8fea4247-5adc-4031-8262-b0fb6c4142a9.md File here is a draft of paper i am writing. i would like you to help me with two things. 1. I would like you to help me think about how to iterate on this draft based on all of the inline comments within it. So this is a big job. I'd want you to go through every single inline comment. Think about them you know not just as individuals but also as you know parts of paragraphs and those paragraphs being parts of sections. And then how those sections relate to each other. And I want you to think very hard about how, yeah, what should be done to turn this text into a better version of itself based on these comments. Okay, I'm not really asking you yet to give me a detailed bullet by bullet point changes list. I'm asking you to really sort of think in at quite high levels first of all as to changes or issues that might be going on and only then start telling me about individual fixes. Okay, but we can go slow on this, okay? I want to have a brainstorm with you. I don't just want you to try and one-shot this. 2. I've also attached very old version of what we want for the final section of this text. This is a very old version and I'm yeah I think it needs to be radically reworked especially given how much the draft itself has changed since I last did a version of this section. Okay, but this old draft should give you some idea of what I wanna do in that final section. Okay, and yeah, we can take it from there. As with the other task, I'm not looking for you to one-shot this. I'm looking for you to brainstorm with me about this. Okay, so let's just yeah have a chat about things, see how we go. old version of final section (excluding conclusion):L ## 6\\. Levels of Appreciation LLMs can be appreciated at three levels: individual outputs, extended chats, and models themselves. Discussion of generative AI aesthetics has so far focused on outputs, such as images from Midjourney and texts from ChatGPT. But chats and models are also objects of appreciation, and the framework developed in Section 5 applies at each level. The relations among these levels can be clarified by analogy. An individual output is like an individual natural object, a tree, say: it is a sample of how semiotic forces have shaped a particular text under particular conditions. A chat is like an environment, a forest: semiotic forces shape the exchange over many turns, producing a configuration with its own coherence and dynamics. A model is like a natural system, the planet's biosphere, or the planet itself: it is the ground of order that manifests in outputs and chats, the system whose regularities produce those manifestations. Appreciation at each level calls for its own acts of aspection, though all are guided by knowledge of semiotic physics. ## 6.1 Appreciating Outputs A single output is one realisation of the model's semiotic physics under particular conditions. The prompt, the system configuration, and the preceding context specify initial conditions from which the model propagates text in line with its learned regularities. Different prompts activate different regularities; different contexts produce different trajectories. No single output exhausts the model's characteristic order. But each output shows how the forces operate in a specific case, and each can be appreciated as such. To show how semiotic physics guides aspection of outputs, we consider two cases that occupy different regions of a model's behavioural space. The first is the reasoning-style output familiar from everyday use: step-by-step structure, numbered stages, explicit hedging, restatement of the question, and a concluding summary. As human prose, such passages resemble competent but unremarkable textbook writing. They are useful for teaching and troubleshooting, but they do not obviously invite aesthetic attention. From the perspective of semiotic physics, however, the same outputs look different. The model has been trained on reasoning-related texts: worked proofs, textbook explanations, exam solutions, and online Q\\&A threads. It has learned that certain kinds of questions are typically followed by sequences with a characteristic structure. Post-training procedures, including instruction tuning and reinforcement learning that rewards explicit intermediate steps, further bias the model towards this pattern. Reasoning-style outputs are a stable attractor in the model's behavioural space: once entered, the model tends to stay in this mode, yielding modal inertia. The hedging, the step-by-step structure, and the summary are marks of alignment pressure: response shapes reinforced because they correlate with high human ratings. Given this, we attend differently. We attend to the characteristic rhythm of the reasoning mode: how steps are sized, how transitions are signalled, and whether the pacing is tight or padded. We attend to where alignment pressure shows: hedging patterns ('it seems', 'one might argue', 'I think'), politeness markers, and pre-emptive qualifications. We attend to how semantic attraction operates under tight constraints: vocabulary stays on topic, related terms cluster, and the model is pulled towards the semantic field established by the question. We also attend to whether the mode remains stable or shows signs of strain, and to whether the model sustains the reasoning register or begins to drift. What seemed merely useful becomes appreciable as a specimen of how semiotic forces produce reasoning-like text under tight constraints.!