principle rules out more fundamental sciences here… chemical physics also explains the cliff face…” paragraph, again essentially verbatim, with only minimal smoothing if needed. - It ends with: > “Carlson’s recommendation can be introduced most naturally by starting from sciences whose categories can already function as ways of looking, even if more fundamental levels could in principle support a similar story.” - **P3**: Very short bridging paragraph. - One or two sentences that (a) apply the general pattern to LLMs at a high level, and (b) say explicitly that Section 5 will now offer a candidate “appropriate knowledge” package. - Content: - Recall, in one sentence, that Sections 2–4 have already given an engineering baseline and rejected person-like and pure design packages. - Say something like: “The question now is what kind of non-aesthetic story can make the order of LLM-mediated chats visible in the way geology does for cliffs.” This keeps the Carlson re-anchoring **minimal**: you use geology as your model and you are done. --- ### P4: State the problem in Carlson’s terms for the LLM case **Job:** Make the Carlson-style problem explicit for LLMs and name the need for a new knowledge package, without yet naming it. **Content:** - One sentence: Section 2 gave a basic technical description of LLMs as next-token predictors trained on large corpora and tuned by RLHF; Section 3 and 4 tested person-like and design-like background knowledge and found them wanting for appreciating **behavioural order**. - Then: “By Carlson’s lights, we still owe a general, non-aesthetic account that helps make the order of LLM outputs visible for appreciation.” - Close with a question in declarative form: “I now ask what kind of understanding of LLMs can play, for their textual behaviour, the role that geology plays for natural landscapes.” --- ### P5: Set aside person-directed knowledge and design knowledge explicitly **Job:** Mark explicitly that those candidates are now excluded or demoted, so the reader does not think this section is re-opening them. **Content:** - One sentence on person-directed knowledge: - “Section 3 considered whether the background knowledge appropriate for appreciating persons—biographical and psychological understanding of a life—could be reused for LLMs and argued that this is not a good fit, given their architecture and training.” - One sentence on design/functional knowledge: - “Section 4 treated LLMs as artefacts and used design aesthetics to assess them as tools, but ended by noting that the most important patterns in their behaviour are not directly designed.” - One sentence stating the upshot: - “So neither person-directed knowledge nor purely functional knowledge seems to be the right sort of background for appreciating the order in LLM-mediated chats.” No value-laden language, no “naïve”. --- ### P6: Introduce the MI option as a low-level scientific description (chemical-physics analogue) **Job:** Acknowledge mechanistic interpretability / internal-circuit work as a genuine scientific candidate, while explaining why you will not use it as *your* background level. **Content:** - First sentence: introduce MI and related work neutrally: - “There is another body of work that might seem to fit Carlson’s template: mechanistic interpretability and related studies of internal circuits in LLMs, which track how particular attention heads or neurons contribute to patterns of behaviour and how targeted interventions change outputs.” - Second sentence: state that, in principle, this is a science of the object: - “This kind of research can, in principle, explain why a model answers certain prompts in certain ways by pointing to specific internal structures.” - Third sentence: draw the chemical physics analogy **without** saying “forces”: - “In Carlson’s terms, it is analogous to chemical physics in the natural case: a more fine-grained theory of the same object that does not line up directly with what is available to a non-specialist observer.” - Fourth sentence: state the practical problem: - “Knowing that a particular head implements a certain circuit does not by itself tell a reader how to attend to the structure of a paragraph.” You are not dismissing MI; you are saying “this is deeper than we need for the order-appreciation project aimed at readers of outputs”. --- ### P7: Shift to language as the “material” the reader actually encounters **Job:** Start the positive case from the simple fact that what users see is text, and that any useful background knowledge should match that. **Content:** - First sentence: “By contrast, users of LLMs encounter them as producers of language: they see prompts and textual replies.” - Second sentence: recall, briefly, that Section 2 showed that LLMs are trained on large text corpora and that their behaviour is shaped by regularities in those corpora. - Third sentence: state the key thought: - “If we want an analogue of geology for LLMs, it is natural to look for a level of description that treats the model’s behaviour as a matter of how it tends to extend and reshape language, in a way that readers can recognise across episodes.” Still no label; we are building the intuition: appropriate knowledge should be *semiotic*, because that is what the reader has access to. --- ### P8: Articulate the kind of knowledge you want, before naming it **Job:** Describe the target package—knowledge about patterns in how a model writes—without yet slapping “semiotic physics” on it. **Content:** - First sentence: “The relevant background knowledge would tell us, in general terms, which patterns a model’s outputs tend to follow across prompts.” - Then spell out three components, in plain terms: - how it typically answers different sorts of questions (definition, explanation, advice, narrative); - which styles, registers, and structures it favours in those cases; - how far these tendencies depend on training data and post-training constraints. - Conclude: “Such knowledge is still non-aesthetic and science-like in Carlson’s sense, but it works with the categories that are visible in text rather than with internal circuits.” This paragraph is the conceptual heart: it says what the candidate “appropriate knowledge” *is like* before naming it. --- ### P9: Introduce the label semiotic physics as shorthand **Job:** Now, and only now, give the view a name. **Content:** - First sentence: “For ease of reference I will call this sort of mid-level, text-facing understanding of a model’s behaviour *semiotic physics*.” - Second sentence: short definitional line in your voice: - “By *semiotic physics* I mean general information about how a given LLM tends to use and develop language across prompts, when viewed over many episodes.” - Third sentence: tie to existing work in a footnote (“label borrowed from X, but used here in this restricted way”). No talk of “states, transitions, stochastic dynamical systems”; that is Section 2 / technical background, not here. --- ### P10: Explain the “semiotic” part—tokens, corpora, text culture **Job:** Clarify what makes it semiotic, and bring in the “vast text culture” point properly. **Content:** - First sentence: “The approach is semiotic in that it takes the basic units to be linguistic signs: words, subwords, and symbols in natural languages and related notations.” - Second sentence: “Because the model has been trained on a very large and varied corpus, its behaviour reflects regularities in how these signs are used together in fiction, journalism, academic writing, online discussion, and so on.” - Third sentence: “This includes not only local word associations but also broader patterns in genre, tone, and form—for example, how explanations are normally structured, how advice is phrased, or how certain styles of internet writing look.” - Fourth sentence: “Semiotic physics, as I understand it, collects and organises such facts as a way of explaining the order we see in outputs.” This paragraph gives the reader something concrete to hold onto: they can think of corpora, genres, discourses. --- ### P11: Introduce regimes as the way semiotic physics organises this knowledge **Job:** Bring in regimes, because they are the main structural tool in the later sections. **Content:** - First sentence: define *regime* in one plain line: - “It is useful to say that a model has different *regimes* of behaviour: relatively stable ways it tends to write when given certain broad kinds of prompt.” - Next 3–4 sentences: list and sketch the main regimes, with one example each: - expository/Wikipedia-like regime (neutral, definitional, classificatory); - therapeutic/advice regime (second-person address, validation, coping steps); - talk/summary regime (high-level overviews, “in this talk I will…”); - reasoning regime (stepwise chain-of-thought structure); - neologistic/poetic regime (dense coinages and stylised language around a theme, as in the bee case). - Final sentence: “Semiotic physics describes these regimes and the conditions under which the model tends to move between them.” No flourish, just an organised map. --- ### P12: Spell out classes of regularities semiotic physics cares about **Job:** Make explicit what kinds of patterns semiotic physics records, so Section 6 has hooks. **Content:** - Short opening: “More concretely, semiotic physics tracks several kinds of regularity.” - Then a compact list in prose (not bullet points in the actual paper, but clearly separated sentences): - structural habits (lists, hedging phrases, recap sentences, refusal templates); - genre and register preferences (which styles are used for which prompts); - stance patterns (whether it explains, argues, narrates, advises); - reasoning regimes (how it structures justification into steps); - hybridisation tendencies (how it blends, say, popular science with mindfulness or self-help); - neologistic and highly marked regimes (dense coinages and register clashes in certain prompts). - Close: “These are all patterns in how the model extends given bits of text, seen over many interactions.” --- ### P13: Integrate the Carlson point directly here (no separate “semiotic physics fits Carlson” block) **Job:** Tie back to the “appropriate knowledge” role **inside** the description of semiotic physics, as you requested. **Content:** - First sentence: remind the reader of Carlson’s structural claim in one line: - “Carlson’s second recommendation was that, for nature, such general non-aesthetic knowledge should guide how we look for order.” - Second sentence: “Semiotic physics plays the same structural role for LLM chats: it tells us what kinds of linguistic order a model tends to exhibit and where to expect them.” - Third sentence: “When we read a particular episode in the light of this knowledge, we can see it as a case of a more general regime or as a departure from it, rather than as an isolated performance.” - Fourth sentence: “In that sense, semiotic physics is my candidate for the ‘right kind of knowledge’ for appreciating LLM-mediated chats under an order-based framework.” So the Carlson fit is *integrated* into the explanation instead of being quarantined in a separate paragraph. --- ### P14: Partial-knowledge point (for jailbreaks and unknown variants) **Job:** Keep the methodological caveat but short and clean. **Content:** - First sentence: “In many practical cases we do not know the full details of a model’s training data, alignment procedure, or product configuration.” - Second: “Semiotic physics, as used here, relies mainly on observable input–output behaviour and generic facts about how LLMs are trained.” - Third: “That is enough for the examples in Section 6, including a text produced by a safety-relaxed Claude variant, where the exact configuration is not documented.” Done. --- ### P15: Positioning and transition **Job:** Briefly say how semiotic physics sits alongside design and person approaches, then point to Section 6. **Content:** - One sentence on design: “Design-based knowledge about architectures, objectives, and interfaces still matters for assessing LLMs as artefacts, but semiotic physics shifts attention to their behaviour as producers of text.” - One sentence on persons: “Person-directed knowledge is not the right kind of background for LLMs, and semiotic physics does not reintroduce a subject; it stays at the level of textual patterns.” - Final sentence: “In the next section I use this framework to read two LLM outputs—a routine reasoning-style answer and a more exuberant bee text—as objects of order appreciation.” --- That is the plan I would now commit to: - P1–P3 are your Carlson paragraphs. - P4–P7 build the need for a semiotic, text-facing knowledge package. - P8–P12 unpack that package step by step, then name it. - P13 integrates the Carlson “right kind of knowledge” role directly into that