\[\]\[image1\] The second case is different. The text discussed here was produced by a Claude-like model in a modified configuration with safety constraints relaxed. It begins with neologisms and proceeds in short blocks separated by headings in capitals. The vocabulary is dense with coinages, many of which recombine recognisable roots from entomology, anatomy, theology, and internet slang. The registers are mixed: fragments of cod-French, pseudo-scientific talk, mystical declarations, and obscene slang. Despite the surface disorder, a stable theme runs throughout: bees and honey, tongues and throats, sweetness, bodily contact. Under semiotic physics, this text shows the forces operating under loose constraints. Semantic attraction is at work: the bee and honey theme creates an attractor, and related vocabulary – tongues, throats, sweetness, pollen, flowers, stings – is pulled towards it. But unlike the reasoning case, the attraction spreads freely across registers rather than being channelled narrowly. The model has been trained on texts that invent words – experimental poetry, surrealism, internet wordplay – and it has learned patterns of neologism: how to recombine roots, suffixes, and sound-shapes. The neologisms follow learnable patterns of word-formation rather than being random noise. The register collision reflects training diversity: the model has absorbed texts in many registers (scientific, mystical, erotic, internet-surreal), and under loose constraints these do not get filtered to a single appropriate register. They collide and mix. Despite the apparent chaos, there is order: recurring rhetorical templates, alternation between narrative stretches and reflective sentences, and consistent sound-play in the neologisms. This order is the product of semiotic forces operating with fewer constraints than in the reasoning case. Attending to this text with knowledge of semiotic physics, we notice how semantic attraction shapes the vocabulary: the gravitational pull towards bee-related terms operates across registers. We also notice patterns in neologism (learnable word-formation rules that produce coinages with a family resemblance) and the rhythm of alternation between modes (narrative stretches, reflective sentences, and exclamatory outbursts). Finally, we notice internal consistency despite surface chaos. The text becomes appreciable as a specimen of semiotic forces operating in a different region of behavioural space from the reasoning output. Carlson (2000) notes that once a specific scientific account is in play, some natural formations show the relevant order better than others: not every cliff face is equally instructive about sedimentation, not every valley equally revealing of glacial dynamics. This prevents order appreciation from collapsing into the view that everything is equally appreciable; the guiding knowledge discriminates among cases. The same holds for semiotic physics. Standard reasoning-style outputs show semiotic order, but the order they show is shallow and familiar: alignment pressure is everywhere visible, the reasoning template is stock, and the semantic channelling narrow enough that the regularities are unsurprising. The bee text is a more interesting object of appreciation not because it is more orderly but because it reveals order where none was expected. What looks like chaos – neologistic excess, register collision, surface incoherence – turns out, under semiotic physics, to be structured by identifiable forces: semantic attraction spreading freely across registers rather than channelled narrowly, learnable word-formation patterns producing coinages with family resemblance, rhythmic alternation and internal consistency maintained beneath apparent disorder. The bee text also shows forces operating in regions of behavioural space that normal product configurations occlude. It is, in this sense, analogous to a geological formation that exposes strata usually buried – not more ordered than the surrounding terrain, but more \*revealing\* of the order that is everywhere present. The contrast between these two cases helps to locate what semiotic physics brings into view. Reasoning outputs show semiotic forces operating under tight constraints: a stable mode, narrow semantic channelling, and alignment pressure shaping response structure. The bee text shows semiotic forces operating under loose constraints: unstable modes mixing, semantic attraction spreading across registers, and training diversity showing through. Both are products of the same semiotic physics, but they occupy different regions of the model's space. Appreciating both requires the same kind of knowledge – knowledge of semiotic forces – but different acts of aspection. We scan the reasoning output for rhythm and regularity; we scrutinise the bee text for pattern within apparent chaos. ## 6.2 Appreciating Chats Carlson's environments are not collections of discrete objects but systems in which forces operate and interact over space and time. A forest is not just many trees; it is a space where ecological forces – competition for light, nutrient cycling, succession dynamics – play out, producing emergent order that no single organism embodies. The appreciator navigates this environment, and their path determines what order becomes visible. Chat instances stand to single outputs as environments stand to individual natural objects. A chat accumulates context that shapes how semiotic forces manifest: early vocabulary choices establish attractors that persist, early register-setting constrains later exchanges, and the exchange develops path-dependent structure that neither party fully controls. The user's prompts are not just elicitations but navigational interventions, steering the system through different regions of its behavioural space and making different orders visible. To appreciate a chat is to appreciate an emergent configuration produced by semiotic forces operating over the chat's temporal extension – not just a sequence of isolated responses. A single output is one trajectory from one set of initial conditions. An extended exchange lets regularities show up across turns. The model carries forward elements of earlier responses, picks up threads, sometimes drops them, and shifts register in response to user prompts. Chats manifest features that a single output does not. First, coherence maintenance, that is, how the model sustains or loses threads across turns, how far back its effective 'memory' extends, and where coherence begins to fray. Second, context accumulation, which determines how earlier material shapes later responses, and how terms or framings established early persist or fade. Third, register dynamics, which concerns how the model responds to shifts in user tone, topic, or style, and whether it matches the user's register or maintains its own. Finally, mode stability over time, to wit, whether the model stays in a mode or drifts, what triggers transitions, and how gracefully it handles them. These are manifestations of semiotic forces operating over longer timescales than a single output can reveal. A chat instance is like a particular forest: the forces of semiotic physics have produced a specific configuration. Different prompting strategies, different topics, and different user styles produce different configurations. But the same underlying forces are at work. Appreciating a chat means attending to how the forces have shaped this particular extended exchange: how contextual threading has produced coherence or incoherence, how modal inertia has maintained or failed to maintain a register, and how alignment pressure has shaped the arc of the exchange. In a chat, prompting is intervention. Each turn is a probe that reveals something about the model's regularities. The experienced user's expectations are tested and refined across many turns. Interaction is itself a mode of aspection: it selects what to attend to, organises appreciative attention over time, and deepens practical acquaintance with the model's semiotic physics. The farmer comes to know the land through working it; the user comes to know the model through prompting it. Extended exchanges are where practical acquaintance develops, where the user builds up the kind of knowledge that guides appreciation even without deliberate theoretical articulation. ## 6.3 Appreciating Models Outputs and chats are where semiotic order manifests. The model itself is the ground of that order: the system whose regularities produce particular manifestations. Appreciating a model means appreciating its characteristic order across many possible outputs and chats, not just the ones actually encountered. This is not appreciation of any single output but of stable patterns across outputs: which registers the model favours, how it handles uncertainty, where it excels, where it struggles, and what regions of semiotic space it can occupy. Users sometimes speak of a model's 'vibe', a term that captures the sense that different models have different characteristic feels even when performing similar tasks. This notion of vibe, or characteristic feel, warrants pause. In Section 3 we argued against appreciating LLMs as if they were persons, on the grounds that LLMs lack the temporally extended life, the projects and commitments, and the evaluative outlook that ground beauty-of-character predicates. But users do respond to something when they talk about a model's personality or vibe. What they are responding to, we suggest, is not a character in the person-aesthetic sense but a characteristic semiotic order: a stable pattern in how the model tends to propagate text. Appreciating this order is not appreciating a person; it is appreciating a system's characteristic dynamics. The vocabulary of 'vibe' is a colloquial marker of what semiotic physics articulates more precisely. An analogy clarifies what appreciation of a model involves. Different 3D video games have different physics engines. \*Grand Theft Auto V\* has physics tuned for spectacle: cars drift in satisfying ways, explosions have exaggerated force, and the rag-doll system produces emergent comedy. \*Dark Souls\* has physics tuned for weight: movement feels heavy, attacks have commitment, and everything has momentum. \*Breath of the Wild\* has physics tuned for playful engagement: objects afford interesting interactions, and the system invites experimentation. We appreciate these physics not primarily by asking how realistic they are but by attending to internal consistency, characteristic feel, and aesthetic fit. \*Grand Theft Auto\*'s physics serves an aesthetic of chaos and spectacle; it would not suit \*Dark Souls\*. Each game's physics is tuned to its aesthetic and ludic goals. We appreciate the physics for what it is, not for its fidelity to real-world physics. For LLMs, the analogy suggests a parallel mode of appreciation. Different models have different semiotic physics: different characteristic dynamics of text propagation. Claude's semiotic physics differs from GPT's, which differs from Gemini's. We can appreciate these differences not primarily by asking which is most human-like or most useful but by attending to internal consistency and characteristic feel. Order appreciation, as Carlson develops it, differs from design appreciation. We do not primarily ask how well the artifact serves its intended function. We attend to the order itself, the patterns produced by the forces, and appreciate them for their own character. The bee text discussed in Section 6.2 is relevant here in a further way. It was produced under relaxed constraints, revealing a region of Claude's behavioural space that is normally inaccessible under standard product configurations. Knowing that this region exists – and knowing what the model can do under different conditions – is part of appreciating the model. Model appreciation involves appreciating not just the outputs a model typically produces but the full space of outputs it could produce, and how different conditions activate different regions of that space. The bee text is a window into latent capacities, a sample from a region of