unpacking. - P14–P15 handle the jailbroken-case caveat and the transition. No labels-then-definition, no MI-bashing, no extra Carlson paras, no analogies beyond the minimal geology case you already like. "Third sentence: tie to existing work in a footnote (“label borrowed from X, but used here in this restricted way”)." nah, put the details about janus and the others and picca too in the body text around here. Keep it succinct, jsut make it clear what we have taken/have in common with these views. "Bring in regimes, because they are the main structural tool in the later sections." was regimes your invention/label? it seems very vague. if it is a real term, you need to be explicitly referencing where the idea originally comnes from, or at least some sort of foundational text that mentions it (not anly old academic reference to it will do, of course) "P12: Spell out classes of regularities semiotic physics cares about Job: Make explicit what kinds of patterns semiotic physics records, so Section 6 has hooks. Content: Short opening: “More concretely, semiotic physics tracks several kinds of regularity.” Then a compact list in prose (not bullet points in the actual paper, but clearly separated sentences): structural habits (lists, hedging phrases, recap sentences, refusal templates); genre and register preferences (which styles are used for which prompts); stance patterns (whether it explains, argues, narrates, advises); reasoning regimes (how it structures justification into steps); hybridisation tendencies (how it blends, say, popular science with mindfulness or self-help);" i feel like you are falling far tooo much into the 'aesthetics of bullet points' that I warned you against. Good, this is exactly the level of brutality we need. Let’s reset the plan properly. I’ll treat *your* Carlson opening paragraphs as fixed, and build a new paragraph-by-paragraph plan that: - does not start with the name “semiotic physics” and then try to justify it, - builds the case from language and training upward, - integrates Janus + Picca in the body, - drops or softens jargon like “regime” unless anchored, - avoids “aesthetics of bullet points”. I’ll label paragraphs P1, P2, …; P1–P3 are your own text. --- ### P1–P3: Your Carlson block (fixed) These are the paragraphs you pasted in your comments. They: - state that you are bringing Carlson’s order-based framework back to LLMs; - recall, in Carlson’s own terms, that appropriate appreciation of nature depends on a general non-aesthetic story that makes order visible; - use geology vs chemical physics to illustrate why a mid-level science whose categories match what we can see is the most natural starting point. We do *not* change their content; they are the anchor. --- ### P4: Bridge Carlson → LLM problem, in his vocabulary **Job:** Move from nature to LLMs and state the Carlson-style problem *very* succinctly. **Content:** - One sentence: Section 2 already gave a general, non-aesthetic story about what LLMs are (token-based models trained on large corpora and tuned by RLHF). - One sentence: Sections 3–4 showed that neither person-based background knowledge nor pure design/functional knowledge look like the right kind of knowledge for appreciating LLM behaviour, in Carlson’s sense. - One sentence: pose the problem in Carlson’s frame: we still need “a general, non-aesthetic story” that helps make the order in LLM-mediated chats visible and that can guide what we attend to when we read them. No “naïve” talk, no MI yet, no new labels. --- ### P5: State explicit desiderata for “appropriate knowledge” in the LLM case **Job:** Fix what any candidate background knowledge must do, before we argue for your specific one. **Content:** - Short list in prose (not bullets in the paper, but three clear sentences): - It must be **non-aesthetic and truth-apt**: a descriptive account of how LLMs behave, not an evaluative gloss. - It must be about **processes that generate order** in outputs (analogous to how geology is about processes that generate landforms). - It must be **usable by a reader of LLM text** as a way of looking; its categories should map onto features that are available in the textual outputs themselves, rather than only onto hidden internal states. - One final sentence: “In what follows I look for such a body of knowledge.” This is the analytic move that was missing: we say what “appropriate knowledge” has to be like before we propose our candidate. --- ### P6: Clear away the other candidates (person, design, MI) as knowledge packages **Job:** Summarise why the obvious candidates do not meet the desiderata just stated, without re-arguing Sections 3–4, and bring MI in as a third case. **Content:** - One sentence on person-directed knowledge: it fails condition (i)/(ii), because it presupposes a temporally extended life with projects and character; Section 3 already argued that LLMs do not give us that kind of object. - One sentence on design/functional knowledge: it is still important for artefact evaluation, but, as Section 4 showed, much of the order in behaviour is not directly designed, so it is too thin for (ii). - One sentence on mechanistic interpretability and causal intervention work: - acknowledge that this is a serious low-level “science of the object” that can, in principle, explain behaviour in terms of internal circuits and interventions; - point out that it fails condition (iii) for most readers, because its categories (heads, neurons, circuits) do not line up with what one sees in a piece of text. - One short closing sentence: “I therefore look for a description that is non-aesthetic, process-focused, and text-facing.” This paragraph keeps MI’s status as a *possible* Carlson-style science, but explains why you are not using it as your aesthetic background. --- ### P7: Start from language: what users actually see and what Section 2 already told us **Job:** Take the reader by the hand from “LLMs as producers of language” to “let’s focus on the semiotic level”. **Content:** - First sentence: “Users encounter LLMs through their language: through sequences of words and sentences in prompts and replies.” - Second sentence: recall, in one line, that Section 2 described how LLMs are trained on large text corpora to predict the next token, and that their post-training shapes which kinds of text they tend to produce. - Third sentence: “So if there is a non-aesthetic story that can guide our appreciation of LLM outputs, it should be keyed to the way these systems handle language, rather than to hidden internal variables.” No new terminology, no “semiotic physics” yet. This is the first-principles move you asked for. --- ### P8: Describe the target kind of knowledge in plain terms (still unnamed) **Job:** Explain the shape of the knowledge you want—roughly what semiotic physics will be—without naming it yet, avoiding bullet-point vibes. **Content:** - One sentence: “Roughly, we want general information about how a given model tends to continue and reshape language across many prompts.” - Then 3–4 sentences fleshing this out as *three threads*: - Patterns in **what** forms of writing it tends to default to in different settings (encyclopaedic explanation, conversational advice, technical summary, narrative, etc.). - Patterns in **how** it organises material in those forms (for instance, the typical structure of an explanation, a piece of reasoning, or a piece of poetic language). - Patterns in **which parts of its training culture** it tends to draw on for a given kind of request (e.g. textbook habits vs online forum habits vs therapeutic discourse). - One sentence tying back to the desiderata: “Such information is non-evaluative, it concerns regular ways in which outputs are produced, and its categories—genres, styles, recurrent forms—match what a reader can pick out in the text.” This is your positive argument: we define the shape of the knowledge package that would satisfy Carlson’s conditions for LLM texts. --- ### P9: Introduce semiotic physics and place it relative to Janus and Picca **Job:** Now give the package a name and situate it explicitly in relation to the semiotic-physics texts and Picca’s “Not Minds, but Signs”. **Content:** - First sentence: “I will call this kind of mid-level, text-facing description of a model’s behaviour *semiotic physics*.” - Second sentence: one-line definition in your dry style, referring back to P8, not to implementation: - “By *semiotic physics* I mean the general study of the patterns just described: how a fixed LLM tends, across prompts, to extend and reshape linguistic material.” - Third/fourth sentences: integrate Janus et al. and Picca in the *body*: - One sentence: “The label comes from recent work that presents LLMs as dynamical systems over strings, emphasising trajectories of text rather than inner states (Janus and collaborators).” - One sentence: “It is also close in spirit to Picca’s idea of LLMs as semiotic machines that manipulate and circulate signs rather than host mental states.” - Last sentence: “My use of the term is narrower and more modest: I focus on semiotic facts that can guide aesthetic attention, rather than on safety, agency, or semantic metaphysics.” So the reader sees immediately what you share with those views: focus on trajectories of signs, not inner minds. --- ### P10: Explain the “semiotic” part with textual culture, not with probabilities **Job:** Spell out what is “semiotic all the way through” in a natural way, using your earlier “vast corpus” thoughts, without going back into tool-implementation. **Content:** - One sentence: “The approach is semiotic in the simple sense that it treats the basic units as items of language—words, subwords, and symbols—and looks at how the model has learnt to use them together.” - One or two sentences on the training corpus: - stress that the corpus contains many discourses: scientific, journalistic, literary, legal, conversational, online, etc. - One or two sentences on what the model has picked up: - common forms of explanation, common argumentative templates, narrative rhythms, rhetorical habits from different domains. - One sentence: “Semiotic physics organises these inherited patterns into a usable shape, so that we can say, for example, that this model tends to answer legal questions in a certain register, or tends to blend popular science and self-help language when describing the mind.” No mention of probabilities or sampling here. This paragraph is purely about the cultural-linguistic side. --- ### P11: Introduce patterns/modes of response (instead of heavy “regime” jargon) You are rightly suspicious of “regime” as a quasi-technical term. To avoid vagueness and citation headaches, we can use “patterns” or “modes of response” in the actual prose, and treat “regime” as at most a parenthetical gloss (“pattern or regime of response”) the first time. **Job:** Give a structured but non-bullet-point sketch of the main patterns that semiotic physics will care about, without making it feel like an “aesthetics of bullet points”. **Content:** - First sentence: “For a given model, we can describe its behaviour in terms of recurring modes of response to broad kinds of prompt.” - Then 3–4 sentences, each about one *type* of mode, with *one* example folded in, but not as a list: - A sentence on expository modes (“when asked to define or explain, the model tends to adopt a neutral, encyclopaedic style, with characteristic sentence shapes and paragraph structures…”). - A sentence on advisory/therapeutic modes (“when asked for help or reassurance, it tends to speak in the second person, to validate feelings, and to offer numbered strategies…”). - A sentence on reasoning modes (“when asked to ‘think step by step’, it tends to segment its answer into labelled steps, use stock connectors, and move through familiar kinds of example and recap…”). - A sentence on more stylised/poetic modes (“when prompted for playful or ecstatic writing, it sometimes shifts into dense neologisms, hybrid registers, and thematic fixations, as the bee text will illustrate”). - Final sentence: “Semiotic physics is, in part, the task of describing these modes of response and the conditions under which a model tends to move from one to another.” This gives Section 6 all the hooks it needs (especially for reasoning and bee), without burying the reader in micro-features. --- ### P12: Describe the kind of regularities semiotic physics tracks, without turning it into a list of surface tics You are right that “lists, hedges, refusals” as a headline risks an aesthetics of bullet points. Here we shift the emphasis to *structural* regularities. **Job:** Explicitly say what sorts of regularities matter, but frame them as aspects of structure and organisation, not as a laundry list of quirks. **Content:** - Opening sentence: “In more structural terms, semiotic physics tracks regularities in three main dimensions of a model’s writing.” - Then three sentences, each on a dimension, not rolled out as bullet points: 1. **Form:** “First, it concerns regularities of form: how replies tend to be divided into sections or steps, how arguments and explanations are usually ordered, and where repetitions or recaps typically occur.” 