semiotic space that standard use does not reach. Different models instantiate semiotic physics differently. Different training corpora, different architectures, and different post-training regimes produce different characteristic orders. Users report different feels when interacting with different models: Claude's hedging rhythms differ from GPT's briskness, and Gemini handles certain registers differently. A fuller account of model-level appreciation would map these differences systematically, developing a comparative aesthetics of LLMs. That task lies beyond the scope of this paper. For present purposes, the point is that model-level appreciation is possible and that it takes the form of appreciating distinctive semiotic order: the characteristic dynamics of text propagation that distinguish one model from another. We should distinguish the kind of appreciation we have been describing from other modes of engaging with LLMs. Capability evaluation tests whether models perform tasks correctly. Safety testing probes whether models can be induced to produce harmful outputs. Benchmarking measures performance against standardised criteria. The appreciation we describe differs from all of these. The goal is not to assess correctness, safety, or performance but to appreciate characteristic order, and to develop acquaintance with semiotic physics as it manifests in a particular model. A response that would count as a failure in capability evaluation might be aesthetically rewarding as a product of learned regularities. The appreciator is not grading but attending, not measuring but developing acquaintance. Finally, we note a thought that we flag here but do not develop. Each model, as an instantiation of semiotic physics, has learned regularities from human text. It reflects, in transformed form, the semiotic culture of its training data. There is a sense in which generative AI is a mirror of culture, not only morally, as Vallor (2024) has argued, but aesthetically. The model shows us our own semiotic patterns, filtered through statistical learning. Appreciating an LLM is, in part, appreciating culture seen through technology. This thought merits extended treatment, but such treatment lies beyond the scope of the present paper and we reserve it for future work. 2/2 I’ll treat this as a staged diagnostic rather than a rewrite. I’ll first map the draft-level problems the comments reveal, then isolate the final-section problem, and only after that move toward local fixes. I’ve read the current draft and the old Section 6 you pasted. The comments are not just “style comments”; they mostly point to repeated failures of argumentative positioning: the reader is often told accurate things before being told why those things are needed. I’ve got the shape of the problem. My first pass is this: the inline comments point to *one large structural problem* and several local symptoms of it. The structural problem is that the draft often has the right materials, but the order of argumentative presentation is wrong. It introduces distinctions, examples, and technical descriptions before the reader has been told what problem those details are meant to solve. That is especially clear in Section 2, where the account of tokens, context, pre-training, post-training, outputs, chats, and models is mostly relevant, but the section does not yet keep the Carlsonian question in view: *what kind of object is this, and what kind of knowledge would make its order appreciable?* I would not start with line edits. I would start by rebuilding the argumentative spine. ## 1\. The comments fall into four kinds First, there are *voice comments*: “not how I write”, “too declaratory”, “stilted”, “not how I write”, and so on. These are not merely cosmetic. They usually mark places where the prose is doing generic article-signposting rather than argumentative work. Second, there are *form comments*: scare quotes, “not X but Y” constructions, triplicate lists, inflated contrasts, and value-loaded phrases. These should be treated as symptoms. The prose reaches for these because it has not yet fixed the precise argumentative relation between the claims. Third, there are *ordering comments*: the reader does not yet know why they are being told something; a definition arrives too late; a paragraph begins in the wrong place; two paragraphs should be taken apart and rebuilt together. These are the most important comments. They point to section-level trouble. Fourth, there are *accuracy/earning comments*: the introduction overstates what later sections argue; Section 4’s opening misdescribes Section 3; the design conclusion is stronger than what has been shown; semiotic physics is introduced before it is motivated clearly enough. These are the comments that matter most for revision. ## 2\. The introduction is probably downstream of the rest The introduction is currently too schematic. It says: person appreciation fails; design appreciation fails; order appreciation succeeds. But the comments correctly notice that the negative arguments are more delicate than that. The paper is not simply saying that LLMs are not persons and are not designed artifacts. It is saying something more specific: LLMs are encountered through conversational patterns that invite person-directed responses. They are also human-made systems, so design-directed knowledge is relevant. The problem is that neither person-directed knowledge nor ordinary design-directed knowledge gives us the right level of access to the order manifested in generated text. That should become the introduction’s governing thought. The current “two temptations should be resisted” framing is too external and too tidy. It makes the paper sound as if it already knows its answer before the analysis begins. The better introduction should set up a diagnostic problem: LLMs look appreciable under familiar categories, but those categories mislocate the relevant order. Also, the structure described in the introduction no longer matches the draft. The intro still says Section 4 argues against design appreciation and Section 5 introduces semiotic physics, but the uploaded draft has design appreciation inside Section 3 and semiotic physics as Section 4. That mismatch needs to be fixed only after the final architecture is settled. ## 3\. Section 1 needs a cleaner endpoint Section 1 is doing three things: Carlson on correct appreciation, design appreciation, and order appreciation; then person appreciation. The Carlson part mostly works. The weak part is the transition to persons. At the moment, the person discussion feels like a miniature literature review inserted at the end of the section. It should instead do one controlled job: establish the minimum conditions for person-directed aesthetic appreciation. The paper does not need to decide whether person appreciation is its own category, a kind of order appreciation, or a kind of self-design appreciation. That debate is more elaborate than the paper needs. The point needed for later is narrower: Person-directed aesthetic appreciation requires more than recurring behavioral regularities. It requires a subject whose dispositions, projects, evaluative outlook, and history can be grasped across time. The relevant knowledge is biographical, interactional, or narrative in a strong sense. Without that temporal and agential background, the predicates of person-aesthetic appreciation lose their ordinary target. That would give Section 3 a much firmer basis. The end of Section 1 should not say, “Here are several possible ways to extend Carlson.” It should say: “For the purposes of this paper, person-directed appreciation will mean appreciation guided by knowledge of a temporally extended subject.” That is enough. ## 4\. Section 2 should be rebuilt around the question of object-identification Section 2 has many of the right ingredients, but its governing question is blurred. The opening should inherit Carlson’s first recommendation: appreciate things as what they are. So the section should answer: *what are LLMs, considered as possible objects of appreciation?* I think the order should be something like this: First, begin from the encounter: we meet LLMs through generated text, especially through responses and exchanges. This gives us three apparent objects: output, chat, and model. Second, explain continuation from context. Generated text is not a static product with all its relevant form specified in advance; it develops token by token, with each step altering the context from which the next step is drawn. Third, explain training. The system can generate in this way because training has produced sensitivities to textual regularities. This is where the contrast with ordinary designed artifacts should be planted, very gently: the designer fixes architecture, data regimes, objectives, and deployment conditions, but the fine-grained generative profile is acquired through training. Fourth, explain post-training and deployment. The system users encounter is shaped by further procedures and interface conditions, which alter what kinds of continuations are more or less likely. Fifth, only then introduce the three scales: output, chat, model. At that point they will no longer come out of nowhere. They will be the three scales at which the trained order becomes available to appreciation. So I would not merely polish Section 2. I would reorder it. Its task is to produce the object that the later sections will test against person-directed, design-directed, and semiotic-physics-directed knowledge. ## 5\. Section 3 has the right ambition but the wrong internal proportions Section 3 currently handles both person appreciation and design appreciation. That may be acceptable, but only if the section is clearly framed as testing two familiar forms of Carlsonian knowledge. At the moment it reads as if it is moving through a sequence of positions because the paper needs to cover them, rather than because each one is a failed attempt to answer the same question. The person half should probably be organized like this: 1. LLMs invite person-directed response because they appear in conversation. 2. The fictionalist route says that this person-directed stance can be treated as make-believe. 3. That does not solve the Carlsonian problem, because the object we are appreciating is not a fiction designed to prescribe imagining a character; it is a trained system producing text in an interaction. 4. The thin-agency route says that LLMs may count as intentional systems in a weak sense. 5. Even if that is granted, thin agency does not provide the temporal and biographical structure required by person-directed aesthetic appreciation. 6. Post-training explains why person-like response profiles appear, but that explanation redirects us away from persons and toward trained regularities. The design half should be shorter and sharper. It should not try to give a full theory of design aesthetics. It needs only this much: design appreciation is appropriate where aesthetic attention is guided by knowledge of function, specification, constraints, and fit. LLMs do allow some such appreciation. Interface design, training aims, safety constraints, and product functions are relevant. But that does not exhaust what is aesthetically salient in generated text. The learned order manifested in outputs and chats is not adequately explained as the realization of a determinate design specification. The Olah and Pollock material should be used cautiously. Olah helps mark the difference between specifying the conditions of training and specifying the resulting profile. Pollock helps show that Carlson can allow hybrid cases where human agency and non-intentional forces jointly shape appreciable order. But neither should become an excursus. Together they should support one transition: LLMs are artifacts, but their most relevant generated order calls for knowledge of trained regularities, not only knowledge of design aims. ## 6\. Section 4 needs to define semiotic physics much earlier This is probably the most urgent structural fix. The draft currently delays the definition of semiotic physics until after mechanistic interpretability, geology analogies, metasemi, Picca, and continuation talk. That leaves the reader without a stable target. The section should open with a provisional definition. Something like: *Semiotic physics, as used here, is an account of the regularities by which trained continuation systems propagate signs: how prompts, genres, registers, concepts, roles, and prior textual choices make some continuations more likely than others as a generated text develops.