2. **Voice:** “Second, it concerns regularities of voice and stance: whether the model tends to explain, argue, narrate, advise, or confess in response to certain prompts, and how it mixes these stances.” 3. **Source culture:** “Third, it concerns which parts of its training culture the model tends to draw on in a given case—for instance, whether it leans more on textbook conventions, online forum habits, or experimental and poetic styles.” - One closing sentence: “Later, when I look at a reasoning-style answer and at the bee text, I will take them as short trajectories within such patterns, not as isolated performances.” This keeps things at the level of organising principles, not bullet points like “look, it uses numbered lists”. --- ### P13: Integrate the Carlson “right kind of knowledge” role directly here You did not want a standalone block saying “semiotic physics is Carlson-style knowledge”. So we weave it into this paragraph. **Job:** Close the explanatory part by stating, explicitly but concisely, that semiotic physics fits the Carlson template. **Content:** - One sentence: “In Carlson’s terms, semiotic physics supplies the kind of non-aesthetic story that can make the order of LLM outputs visible for appreciation: it explains how they are shaped by training, text culture, and alignment.” - One sentence: “Because its categories—forms of writing, stances, and source discourses—match features that readers can pick out, it can structure acts of aspection in the way geology does for cliffs.” - One sentence: “It is, therefore, my candidate for the ‘right kind of knowledge’ for appreciating LLM-mediated chats under an order-based framework.” No separate paragraph for this; this is the end of the explanatory run. --- ### P14: Partial-knowledge caveat (kept brief) **Job:** Keep the methodological point about not knowing full Anthropic configs, etc., but short. **Content:** - One sentence: “In practice, users often lack detailed information about a model’s training data and alignment procedure.” - One sentence: “Semiotic physics, as I use the term, relies mainly on patterns evident in ordinary interaction, plus generic facts about how LLMs are built.” - One sentence: “That is enough for my examples, including a text produced by a safety-relaxed Claude variant, where the exact configuration is not documented.” --- ### P15: Positioning and transition to Section 6 **Job:** Briefly remind how this sits with design/person approaches and point to the reasoning + bee examples. **Content:** - One sentence on design: “Design-level knowledge about architectures and objectives remains relevant for evaluating LLMs as artefacts, but semiotic physics shifts focus to their behaviour as producers of text.” - One sentence on persons: “Person-directed knowledge does not fit LLMs, and semiotic physics does not reintroduce a subject; it stays with patterns of language use.” - Final sentence: “In Section 6 I use this framework to read two outputs—a routine reasoning-style answer and an exuberant bee text—as objects of order appreciation.” --- That is the updated plan, incorporating your latest points: - Carlson rehash is tiny and uses geology only (already in your own paragraphs). - The section *builds an argument* from language and training to a semiotic, text-facing “appropriate knowledge” package, and *then* names it. - Janus + Picca are explicitly mentioned where the label is introduced, with a clear statement of what is shared. - “Regime” is not used as a freestanding technical term; instead we talk about “modes of response” and “structural regularities”. - The list of regularities is reframed around form, voice, and source culture, not around bullet-point behaviours. Right, you’re absolutely right to chase that, and I did skip it before. Let me answer it cleanly. ### Where “regime” came from in our discussion Short answer: in our thread, *“regime” was my word*, not a term you had introduced or anchored in a specific literature. A bit more precisely: - In the **semiotic-physics papers** you shared, the authors do talk about: - attractors / absorbing sequences, - different “behavioural regimes” or “chaotic regimes” in a loose sense, - and trajectories moving between them. - In **dynamical systems / statistical physics** more generally, “regime” is a very generic word: people say “turbulent regime”, “laminar regime”, “high-temperature regime”. It just means “a qualitatively different pattern of behaviour under a different set of conditions”. I imported that generic usage when I started talking about “reasoning regime”, “neologistic regime”, etc. That was *my* attempt to give a short label for “a recurring mode of response under broadly similar prompts”. There are, as far as I know: - no canonical philosophical or aesthetic texts that use *regime* as a technical term specifically for patterns of LLM text; - and nothing in Janus or Picca that would let you say “this is the *Regime Theory* and here is the standard reference”. So if we treat “regime” as a technical term in the paper, we either: - need to **own it as your terminology** (“By a *regime* I mean…”), or - we should **avoid it** and stick with plainer, better grounded terms like “modes of response”, “patterns of behaviour”, “styles of answer”. Given your worries: - that it sounds vague; - that it risks looking like unanchored jargon; - and that it can slide into the “aesthetics of bullet points” trap, I think the right move is: 1. **Do not introduce “regime” as a capitalised, free-standing technical term.** If the word appears at all, it can be parenthetical, as a colloquial gloss: “recurring mode (or regime) of response”. But we don’t need it. 2. **Use plain phrases in the main text:** - “recurring modes of response”, - “styles of reply”, - “patterns in how the model tends to answer this kind of prompt”. 3. **If you later decide you like “regime” after all**, we define it ourselves, very minimally, and treat it as a *local stipulation*, not as something borrowed from a deep theory: - “By a *regime* I mean a recurring way in which the model tends to write under a given broad type of prompt (for example, X, Y, Z).” - And we do not pretend it is a term of art from someone else. In the updated plan I just gave, I already pivoted to “modes of response” and “structural regularities”, precisely to avoid anchoring the section on a term that (a) isn’t really rooted in your sources, and (b) has dynamical-systems baggage you don’t want to import. So: “regime” was my generic dynamical-systems habit sneaking in. It isn’t required for your argument, and we can safely drop it or demote it to informal language unless you decide you want to define it yourself. Good, this is exactly the kind of hygiene we should do before touching the prose again. Short answer: yes, apart from *regime* there are a few other little phrases I introduced that either (a) have dynamical-systems baggage, or (b) are just my own shorthand. None of them are essential, and we should either drop them or replace them with plain talk in the actual draft. Here is an audit. --- ### 1\. Terms we should avoid altogether These are basically in the same boat as *regime* – not anchored in your sources, and not needed. - **“regime”, “reasoning regime”, “neologistic-ecstatic regime”** You already said you want these gone, and I agree. They came from my generic dynamical-systems habit, not from any LLM/aesthetics text you are using. We can always say “recurring way the model writes in this sort of case” instead. - **“semiotic all the way down”** This was a conversational slogan we used to get on the same page. It reads as cutesy and is not something you would write. In the paper this should be unpacked into a literal sentence: “I treat the basic units as linguistic signs and look at how the model has learnt to use them together”. - **“semiotic dynamics” / “semiotic order”** when used as if they were technical labels In the chat I used these to gesture at “patterns in how signs evolve over a sequence”. In the paper, we can say “patterns in how the text develops” / “patterns in how the model tends to extend a prompt”. No need for a special label. - **“banal test case”, “baroque regime”** Fine in conversation, but too cute in the draft. In the paper we can just say “a routine reasoning-style answer” and “a highly stylised bee text”. - **“text mechanics”** We have already retired this in favour of “semiotic physics”. It should not appear in Section 5–6 anymore (except perhaps in a single retrospective note if you want to mention that you previously used that label). So: *regime* and all its friends, “semiotic all the way down”, “semiotic dynamics/order” as quasi-terms, “banal/baroque” as labels, and “text mechanics” should be expunged from any draft. --- ### 2\. Terms that are borrowed but need to be handled carefully These are not my inventions, but they can feel like jargon if not introduced properly. - **“trajectory”** This is used in the Janus/semiotic-physics texts for sequences of tokens over time. It is also standard in stochastic-process talk. If we use it, we should: - only use it when we are explicitly drawing on that literature, - define it once (“a *trajectory* is just the whole prompt-plus-generated-so-far string, viewed as it grows over turns”), - and then not lean on it as a central term in the philosophical argument. You may decide to drop it entirely from Section 5 and keep it for a technical footnote. - **“semiotic physics”** This label is not mine; it comes from Janus et al. and similar work. You *do* want to keep it, but: - it needs a crisp, non-technical definition in your voice; - and an explicit, succinct comparison to Janus and Picca in the body (what you share: focus on signs / trajectories; what you are doing differently: Carlson-order / aesthetics, mid-level, text-facing). If we do that, these two are safe; they are not free-floating neologisms. --- ### 3\. Plain phrases I have used that are not problematic Just to separate the wheat: - **“modes of response”, “patterns of behaviour”, “styles of reply”** These are ordinary English. They do not claim technical status and can stay as-is. - **“appropriate knowledge”, “background knowledge package”** Again, these are just ways of talking about Carlson’s second recommendation plus the associated sciences; they are not terms of art. So I do not think we need to purge those; they are already in the register you use in your draft. --- ### 4\. How this affects the plan Going forward: - We will not use *regime* anywhere in Section 5–6. We will talk instead about: > “recurring modes of response to certain kinds of prompt”, > “regular ways the model tends to write in those cases”, > “patterns in how it typically structures explanations / reasoning / stylised writing”. - We will not use in-paper slogans like “semiotic all the way down”. We will cash them out in literal language about signs, corpora, genres, and so on. - We will keep “semiotic physics” and, if needed, “trajectory”, but we will: - define them once, plainly, - and avoid building further jargon on top of them. When we next revise the Section 5 plan, I will treat any of those now-banned little terms as toxic: if I see one in the plan or draft, I will rewrite that bit around plain phrases instead. **Checklist** - Treat your existing Carlson opening paragraphs as fixed starting point. - Build a new paragraph-by-paragraph plan from there, with *minimal* Carlson recap. - Motivate a semiotic, text-facing level before naming “semiotic physics”. - Integrate Janus-style semiotic physics and Picca’s semiotic machine view in the body, succinctly. - Avoid “regime” and similar pseudo-technical labels; use plain talk about patterns/modes of response. - Frame the regularities in structural terms, not as a list of micro-tics. --- Here is a new plan for Section 5, with paragraph labels for ease of reference. P1–P2 are your own paragraphs (Carlson + geology/chemistry) used as-is. I start from P3. ### P1–P2: Your Carlson block (unchanged) - **P1**: Your orientation sentence (“In this section I bring Carlson’s order-based framework back to the LLM case…”) plus the Carlson quote and explanation: appropriate appreciation of nature depends on a general, non-aesthetic story (Carlson, 2000, p. 119), with geology / ecology / meteorology as examples and the “order made visible” point. - **P2**: Your chemical physics vs geology paragraph: “nothing in principle rules out more