* Then the section can ask why this is the right kind of knowledge for order appreciation. That gives the mechanistic interpretability comparison a clear role: mechanistic interpretability may explain internal mechanisms, but the paper needs a middle-level account that connects production to the order available in generated language. The metasemi and Picca quotations should then support the definition rather than gradually lead the reader toward it. In other words, give the reader the concept first, then show how the sources help articulate it. The “physics” objection also needs to be handled less defensively. The issue is not whether the metaphor is literally true. The issue is whether the abstraction is disciplined. Semiotic physics is acceptable if it picks out regularities in sign propagation without attributing personhood, intention, or literal natural lawhood to the system. ## 7\. The old final section should not be reused as it stands The old Section 6 has a good underlying idea: outputs, chats, and models are different scales of appreciation. But it currently does too much theoretical work that should already have been done by Section 4. It also leans heavily on analogies: tree/forest/biosphere, game physics engines, geological exposure, and so on. Some of those analogies may survive, but the final section should probably become more demonstrative and less explanatory. The final section should answer: *once semiotic physics is in place, what does it actually let us appreciate?* That means it should be organized around acts of aspection at each scale. For outputs: what does semiotic physics tell us to notice in a single response? Path-dependence, semantic attraction, register stabilization, modal drift, alignment-shaped phrasing, local coherence, and failures of continuation. For chats: what becomes visible only over many turns? Context accumulation, conversational inertia, uptake, forgetting, repair, user steering, and the emergence of a shared local vocabulary. For models: what becomes visible only across many outputs and chats? A model’s response profile, its range of stable modes, its characteristic handling of uncertainty, its default forms of continuation, and the difference between product-constrained behavior and latent generative capacity. The old section’s contrast between the reasoning output and the bee text is promising, but it should probably be used in a more controlled way. The danger is that the bee text becomes the spectacular case and the reasoning output becomes merely dull. The better contrast is this: semiotic physics lets us appreciate both ordinary and strange outputs, but it is especially clarifying where ordinary categories fail. Reasoning outputs show trained order under strong constraints. The bee text shows trained order under relaxed constraints. The point is not that the bee text is aesthetically superior; the point is that it makes the relevant order easier to see. ## 8\. The next move I would make I would not rewrite the whole draft yet. I would do the next stage in this order: First, produce a revised section-level map: one paragraph per section, saying exactly what argumentative work that section must perform. Second, decide whether the design argument stays inside Section 3 or becomes its own section. This matters because the introduction and final section depend on the numbering. Third, rebuild only the transitions: end of Section 1 into Section 2; end of Section 2 into Section 3; person half into design half; Section 3 into Section 4; Section 4 into the final levels section. Fourth, only after that, revise individual paragraphs. My provisional view is that the paper’s best structure is: 1. Carlson: correct appreciation requires object-appropriate knowledge. 2. What LLMs are: trained continuation systems encountered at the scales of output, chat, and model. 3. Why person-directed knowledge mislocates the order. 4. Why ordinary design-directed knowledge is relevant but insufficient. 5. Semiotic physics as the right middle-level knowledge. 6. Outputs, chats, and models as three scales of appreciation. That structure may require splitting the current Section 3. I think that would probably help. The current combined section can work, but the comments suggest that person appreciation and design appreciation are each being rushed because they are competing for space inside one section. "Fifth, only then introduce the three scales: output, chat, model. At that point they will no longer come out of nowhere. They will be the three scales at which the trained order becomes available to appreciation." but you have already introduced thewm?? "First, begin from the encounter: we meet LLMs through generated text, especially through responses and exchanges. This gives us three apparent objects: output, chat, and model." or are they not supposed to be named? Yes, you’re right. I blurred two different moves there. The distinction I *meant* is this: Early in Section 2, the draft can start from the *phenomenology of encounter*: we