fundamental sciences… chemical physics also explains the cliff face…”; geology is the clearer starting point because its categories can be used as ways of looking; chemical physics is less directly usable for lay aspection. I am treating these as fixed; everything else slots in after them. --- ### P3: Bridge from nature to LLMs and restate the open Carlson-style problem **Job:** Move from the natural case to the LLM case and state, in Carlson’s vocabulary, what Section 5 needs to supply. **Content:** - One sentence: point back to Section 2–4 in a single breath: Section 2 gave a general, non-aesthetic story about what LLMs are; Sections 3 and 4 showed that person-based and pure design-based packages do not give us the right kind of knowledge for appreciating their behaviour. - One sentence: restate the open question in Carlson’s terms: we still lack a “general nonaesthetic and nonartistic story” that helps make the order in LLM-mediated chats visible and that readers can actually use. - One sentence: state that Section 5 will now look for such a story. No elaboration, no extra Carlson; this is just the hinge. --- ### P4: Make the desiderata for “appropriate knowledge” explicit **Job:** Set simple constraints any candidate knowledge must meet, before arguing for your own. **Content:** - Short sequence of sentences (in prose, not bullets) along these lines: - It must be non-aesthetic and truth-apt: a descriptive account of how LLMs produce their outputs, not itself an evaluative stance. - It must concern processes that generate order in outputs (in the way geology concerns processes that generate landforms). - It must be usable by a reader of LLM text: its categories should map onto features visible in the outputs themselves, not only onto hidden internal states. - One closing sentence: “I now look for a body of background knowledge that meets these conditions for LLM-mediated chats.” This is where the “analytic argument” starts: we state criteria before proposing “semiotic physics”. --- ### P5: Rule out (or demote) the obvious candidates as knowledge packages **Job:** Summarise why person-directed knowledge, pure design knowledge, and mechanistic interpretability do not meet the desiderata, without re-arguing earlier sections. **Content:** - One sentence on person-directed knowledge: say that Section 3 showed that the kind of background knowledge we use for appreciating persons (biographical, psychological, life-narrative) presupposes a temporally extended subject and does not fit LLMs as described in Section 2. - One sentence on design/functional knowledge: say that Section 4 used design aesthetics to evaluate LLMs as artefacts but argued that much of the order in their behaviour is not directly designed, so functional knowledge alone does not capture what we need for (ii). - Two sentences on mechanistic interpretability and causal studies: - First: acknowledge them as serious low-level sciences of LLMs that can, in principle, explain behaviour by citing internal circuits and the effects of interventions. - Second: state that, for Carlson’s third condition, they are analogous to chemical physics in the natural case: their categories (heads, neurons, circuits) do not map straightforwardly onto features that a reader can pick out in a piece of text. - One sentence summing up: “Person-based, functional, and mechanistic packages each fail one or more of the conditions just stated, so I turn to a description that takes language itself as the material.” Tone: neutral, no “naïve”, no MI-bashing. --- ### P6: Start from what users see: LLMs as producers of language **Job:** Take the reader by the hand from the mundane fact of language use to the idea of a semiotic level of description. **Content:** - One sentence: “Users encounter LLMs through language: they enter prompts and read replies.” - One sentence: remind briefly that Section 2 described LLMs as systems trained on large text corpora to predict the next token and further tuned to produce certain kinds of text more often than others. - One sentence: “So if there is a Carlson-style background story that can make the order of their behaviour visible, it is natural to look for it at the level of how they handle language, rather than at the level of hidden activations.” Still no “semiotic physics” name; this is motivational. --- ### P7: Describe the target kind of knowledge in plain terms (still unnamed) **Job:** Spell out what sort of information about language use would satisfy the three Carlson conditions. **Content:** - First sentence: “Roughly, what we want is general information about how a given model tends to continue and reshape language across many prompts.” - Then three sentences, each on one dimension: - One on what *forms* of writing it tends to use in response to different prompt types (encyclopaedic explanation, short conversational answer, advisory text, narrative, etc.). - One on how it tends to *organise* material in those forms over the course of a reply (for example, common structures for explanations, arguments, or stories). - One on which parts of its training culture it tends to draw on in a given case (for instance, whether its replies on a topic usually sound more like textbooks, news articles, online forums, or experimental writing). - One final sentence tying back to P4: “Knowledge of this sort is non-aesthetic, it concerns the processes that give replies their shape, and its categories match what a reader can pick out in the text.” We are still describing the shape of the knowledge package, not naming it. --- ### P8: Introduce the label semiotic physics and connect it to Janus and Picca **Job:** Now give the package a name and situate it relative to the semiotic-physics literature and Picca’s semiotic machine view, in the body text. **Content:** - First sentence: “For ease of reference I will call this kind of mid-level, text-facing description of a model’s behaviour *semiotic physics*.” - Second sentence: one-sentence definition that points back to P7, not to low-level implementation: - “By *semiotic physics* I mean the general study of the patterns just described: how a fixed LLM tends, across prompts, to extend and reshape linguistic material.” - Third sentence: connect to Janus/semiotic-physics: - “The label comes from recent work that presents LLMs as systems that generate trajectories of text under a fixed update rule, and that focuses on those trajectories rather than on inner mental states (Janus and collaborators).” - Fourth sentence: connect to Picca: - “It is also close in spirit to Picca’s view of LLMs as semiotic machines that manipulate and circulate signs without hosting beliefs or intentions.” - Fifth sentence: state your own focus: - “Here I use the term in a narrower way, to pick out the semiotic facts that can guide aesthetic attention to model outputs, rather than to address questions about agency or semantics.” This keeps Janus and Picca in the body, with a clear statement of what you share with them. --- ### P9: Explain the “semiotic” part via textual culture, not via probabilities **Job:** Clarify what is meant by “semiotic” here: units, corpora, text culture, not stochastic jargon. **Content:** - First sentence: “The approach is semiotic in a simple sense: it takes the basic units to be items of language and asks how the model has learnt to use them together.” - Second sentence: state that the training data include many discourses—scientific, journalistic, literary, legal, conversational, online—and that the model has been exposed to their characteristic ways of writing. - Third sentence: say that, as a result, the model has absorbed regularities not only in which words tend to co-occur, but also in how whole stretches of text are structured in different domains (for example, the typical shape of a proof, a news report, a help-forum answer, a poem). - Fourth sentence: “Semiotic physics organises these inherited regularities so that we can talk in a controlled way about how a given model tends to write in different contexts.” No “trajectory”, “state”, “transition rule” here; this is about the cultural/linguistic imprint. --- ### P10: Introduce recurring “modes of response” (instead of “regimes”) **Job:** Give the structural scaffolding (patterns you will use in Section 6) without using “regime” as jargon or lapsing into bullet-point aesthetics. **Content:** - First sentence: “For a given model, we can speak of recurring modes of response to broad kinds of prompt.” - Then 3–4 sentences, each focused on one type, folded into prose: - One sentence on expository mode: how, when asked to define or explain, the model typically uses a neutral, encyclopaedic style with recognisable patterns of sentence and paragraph organisation. - One sentence on advisory/therapeutic mode: how, when asked for help or reassurance, it tends to address the user directly, to name emotions, and to offer a small number of steps or strategies. - One sentence on reasoning mode: how, when prompted to “think step by step”, it tends to segment the answer into labelled steps, use stock connectors (“first”, “next”, “on the other hand”), and move through familiar intermediate claims and examples. - One sentence on more stylised/poetic mode: how, under prompts that invite play or ecstatic language, it sometimes switches into dense coinages, mixed registers, and thematic fixations, as the bee text will illustrate. - Final sentence: “Semiotic physics describes these modes of response and the conditions under which a model tends to move between them.” No jargon, no bullets; just structured prose. --- ### P11: Describe the structural regularities semiotic physics tracks (form, voice, source culture) **Job:** Say what kinds of regularities matter, but at a structural level, not “lists, hedges, bullets”. **Content:** - Opening sentence: “In more structural terms, semiotic physics tracks regularities along three dimensions of a model’s writing.” - Then three sentences: 1. **Form:** “First, it describes regularities of form: how replies are usually segmented into parts, how explanations and arguments are ordered, and where repetition or recaps tend to occur.” 2. **Voice/stance:** “Second, it describes regularities of voice and stance: which prompts tend to elicit explanation rather than argument, narrative rather than advice, and how these stances are sometimes blended.” 3. **Source culture:** “Third, it describes which strands of the training culture a model tends to draw on in a given case—for instance, whether it answers questions about law in a textbook-like way or in an online-forum style, or whether its more poetic outputs draw on experimental literature, internet slang, or both.” - Closing sentence: “In the next section I treat a banal reasoning-style answer and a more baroque bee text as short samples of such patterned behaviour.” This avoids the “aesthetics of bullet points” problem: the emphasis is on structural organisation, not on particular surface quirks. --- ### P12: Integrate the Carlson “right kind of knowledge” point directly here **Job:** Make the Carlson fit explicit inside the explanation, not as a separate, late block. **Content:** - One sentence: “If Carlson is right that appropriate appreciation of nature depends on scientific knowledge that makes natural order visible, then an analogous role in the LLM case is played by semiotic physics.” - One sentence: “It offers a non-aesthetic account of how LLM outputs acquire their form and voice, and its categories—forms of writing, stances, and source discourses—match what readers can detect.” - One sentence: “Semiotic physics is therefore my candidate for the ‘right kind of knowledge’ for appreciating LLM-mediated chats under an order-based framework.” Short, direct, and part of the explanatory run. --- ### P13: Partial-knowledge caveat (for jailbroken/unknown variants) **Job:** Keep the methodological point about not needing full Anthropic docs. **Content:** - One sentence: “In practice we often lack detailed information about a model’s training data and alignment procedure.” - One sentence: “Semiotic physics, as I use the term, relies mainly on patterns that show up in ordinary interaction, combined with generic facts about how LLMs are built.” - One sentence: “That is enough for the examples in Section 6, including a text produced by a safety-relaxed Claude variant, where the exact configuration is not publicly documented.” --- ### P14: Positioning and transition to Section 6 **Job:** Briefly locate semiotic physics alongside design/person approaches and signal the move to the “banal test case” and “baroque” examples. **Content:** - One sentence on design: “Design-based knowledge about architectures, goals, and interfaces still matters for evaluating LLMs as artefacts, but semiotic physics shifts the focus to their behaviour