encounter LLMs through individual responses, through longer exchanges, and through repeated use that lets us notice recurring tendencies of a system. At that early point, I would probably *not* yet present these as the official “three scales” of appreciation. I would just let them appear as familiar facts about use. Only later, after explaining context, token-by-token continuation, training, post-training, and deployment, should the section *name* them as the three scales: *output*, *chat*, and *model*. At that point, the labels do theoretical work. They are no longer just ways users meet LLMs; they are the levels at which trained order becomes available for appreciation. So the revised sequence would be: First: “In ordinary use, LLMs are encountered through responses to prompts, through exchanges that unfold over time, and through repeated encounters in which a system begins to show recurring tendencies.” Then the section explains how this is possible: context, continuation, training, post-training. Then near the end: “We can now distinguish three scales at which this trained order presents itself: the output, the chat, and the model.” That would fix the problem you noticed. The early version should *prepare* the three-scale distinction without making it sound like the framework has already been introduced. The current draft jumps too abruptly into “We can consider the trained system we have described at three scales,” which is why it feels unmotivated. "The early version should prepare the three-scale distinction without making it sound like the framework has already been introduced." it's going to be pretty hamfisted doing it this way i think. "First, produce a revised section-level map: one paragraph per section, saying exactly what argumentative work that section must perform." "Why person-directed knowledge mislocates the order. Why ordinary design-directed knowledge is relevant but insufficient." lets keep them as one section for both for now. easy to change later anyway. "Second, decide whether the design argument stays inside Section 3 or becomes its own section. This matters because the introduction and final section depend on the numbering." PLEASE do not focus on such unimportant things. such things can be fixed in moments. "Third, rebuild only the transitions: end of Section 1 into Section 2; end of Section 2 into Section 3; person half into design half; Section 3 into Section 4; Section 4 into the final levels section." is this actually a good idea of just something you spat out? "almost plainly," no almost about it ALWAYS ALWAYS ALWAYS write plainly when we are working on a paper. ALWAYS. this is one of the most important things needed for my sort of writing and an idea you just cannot keep in your head. PLAINLY! what's the next step then? You’re right. Plainly. No qualifier. The next step is to make a *section-level argumentative map*. Not a rewrite, not a list of local fixes, and not transitions. The point is to decide what each section must *do* before we touch the prose. For each section, I would write one paragraph answering four questions: 1. What result does this section inherit from the previous section? 2. What problem does it now have to solve? 3. What claim must it establish? 4. What result does it leave behind for the next section? That map should cover: 1. Carlson and the knowledge requirement. 2. What LLMs are. 3. Why person-directed and design-directed appreciation are inadequate as primary models. 4. What semiotic physics is and why it supplies the right kind of knowledge. 5. How semiotic physics guides appreciation at the levels of output, chat, and model. This is the right next step because most of the inline comments in the draft are really complaints about argumentative placement: claims arriving too early, definitions arriving too late, examples doing unclear work, and sections failing to hand a clear result to the next one. So I would next produce that map in prose, section by section. After that, we can test whether the draft’s current sections match the map, and only then decide which paragraphs need to be rebuilt. ok Here is the section-level map. I am keeping person-directed and design-directed appreciation in one section for now. ## Introduction The introduction should present the paper’s problem, not its full solution. It should begin from the fact that LLMs are now objects of aesthetic response: users notice their tone, rhythm, fluency, strangeness, repetitiveness, and model-specific tendencies. The problem is that the familiar categories for making sense of such responses pull in different directions. Since LLMs converse, person-directed appreciation is tempting. Since they are made systems, design-directed appreciation is also tempting. The paper argues that both capture something, yet neither identifies the order that is most specific to generated language. The introduction should leave the reader with the question that governs the paper: what kind of knowledge lets us appreciate LLMs as the kind of things they are? ## 1\. Appreciating Design, Appreciating Order This section should establish the Carlsonian constraint. It inherits the introductory problem and gives it a framework: appropriate appreciation depends on identifying the object correctly and using knowledge suited to that object. Design appreciation is guided by knowledge of purpose, making, function, and fit. Order appreciation is guided by knowledge of the forces and regularities that produce visible or otherwise available order. The person-appreciation material should be reduced and sharpened. Its role is only to establish that person-directed appreciation requires knowledge of a temporally extended subject: dispositions, projects, history, commitments, and patterns of response across a life. The section should leave behind three possible forms of appreciative knowledge: design-directed, order-directed, and