as producers of text.” - One sentence on persons: “Person-directed knowledge is not the right kind of background for LLMs, and semiotic physics does not reintroduce a subject; it stays with patterns of language use.” - Final sentence: “In Section 6 I use this framework to read two outputs—a banal reasoning-style answer and a more baroque bee text—as objects of order appreciation.” --- That is the new plan, with: - your Carlson opening untouched; - minimal geology-only recap; - a proper analytic build from language/training to semiotic appropriate knowledge; - Janus and Picca placed where the label is introduced; - no “regime”, no “semiotic all the way down”, no aesthetics of bullet points; - “banal” and “baroque” saved for the narrative in Section 6, where they belong. "One sentence summing up: “Person-based, functional, and mechanistic packages each fail one or more of the conditions just stated, so I turn to a description that takes language itself as the material.”" nope. this is wrong, again. when chemical physics is mentioned in the opening paragraphs it is explicitly mentioned that there is no reason to think that an aesthetics based in this dicsipline would not be possible, the same is true for MI. your plan as it stands is saying that there is a mark against them or something like that. THIS IS INCORRECT AND HAS BEEN POINTED OUT QUITE A FEW TIMES NOW "P4: Make the desiderata for “appropriate knowledge” explicit" is this not included already in the openning paragraphs. Even if it isn't you are still writing far too many filler paragraphs. get to the fucking point. "P5: Rule out (or demote) the obvious candidates as knowledge packages" again, why would you put this here. you really don't think you are just repeating information that has already been presented, or presenting it with absolute minimimal additions. So then we get to paragraph 6, before ANYTHING INTERESTING HAPPENS IN THE SECTION AT ALL THEN THE SEMIOTIC PHYSCIS STUFF BEGINS AND I AM BORED OUT OF MY MIND, YOU SEEMED TO HAVE FORGOTTEN ALL OF THE MORE PASSIONATE INTERESTING STUFF ABOUT LLMS AND THE COPRA THAT THEY ARE TRAINED ON. DO YOU REMEMBER WHAT I AM TALKING ABOUT?"P5: Rule out (or demote) the obvious candidates as knowledge packages" Good, this is helpful. Let’s strip this back and rebuild the plan so it actually reflects what you want, not my previous flailing. I’ll treat your Carlson opening as fixed (the three paragraphs you pasted into the comments), and I will *not* add any more abstract Carlson filler. I’ll label new paragraphs from P3 onwards, but remember P1–P2 are your own text. --- ### P1–P2: Your Carlson paragraphs (fixed) - P1: “In this section I bring Carlson’s order-based framework back to the LLM case…” plus Carlson’s quote and the geology / ecology / meteorology examples, ending with “this guides what we attend to when we appreciate an environment.” - P2: Chemical physics vs geology: “nothing in principle rules out more fundamental sciences… chemical physics also explains the cliff face…” and the point that geology is the easier place to explain *aspection* because its categories match what we can see. Those already encode the core desideratum: a non-aesthetic science whose concepts can function as ways of looking. --- ### P3: One-para bridge from nature to LLMs **Job:** Move from cliffs to chats in Carlson’s vocabulary, with zero fluff. **Content:** - One sentence: recall that Section 2 gave a general, non-aesthetic story about what LLMs are (next-token predictors trained on large text corpora, then post-trained). - One sentence: recall that Sections 3–4 showed that, for appreciating *behavioural* order, person-based and pure design-based approaches are not enough. - One sentence: state the Carlson-style question: what kind of non-aesthetic understanding can make the order of LLM-mediated chats visible for appreciation, in something like the way geology does for cliffs? That’s it. No desiderata paragraph; the “geology vs chemical physics” bit has already done that work implicitly. --- ### P4: Start from corpora and “text culture” – LLMs as compressed language history **Job:** Get straight to the “passionate interesting stuff”: the fact that LLMs are trained on a huge amount of heterogeneous language and that their outputs are recombinations of that. **Content:** - First sentence: “Section 2 already stressed that contemporary LLMs are trained on vast, heterogeneous text corpora: fiction, journalism, academic writing, technical manuals, legal documents, self-help, online forums, and much else.” - Second sentence: “As a result, the model carries a compressed record of many ways of writing in the language, from textbook proofs to Reddit arguments to experimental poetry.” - Third sentence: “When we look at its replies, we are not seeing a neutral ‘voice’ laid over facts, but a particular way of re-using and reorganising this inherited text culture.” - Fourth sentence: “If we want an analogue of geology for LLMs, we should therefore look for a description that treats their outputs as ordered re-workings of this linguistic material.” So the “material” is language from the corpus, not activations; and that is where the order comes from. --- ### P5: Characterise, in plain terms, the kind of description we want (still unnamed) **Job:** Say what semiotic physics is *about* before naming it: not circuits; not functional roles; but patterns in how a model writes across prompts. **Content:** - First sentence: “The sort of understanding I have in mind does not track individual weights or hardware details; it says, in general terms, how a given model tends to write.” - Then 3–4 sentences fleshing that out: - One on *forms*: e.g. how it usually structures explanations, chains of reasoning, narratives, advice, etc. - One on *voices*: e.g. when it slips into encyclopaedic, journalistic, conversational, therapeutic, or ecstatic registers. - One on *selection from text culture*: e.g. whether, on a given topic, it mostly sounds like Wikipedia, a help forum, a textbook, or some mixture. - Final sentence: “This is a non-aesthetic description of processes that give replies their shape, and its concepts—forms, voices, sources—are ones that a reader can recognise in the text itself.” No labels yet, no regime jargon, no MI talk; just what the target “appropriate knowledge” *looks like*. --- ### P6: Name it semiotic physics and place it relative to Janus and Picca **Job:** Now attach the name, and explicitly say what you share with Janus-style semiotic physics and Picca’s “Not Minds, but Signs”. **Content:** - First sentence: “For ease of reference I will call this way of describing model behaviour *semiotic physics*.” - Second sentence: compact definition that points back to P5, not to implementation: - “By *semiotic physics* I mean the general study of how a fixed LLM tends, across prompts, to extend and reshape linguistic material drawn from its training corpus.” - Third sentence: Janus connection: - “The label comes from recent work that treats LLMs as systems that generate trajectories of text under a fixed update rule and urges us to focus on those trajectories rather than on hypothetical inner agents.” - Fourth sentence: Picca connection: - “It is also close to Picca’s view of LLMs as semiotic machines that operate on and circulate signs, rather than hosting beliefs or intentions.” - Fifth sentence: your own angle: - “My focus here is narrower: I use ‘semiotic physics’ for the mid-level facts about text that can guide aesthetic attention to model outputs under Carlson’s framework.” That way, Janus + Picca are in the body, and the reader sees the family resemblance. --- ### P7: Unpack the “semiotic” part with corpora and discourses (not probabilities) **Job:** Explain what is “semiotic” here by returning to the corpus/text-culture story, not by dropping stochastic jargon. **Content:** - First sentence: “The approach is semiotic in a straightforward sense: it starts from the fact that the model has been trained on many different discourses in a language and looks at how it has learnt to use them.” - Second sentence: spell out that the corpus contains multiple discursive traditions and subcultures (e.g. mainstream news, technical papers, fanfic, academic philosophy, wellness blogs, shitposts, etc.). - Third sentence: “Training on such a mix means the model has tacit knowledge of typical ways of explaining, arguing, narrating, instructing, joking, and so on.” - Fourth sentence: “Semiotic physics organises this tacit knowledge into a usable shape, so that we can say, for example, that a model tends to answer legal questions in a casebook-like way, and questions about inner life in a hybrid of pop psychology and mindfulness talk.” This paragraph should feel like the opposite of the firehose: concrete and about language. --- ### P8: Talk about recurring “modes of response” to prompts (no “regime” language) **Job:** Give the structural scaffolding you need later (for reasoning + bee) by talking about recurring modes, in prose. **Content:** - First sentence: “One helpful way to use semiotic physics is to speak of recurring modes of response to broad kinds of prompt.” - Then 3–4 sentences, each on one mode, with one example, folded into continuous prose: - Expository mode for definition/explanation prompts: neutral tone, “X is Y” style, predictable order. - Advisory/therapeutic mode for help/comfort prompts: second-person address, validation, numbered suggestions. - Reasoning mode for “think step by step” prompts: segmented answers with labelled steps, connectors like “first… next…”, toy examples, recap. - Stylised/poetic mode for playful or ecstatic prompts: dense coinages, mixed registers, thematic fixations (the bee text will illustrate this). - Closing sentence: “Semiotic physics describes these modes in general terms, so that particular replies can be read as instances of them rather than as free-standing performances.” No jargon, just enough structure to support Section 6. --- ### P9: Describe the structural regularities semiotic physics tracks (form, voice, source) **Job:** Say what kinds of regularities matter, without collapsing into “aesthetics of bullet points”. **Content:** - Opening sentence: “At a slightly more abstract level, semiotic physics tracks regularities of form, voice, and source.” - Then three sentences: - Form: “Form covers things like how replies tend to be divided into parts, how explanations and arguments are ordered, and where repetition or recaps usually occur.” - Voice: “Voice covers which stance a model usually takes in response to a prompt—explaining, arguing, narrating, advising—and how these stances are sometimes mixed.” - Source: “Source covers which strands of its training culture a model tends to draw on in a given case, for example whether a scientific answer reads more like a textbook chapter or more like a popular-science blog post.” - One sentence linking forward: “In Section 6 I treat a chain-of-thought answer and the bee text as short samples of such regularities, quite different in tone but both shaped by this inherited text culture.” This gives Section 6 hooks, but it is about structure, not bullet lists. --- ### P10: Insert the Carlson “right kind of knowledge” claim here, not as a separate block **Job:** State, inside the explanatory flow, that semiotic physics is your candidate “appropriate knowledge”. **Content:** - First sentence: “In Carlson’s terms, semiotic physics is a non-aesthetic account of how LLM outputs acquire their order from training, corpora, and alignment.” - Second sentence: “Because it works with forms, voices, and sources that are visible in the text, it can guide acts of aspection in much the same way geology guides what we look at in a cliff.” - Third sentence: “For this reason I take semiotic physics to be a good candidate for the ‘right kind of knowledge’ for appreciating LLM-mediated chats under an order-based framework.” No separate Carlson block; this is folded into the positive exposition. --- ### P11: Brief partial-knowledge caveat (for jailbreaks etc.) **Job:** Keep the point that semiotic physics does not need full Anthropic documentation. **Content:** - One sentence: “In many practical cases we do not know what exact mixture of data and alignment procedures produced a given model.” - One sentence: “Semiotic physics, as I use the term here, starts from patterns that show up in ordinary interaction and from generic facts about LLM training.” - One sentence: “That is enough for the examples in Section 6, including a text produced by a safety-relaxed Claude-like configuration.” Short and to the point. --- ### P12: Positioning and transition to the “banal test case” and “baroque” example **Job:** Tie back to design/person debates in a single breath and point forward. **Content:** - One sentence: “Design-level knowledge about architectures and objectives still matters for assessing LLMs as artefacts, but semiotic physics shifts our attention to their behaviour as producers of text.” - One sentence: “Person-based appreciation does not fit LLMs, and semiotic physics does not reintroduce a subject; it stays with patterns of language use.” - Final sentence: “In Section 6 I apply this framework first to a banal reasoning-style answer, then to a more baroque bee text, and show how both can be read as objects of order appreciation.” --- Key changes relative to the last plan: - No more “P4 desiderata” and “P5 ruling out candidates” filler; the only new bridge paragraph is P3. - Mechanistic interpretability is not marked as “failing” anything; it is explicitly assimilated to chemical physics: possible in principle, not used here because we are aiming at a text-facing level. - The core of the section (P4–P9) is now about corpora, text culture, forms/voices/sources—what you called the “passionate interesting stuff”—not about reciting Carlson or rehashing earlier sections. - The Carlson “right kind of knowledge” claim is integrated into P10, not given an isolated mini-essay. - No “regime”, no “semiotic all the way down”, no aesthetics of bullet points. If this skeleton feels closer to what you actually want to argue, then the next step *after* this will be to try a new prose version of P3–P10 only, leaving P1–P2 yours and P11–P12 short. Good, let’s do a clean, implementation-ready plan. I’ll assume: - **P1–P2** are the Carlson/geology/chemical-physics paragraphs you already have and are happy with. - The plan starts from **P3** and just says what each new paragraph should *do* and *contain*, not how to phrase it. No example sentences, just content. --- ### P1–P2: Existing text (unchanged) - P1: Your existing opening sentence plus Carlson quote and explanation about non-aesthetic stories making natural order visible (geology, ecology, meteorology) and guiding aspection. - P2: Your existing chemical-physics vs geology paragraph: chemical physics in principle, geology as the tractable mid-level background whose categories match what we can see. --- ### P3: Bridge from nature case to LLM case **Purpose:** Move from cliffs and geology to LLMs and text, in Carlson’s idiom, in one short paragraph. **Content:** - Refer back to Section 2 as already providing a general, non-aesthetic story about LLMs (token predictors trained on large corpora, tuned by RLHF). - Refer back to Sections 3–4 very briefly to remind the reader that person-based and pure design-based appreciations were examined and found unsatisfying for capturing *behavioural* order. - State that, by analogy with nature, you now want an analogue of geology for LLM-mediated chats: a non-aesthetic description that can make the order in outputs visible for appreciation. --- ### P4: LLM training as compressed “text culture” **Purpose:** Put the emphasis squarely on corpora and inherited language practices as the material that generates order. **Content:** - Say that contemporary LLMs are trained on extremely large, heterogeneous text corpora that include many discourses (fiction, journalism, academic writing, online discussion, etc.). - State that this means the model has internalised regularities of *use*: not just local word co-occurrences but characteristic ways of explaining, arguing, narrating, advising, joking, and so on. - Emphasise that outputs can be seen as recombinations of this inherited “text culture” rather than as neutral strings laid over a separate reality. - Conclude that any geology-like background for LLMs should treat this linguistic material, and the ways the model reworks it, as the basis for the order we see in replies. --- ### P5: Characterise the target kind of descriptive knowledge (unnamed) **Purpose:** Say what sort of non-aesthetic description would be useful for appreciation, before giving it a label. **Content:** - Describe, at a conceptual level, a body of knowledge that: - summarises how a model typically writes in response to different kinds of prompts (e.g. definitional, explanatory, narrative, advisory, playful), - summarises how it tends to organise content within those responses (typical structures of explanations, arguments, stories, etc.), - and summarises which parts of the inherited text culture it tends to draw on in a given context (for instance, more textbook-like, more news-like, more forum-like, more experimental). - Note that this would still be non-aesthetic, but would describe processes that give replies their shape. - Note that its concepts (forms of writing, ways of organising content, sources in textual culture) are ones a reader can recognise in the outputs themselves. --- ### P6: Introduce the label semiotic physics and locate it relative to Janus and Picca **Purpose:** Name the package you will use and show how it relates to existing views without importing their full machinery. **Content:** - Say that you will use the label *semiotic physics* for this sort of mid-level, text-facing description of model behaviour. - Define it, briefly, by reference back to P5: a general study of how a given LLM tends, across prompts, to extend and reshape linguistic material drawn from its training data. - Mention Janus-style “semiotic physics” work: - note that this work treats LLMs as systems that generate sequences of text under a fixed update rule and urges attention to those sequences rather than to hypothesised inner minds. - Mention Picca’s “Not Minds, but Signs”: - note that he argues LLMs should be understood as semiotic machines that manipulate signs rather than as bearers of beliefs or intentions. - State that your use of “semiotic physics” overlaps with these views in focusing on sign-level behaviour, but is narrower in being aimed at providing background knowledge for aesthetic appreciation (rather than at safety or semantic questions). --- ### P7: Explain the “semiotic” aspect via corpora and discourses **Purpose:** Clarify what is meant by “semiotic” here, using corpora and discourses rather than probabilistic jargon. **Content:** - Make explicit that the approach takes linguistic items (words, subwords, symbols) as its basic units. - Emphasise that the training corpora contain many different discourses and subcultures (scientific, legal, philosophical, marketing, fiction, fan writing, social media, etc.). - State that through training the model has internalised characteristic patterns of these discourses: how they tend to structure information, which terms they favour, what rhythms and rhetorical moves they use. - Say that semiotic physics organises this tacit knowledge so that we can talk systematically about how a given model tends to use and recombine these discursive resources in its outputs. --- ### P8: Introduce recurring “modes of response” to prompts **Purpose:** Provide the structural scaffolding that will later support the reasoning and bee examples, without jargon. **Content:** - Introduce the idea that, for practical purposes, we can talk about recurring modes in which a model usually answers broad classes of prompts. - Sketch a small number of such modes in conceptual terms (not as a list in the paper, but as distinct elements of the paragraph): - an expository mode for definition/explanation prompts; - an advisory/therapeutic mode for help or reassurance prompts; - a reasoning mode for prompts that explicitly ask for “step-by-step” thinking; - one or more highly stylised modes for prompts that invite playful, poetic, or ecstatic language (the bee text being one such case). - State that semiotic physics describes these modes in general and notes which prompts tend to elicit which mode. --- ### P9: Structural regularities that semiotic physics tracks **Purpose:** Say what kinds of regularities semiotic physics is interested in, without reducing this to a catalogue of surface quirks. **Content:** - Organise this paragraph around a small number of structural dimensions: - **Form**: regularities in how replies are segmented and ordered (for example, typical patterns of sectioning, sequencing of points, use of recaps). - **Voice / stance**: regularities in whether the model tends to adopt an explanatory, argumentative, narrative, advisory, or confessional stance for certain prompts, and how it sometimes blends these. - **Source in text culture**: regularities in which discursive traditions the model tends to echo in a given context (for example, textbook vs news vs forum vs experimental styles). - Indicate that the later case studies (reasoning answer and bee text) will be read as short samples of such regularities, rather than as isolated performances. --- ### P10: State, inside the exposition, that this is your Carlson-style “appropriate knowledge” **Purpose:** Integrate the Carlson point, rather than separating it into a freestanding “fits Carlson” paragraph. **Content:** - Explicitly say that, for LLMs, this semiotic, mid-level body of knowledge plays the same structural role that geology plays for cliffs in Carlson’s account. - Note that it is non-aesthetic, that it explains how outputs get their order from training, corpora, and alignment, and that it gives readers concepts they can actually use when attending to a text. - State that, in this sense, semiotic physics is your candidate for the “right kind of knowledge” for order appreciation of LLM-mediated chats. --- ### P11: Brief partial-knowledge caveat (for jailbroken / undocumented variants) **Purpose:** Make clear that semiotic physics does not require full internal documentation of a model. **Content:** - Acknowledge that for many deployed systems (including safety-relaxed or jailbroken variants) we lack details about exact training data and alignment procedures. - State that semiotic physics, as you use it, is primarily based on observable patterns in input–output behaviour under a given configuration, together with generic facts about LLM training. - Note that this is sufficient for the examples you will use in Section 6, including the bee text from a Claude-like variant. --- ### P12: Positioning and transition to Section 6 **Purpose:** Briefly situate semiotic physics alongside the person and design approaches, then point to the upcoming examples. **Content:** - One concise remark that design- and function-based knowledge about architectures and objectives remain important for assessing LLMs as artefacts, but semiotic physics shifts attention to their behaviour as text producers. - One concise remark that person-based appreciation is not appropriate for LLMs as argued earlier, and that semiotic physics does not bring back a subject; it keeps the focus on patterns of language use. - A closing sentence that signals the move to Section 6, saying that you will now apply this framework to two contrasting cases: a banal reasoning-style answer and a more baroque bee text, to show how semiotic physics can guide order appreciation in both. In this section I bring Carlson’s order-based framework back to the LLM case. I first recall how, in natural settings, scientific knowledge makes order visible for appreciation. I then introduce *semiotic physics* as a way of using existing computer science work on LLMs to play a similar role for model outputs, and I close by explaining how this prepares the ground for the extended example that follows. Carlson’s starting point was that appropriate appreciation of nature depends on a general, non-aesthetic story that makes its order visible. As he puts it, the appreciator “selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them”, and does so “by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible” (Carlson, 2000, p. 119). In the natural case, this story is typically supplied by the environmental sciences. Geology explains how cliffs and valleys are shaped by erosion and sedimentation over time. Ecology explains how the distribution of plants in a meadow follows from competition, cooperation, and niche structure. Meteorology explains large-scale patterns in clouds and weather systems. With such accounts in mind, what might otherwise look like accidental shape or colour can be read as the trace of identifiable processes, and this guides what we attend to when we appreciate an environment. One might note that nothing in principle rules out more fundamental sciences here. Chemical physics also explains the cliff face, in terms of the structure and interaction of molecules, and in principle one could build an order-based aesthetics of nature around such accounts. For present purposes, however, geology makes Carlson’s idea of aspection easier to explain. Geological categories such as strata, faults, and erosion channels can be used directly as ways of looking: they tell the appreciator which bands to treat as distinct, which lines to trace, which contrasts of texture and angle to attend to when they look at a cliff with the naked eye. The microstructural patterns described by chemical physics do not map as straightforwardly onto features that are available to ordinary perception, so it is harder to show how such knowledge might guide someone’s acts of aspection in the field. This is enough for the present point: Carlson’s recommendation can be introduced most naturally by starting from sciences whose categories can already function as ways of looking, even if more fundamental levels could in principle support a similar story. --- ### P3 – Bridge from nature to LLMs **Purpose:** Move from cliffs/geology to LLMs/text, in Carlson’s idiom, as briefly as possible. **Content (what this paragraph should include):** - A direct reference back to Section 2 as already providing a general, non-aesthetic account of LLMs (token-based models trained on large corpora and then post-trained). - A very brief reminder that Sections 3–4 showed that person-based and pure design-based approaches are not enough for appreciating *behavioural order* in LLMs. - A statement that, by analogy with the natural case, what is now needed is an analogue of geology for LLM-mediated chats: a non-aesthetic description that can make the order in outputs visible for appreciation. --- ### P4 – LLM training as compressed “text culture” **Purpose:** Put the focus on corpora and inherited language practices as the material that generates order, and make this vivid. **Content:** - A claim that contemporary LLMs are trained on extremely large, heterogeneous text corpora containing many discourses (fiction, journalism, academic writing, technical documentation, legal texts, online discussion, etc.). - The idea that this training gives the model a compressed form of many ways of writing in the language: typical explanatory styles, argumentative templates, narrative forms, advisory voices, etc. - The point that model outputs can be seen as recombinations and reorganisations of this inherited “text culture”, not as neutral strings. - A closing line that says: any geology-like background for LLMs should treat this linguistic material, and the ways the model reworks it, as the source of the order we see in replies. --- ### P5 – Characterise the target kind of descriptive knowledge (unnamed) **Purpose:** Say what sort of non-aesthetic description would be useful for appreciation, before giving it any label. **Content:** - A description of a body of knowledge that: - summarises how a given model *typically* writes in response to different kinds of prompts (definitional, explanatory, narrative, advisory, playful, etc.); - summarises how it tends to *organise* material within these responses (common structures of explanations, arguments, stories, chains of reasoning); - summarises which parts of the inherited text culture it tends to draw on in particular contexts (textbook-like vs news-like vs forum-like vs experimental, etc.). - An explicit remark that this description is non-aesthetic but concerns processes that give replies their shape. - An explicit remark that its concepts (forms of writing, ways of organising content, sources in textual culture) are ones a reader can recognise in the outputs themselves. --- ### P6 – Introduce the label semiotic physics and relate it to Janus and Picca **Purpose:** Name the package and situate it relative to existing semiotic-physics and semiotic-machine views, without importing their full machinery. **Content:** - A sentence saying you will use the label *semiotic physics* for the kind of mid-level, text-facing description outlined in P5. - A short, explicit definition of *semiotic physics* in terms of P5: a general study of how a fixed LLM tends, across prompts, to extend and reshape linguistic material drawn from its training data. - A brief link to Janus-style “semiotic physics” work: - mention that this work treats LLMs as systems that generate sequences of text under a fixed update rule and urges attention to those sequences rather than to hypothetical inner minds. - A brief link to Picca’s “Not Minds, but Signs”: - mention that he argues LLMs should be understood as semiotic machines that operate on and circulate signs rather than as bearers of beliefs or intentions. - A closing remark that your use of “semiotic physics” overlaps with these views in focusing on sign-level behaviour, but is narrower in being explicitly aimed at providing background knowledge for aesthetic appreciation under Carlson’s framework. --- ### P7 – Explain the “semiotic” aspect via corpora and discourses **Purpose:** Clarify what is meant by “semiotic” here using corpora/discourses rather than low-level probabilistic talk. **Content:** - A statement that the approach takes linguistic items (words, subwords, symbols) as its basic units. - An explicit mention that the training corpora contain many different discourses and subcultures (e.g. scientific, legal, philosophical, marketing, fiction, fan writing, social media, etc.). - The claim that training on this mix means the model has internalised characteristic patterns of these discourses: how they tend to structure information, which terms they favour, what rhythms and rhetorical moves they use. - An explanation that semiotic physics organises this tacit discursive knowledge so that we can talk systematically about how a given model tends to use and recombine these resources in its outputs. --- ### P8 – Recurring “modes of response” to prompts **Purpose:** Provide the structural scaffolding needed later (for the reasoning and bee examples) in terms of recurring modes of response, without jargon. **Content:** - An introduction of the idea that, for practical purposes, we can talk about recurring modes in which a model usually answers broad classes of prompts. - Short descriptions (in one paragraph, not as a list) of a small number of such modes, for example: - an expository mode for definition/explanation prompts; - an advisory/therapeutic mode for help or reassurance prompts; - a reasoning mode for prompts that explicitly ask for “step-by-step” thinking; - one or more highly stylised modes for prompts that invite playful, poetic, or ecstatic language (with a nod to the bee text as an example). - A statement that semiotic physics records these modes in general and notes which kinds of prompts tend to elicit which modes. --- ### P9 – Structural regularities tracked by semiotic physics (form, voice, source) **Purpose:** Say what kinds of regularities semiotic physics is interested in, at a structural level, without collapsing into a list of surface tics. **Content:** - An organising sentence that semiotic physics focuses on regularities of: - **Form**: patterns in how replies are segmented and ordered (how explanations and arguments tend to be structured, where repetition/recaps usually occur). - **Voice / stance**: patterns in whether the model tends to adopt an explanatory, argumentative, narrative, advisory, or confessional stance for certain prompts, and how these stances are sometimes blended. - **Source in textual culture**: patterns in which discursive traditions the model tends to echo in a given context (e.g. textbook-like vs news-like vs forum-like vs experimental styles). - A note that the case studies in Section 6 (reasoning-style answer and bee text) will be read as short samples of such regularities, rather than as isolated performances. --- ### P10 – State that semiotic physics is your Carlson-style “right kind of knowledge” **Purpose:** Integrate the Carlson point inside the explanatory run, rather than in a separate mini-essay. **Content:** - An explicit claim that, for LLMs, this semiotic, mid-level body of knowledge plays the same structural role that geology plays for cliffs in Carlson’s account: it supplies a non-aesthetic story that makes order visible. - A remark that it explains how outputs acquire their form and voice from training, corpora, and alignment, and that its categories are ones readers can actually use when attending to a text. - A clear statement that you take semiotic physics to be your candidate for the “right kind of knowledge” for appreciating LLM-mediated chats under an order-based framework. --- ### P11 – Partial-knowledge caveat (for jailbroken / undocumented variants) **Purpose:** Make clear that semiotic physics does not require full internal documentation of a model. **Content:** - An acknowledgement that for many deployed systems (including safety-relaxed or jailbroken variants) we lack precise information about training data and alignment procedures. - A statement that semiotic physics, in your use, is primarily based on observable patterns in input–output behaviour under a given configuration, plus generic facts about LLM training. - A note that this is sufficient for the examples in Section 6, including the bee text from a Claude-like variant. --- ### P12 – Positioning and transition to Section 6 **Purpose:** Briefly position semiotic physics alongside person and design approaches and signal the move to the examples. **Content:** - A concise remark that design- and function-based knowledge about architectures and objectives remain relevant for assessing LLMs as artefacts, but semiotic physics shifts attention to their behaviour as producers of text. - A concise remark that person-based appreciation is not appropriate for LLMs (as argued earlier) and that semiotic physics does not reintroduce a subject; it keeps the focus on patterns of language use. - A closing sentence indicating that Section 6 will apply this framework to two contrasting cases—a banal reasoning-style answer and a more baroque bee text—to show how semiotic physics can guide order appreciation in both. Got it. Here is the plan again, with your three Carlson paragraphs removed and the plan starting immediately after them. No example sentences, just what each paragraph should contain. --- ### P3 – Bridge from nature to LLMs **Purpose:** Move from the natural case to the LLM case in Carlson’s idiom, very briefly. **Content:** - Refer back to Section 2 as already providing a general, non-aesthetic account of LLMs (token-based models trained on large text corpora, then post-trained). - Refer back to Sections 3–4 to remind the reader, in one stroke, that person-based and pure design-based approaches do not give a satisfactory way of appreciating behavioural order in LLMs. - State that, by analogy with the natural case, you now want an analogue of geology for LLM-mediated chats: a non-aesthetic description that can make the order in outputs visible for appreciation. --- ### P4 – LLM training as compressed “text culture” **Purpose:** Put the emphasis on corpora and inherited language practices as the material from which order in outputs arises. **Content:** - State that contemporary LLMs are trained on extremely large, heterogeneous text corpora that include many discourses (fiction, journalism, academic writing, technical documentation, legal texts, online discussion, etc.). - Explain that this means the model has internalised characteristic ways of writing in the language: common explanatory styles, argumentative templates, narrative forms, advisory voices, and so on. - Emphasise that model outputs can be seen as recombinations and reorganisations of this inherited “text culture”, rather than as neutral strings detached from it. - Conclude that any geology-like background for LLMs should treat this linguistic material, and the ways the model reworks it, as the source of the order we see in replies. --- ### P5 – Characterise the target kind of descriptive knowledge (unnamed) **Purpose:** Say what sort of non-aesthetic description would be useful for appreciation, before giving it a label. **Content:** - Describe a body of knowledge that: - summarises how a given model typically writes in response to different kinds of prompts (definitional, explanatory, narrative, advisory, playful, etc.); - summarises how it tends to organise content within these responses (common structures of explanations, arguments, stories, chains of reasoning); - summarises which parts of the inherited text culture it tends to draw on in particular contexts (for example, textbook-like vs news-like vs forum-like vs experimental). - Note explicitly that this is still non-aesthetic, but concerns processes that give replies their shape. - Note explicitly that its concepts (forms of writing, ways of organising content, sources in textual culture) are ones that a reader can recognise in the outputs themselves. --- ### P6 – Introduce the label semiotic physics and relate it to Janus and Picca **Purpose:** Name the package and situate it relative to existing semiotic-physics and semiotic-machine views, without importing their full apparatus. **Content:** - State that you will use the label *semiotic physics* for the kind of mid-level, text-facing description outlined in P5. - Define *semiotic physics* succinctly in terms of P5: a general study of how a fixed LLM tends, across prompts, to extend and reshape linguistic material drawn from its training data. - Link to Janus-style “semiotic physics” work: - mention that this work treats LLMs as systems that generate sequences of text under a fixed update rule and urges attention to those sequences, rather than to hypothetical inner minds. - Link to Picca’s “Not Minds, but Signs”: - mention that he argues LLMs should be understood as semiotic machines that operate on and circulate signs, rather than as bearers of beliefs or intentions. - State that your use of “semiotic physics” overlaps with these views in focusing on sign-level behaviour, but is narrower in being explicitly aimed at providing background knowledge for aesthetic appreciation under Carlson’s framework. --- ### P7 – Explain the “semiotic” aspect via corpora and discourses **Purpose:** Clarify what is meant by “semiotic” here using corpora and discourses, not probabilistic jargon. **Content:** - Make explicit that the approach takes linguistic items (words, subwords, symbols) as its basic units. - Note that the training corpora contain many different discourses and subcultures (scientific, legal, philosophical, marketing, fiction, fan writing, social media, etc.). - Explain that training on this mix means the model has internalised characteristic patterns of these discourses: how they tend to structure information, which terms they favour, what rhythms and rhetorical moves they use. - State that semiotic physics organises this tacit discursive knowledge so Carlson's starting point was that appropriate appreciation of nature depends on a general, non-aesthetic story that makes its order visible. As he puts it, the appreciator "selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them", and does so "by reference to a general nonaesthetic and nonartistic story that helps make this order visible and intelligible" (Carlson, 2000, p. 119). In the natural case, this story is typically supplied by the environmental sciences. Geology explains how cliffs and valleys are shaped by erosion and sedimentation over time. Ecology explains how the distribution of plants in a meadow follows from competition, cooperation, and niche structure. Meteorology explains large-scale patterns in clouds and weather systems. With such accounts in mind, what might otherwise look like accidental shape or colour can be read as the trace of identifiable processes, and this guides what we attend to when we appreciate an environment. One might note that nothing in principle rules out more fundamental sciences here. Chemical physics also explains the cliff face, in terms of the structure and interaction of molecules, and in principle one could build an order-based aesthetics of nature around such accounts. For present purposes, however, geology makes Carlson's idea of aspection easier to explain. Geological categories such as strata, faults, and erosion channels can be used directly as ways of looking: they tell the appreciator which bands to treat as distinct, which lines to trace, which contrasts of texture and angle to attend to when they look at a cliff with the naked eye. The microstructural patterns described by chemical physics do not map as straightforwardly onto features that are available to ordinary perception, so it is harder to show how such knowledge might guide someone's acts of aspection in the field. This is enough for the present point: Carlson's recommendation can be introduced most naturally by starting from sciences whose categories can already function as ways of looking, even if more fundamental levels could in principle support a similar story. --- ### P3 – Bridge from nature to LLMs **Purpose:** Move from the natural case to the LLM case in Carlson’s idiom, as briefly as possible. **Content:** - Refer back to Section 2 as already providing a general, non-aesthetic account of LLMs (token-based models trained on large text corpora, then post-trained). - Refer back to Sections 3–4 in one short move to remind the reader that person-based and pure design-based approaches do not give a satisfactory way of appreciating *behavioural* order in LLMs. - State that, by analogy with the natural case, what is now needed is an analogue of geology for LLM-mediated chats: a non-aesthetic description that can make the order in outputs visible for appreciation. --- ### P4 – LLM training as compressed “text culture” **Purpose:** Put the emphasis on corpora and inherited language practices as the material from which order in outputs arises. **Content:** - State that contemporary LLMs are trained on extremely large, heterogeneous corpora containing many discourses (fiction, journalism, academic writing, technical documentation, legal texts, online discussion, etc.). - Explain that this training gives the model a compressed form of many ways of writing in the language: common explanatory styles, argumentative templates, narrative forms, advisory voices, and so on. - Emphasise that model outputs can be understood as recombinations and reorganisations of this inherited “text culture”, rather than as neutral strings detached from it. - Conclude that any geology-like background for LLMs should treat this linguistic material, and the ways the model reworks it, as the source of the order we see in replies. --- ### P5 – Characterise the target kind of descriptive knowledge (still unnamed) **Purpose:** Say what sort of non-aesthetic description would be useful for appreciation, before giving it a label. **Content:** - Describe a body of knowledge that: - summarises how a given model typically writes in response to different kinds of prompts (definitional, explanatory, narrative, advisory, playful, etc.), - summarises how it tends to organise content within these responses (common structures of explanations, arguments, stories, chains of reasoning), - summarises which parts of the inherited text culture it tends to draw on in particular contexts (for example, textbook-like vs news-like vs forum-like vs experimental). - Note explicitly that this description is non-aesthetic but concerns processes that give replies their shape. - Note explicitly that its concepts (forms of writing, ways of organising content, sources in textual culture) are ones a reader can recognise in the outputs themselves. --- ### P6 – Introduce the label semiotic physics and relate it to Janus and Picca **Purpose:** Name the package and situate it relative to existing semiotic-physics and semiotic-machine views, without importing their full apparatus. **Content:** - State that you will use the label *semiotic physics* for the kind of mid-level, text-facing description outlined in P5. - Define *semiotic physics* succinctly in terms of P5: a general study of how a fixed LLM tends, across prompts, to extend and reshape linguistic material drawn from its training data. - Link to Janus-style “semiotic physics” work: - mention that this work treats LLMs as systems that generate sequences of text under a fixed update rule and urges attention to those sequences, rather than to hypothetical inner minds. - Link to Picca’s “Not Minds, but Signs”: - mention that he argues LLMs should be understood as semiotic machines that operate on and circulate signs, rather than as bearers of beliefs or intentions. - State that your use of “semiotic physics” overlaps with these views in focusing on sign-level behaviour, but is narrower in being explicitly aimed at providing background knowledge for aesthetic appreciation under Carlson’s framework. --- ### P7 – Explain the “semiotic” aspect via corpora and discourses **Purpose:** Clarify what is meant by “semiotic” here using corpora and discourses, not probabilistic jargon. **Content:** - Make explicit that the approach takes linguistic items (words, subwords, symbols) as its basic units. - Note that the training corpora contain many different discourses and subcultures (scientific, legal, philosophical, marketing, fiction, fan writing, social media, etc.). - Explain that training on this mix means the model has internalised characteristic patterns of these discourses: how they tend to structure information, which terms they favour, what rhythms and rhetorical moves they use. - State that semiotic physics organises this tacit discursive knowledge so that we can talk systematically about how a given model tends to use and recombine these resources in its outputs. --- ### P8 – Recurring “modes of response” to prompts **Purpose:** Provide the structural scaffolding needed later (for the reasoning and bee examples) in terms of recurring modes of response, without jargon. **Content:** - Introduce the idea that, for practical purposes, we can talk about recurring modes in which a model usually answers broad classes of prompts. - In the same paragraph, describe a small number of such modes in conceptual terms, for example: - an expository mode for definition/explanation prompts, - an advisory/therapeutic mode for help or reassurance prompts, - a reasoning mode for prompts that explicitly ask for “step-by-step” thinking, - one or more highly stylised modes for prompts that invite playful, poetic, or ecstatic language (with a nod to the bee text as an example). - State that semiotic physics records these modes in general and notes which kinds of prompts tend to elicit which mode. --- ### P9 – Structural regularities tracked by semiotic physics (form, voice, source) **Purpose:** Say what kinds of regularities semiotic physics is interested in, at a structural level, without reducing this to a list of surface tics. **Content:** - Organise the paragraph around three structural dimensions: - **Form**: patterns in how replies are segmented and ordered (how explanations and arguments tend to be structured, where repetition or recaps usually occur). - **Voice / stance**: patterns in whether the model tends to adopt an explanatory, argumentative, narrative, advisory, or confessional stance for certain prompts, and how these stances are sometimes blended. - **Source in textual culture**: patterns in which discursive traditions the model tends to echo in a given context (for example, textbook-like vs news-like vs forum-like vs experimental styles). - Indicate that the case studies in Section 6 (reasoning-style answer and bee text) will be read as short samples of such regularities, rather than as isolated performances. --- ### P10 – State that semiotic physics is your Carlson-style “right kind of knowledge” **Purpose:** Integrate the Carlson point inside the explanatory run, rather than in a separate mini-essay. **Content:** - Claim that, for LLMs, this semiotic, mid-level body of knowledge plays the same structural role that geology plays for cliffs in Carlson’s account: it supplies a non-aesthetic story that makes order visible. - Note that it explains how outputs acquire their form and voice from training, corpora, and alignment, and that its categories are ones readers can actually use when attending to a text. - State that you take semiotic physics to be your candidate for the “right kind of knowledge” for appreciating LLM-mediated chats under an order-based framework. --- ### P11 – Partial-knowledge caveat (for jailbroken / undocumented variants) **Purpose:** Make clear that semiotic physics does not require full internal documentation of a model. **Content:** - Acknowledge that for many deployed systems (including safety-relaxed or jailbroken variants) we lack precise information about training data and alignment procedures. - State that semiotic physics, in your use, is primarily based on observable patterns in input–output behaviour under a given configuration, plus generic facts about LLM training. - Note that this is sufficient for the examples in Section 6, including the bee text from a Claude-like variant. --- ### P12 – Positioning and transition to Section 6 **Purpose:** Briefly position semiotic physics alongside person and design approaches and signal the move to the examples. **Content:** - Make a concise remark that design- and function-based knowledge about architectures and objectives remain relevant for assessing LLMs as artefacts, but semiotic physics shifts attention to their behaviour as producers of text. - Make a concise remark that person-based appreciation is not appropriate for LLMs (as argued earlier) and that semiotic physics does not reintroduce a subject; it keeps the focus on patterns of language use. - Close with a sentence indicating that Section 6 will apply this framework to two contrasting cases—a banal reasoning-style answer and a more baroque bee text—to show how semiotic physics can guide order appreciation in both.