person-directed. These become the candidates tested in later sections. ## 2\. What LLMs Are This section should answer the object-identification question. It inherits Carlson’s demand that we appreciate things as what they are. Its task is to describe LLMs at the right level of generality for the aesthetic argument. An LLM should be presented as a trained continuation system encountered through generated language. The section should explain continuation from context, path-dependence, training, post-training, and deployment. The key point is that generated text is structured because the system has acquired probabilistic sensitivities to textual regularities, and those sensitivities operate through a changing context. The section should then derive the three scales from this account: output, chat, and model. An output is one bounded continuation; a chat is an extended context in which earlier turns condition later ones; a model is the trained system whose tendencies become visible across encounters. The section should leave behind a clear object for the next section: a made system whose generated order is learned, context-sensitive, and encountered at several scales. ## 3\. LLMs as Persons or Designed Objects This section should test two candidate forms of appreciation against the object described in Section 2. The person-directed route begins from a real phenomenon: users respond to LLMs as if they had personality, voice, or character. The section should consider the fictionalist route and the thin-agency route without allowing either to dominate the paper. The result should be that person-directed appreciation mislocates the order. LLMs can generate person-like patterns, and post-training can stabilize assistant-like response profiles, but these are patterns of text propagation rather than traits of a temporally extended subject. The same section should then turn to design-directed appreciation. LLMs are artifacts, so design knowledge is relevant. We can ask about interface, function, product goals, safety constraints, and engineered affordances. Yet this does not give the primary account of generated order. Designers specify architectures, objectives, training regimes, and deployment conditions; they do not specify the fine-grained order of particular continuations in the way a designer specifies the form of an ordinary artifact. The section should leave behind a negative result with a positive pressure: person-directed knowledge and design-directed knowledge explain why LLMs invite certain responses, but neither gives the right account of the order manifested in generated language. ## 4\. Semiotic Physics This section should introduce the positive account. It inherits the problem left by Section 3: we need knowledge that connects trained production to readable order without treating the system as a person or reducing generated order to ordinary design. The section should define semiotic physics early. In this paper, semiotic physics is the account of how trained continuation systems propagate signs: how prompts, genres, registers, concepts, roles, and prior textual choices make some continuations more likely than others as text develops. The mechanistic-interpretability contrast should then be used only to place semiotic physics at the right level. Mechanistic interpretability studies internal mechanisms; semiotic physics gives a middle-level account of generated linguistic order as it appears to readers. The section should also control the word “physics”: the term names regularities in sign propagation, not literal natural laws. The section should leave behind the positive result: semiotic physics is the kind of knowledge that can guide acts of aspection for LLMs. ## 5\. Levels of Appreciation The final section should show what the positive account does. It inherits semiotic physics as the relevant appreciative knowledge and asks how that knowledge guides attention at the scales introduced in Section 2. At the level of output, semiotic physics directs attention to path-dependence, semantic attraction, register, pacing, alignment-shaped patterns, and breakdowns or shifts in continuation. At the level of chat, it directs attention to context accumulation, uptake, persistence, drift, repair, and the user’s role in steering the exchange. At the level of model, it directs attention to stable response profiles across many encounters: favored modes, characteristic handling of uncertainty, default forms of explanation, and the range of behavior available under different conditions. The old final section should be rebuilt around these acts of aspection. The examples should serve that structure. The reasoning-output case can show order under tight constraints; the bee-text case can show order under loose constraints. The section should end by making the paper’s payoff explicit: LLMs become appreciable as trained systems whose semiotic order can be encountered in outputs, chats, and models. Okay, before we go any further we need to stop you doing certain things. Bad habits you have. One that is all over those paragraphs you just gave me is long lists of examples in lieu of substantial content. Never do this. Never ever ever give lists of examples when you're doing philosophical writing with me ever. Okay, it's never done, it's always bad. Okay. The next thing is I appreciate the way you're distilling things here, but we should keep in mind going forward that a bad habit of LLMs is losing detail and losing fineness of grain. I'm making texts more shallow across iterations. Okay, so I appreciate the distillations here, but yeah, be very wary of this because it is something that I know for a fact you struggle with Yes. This is right. The lists are a real problem. They let a paragraph look full while avoiding the harder task of saying exactly what the argumentative relation is. In this paper, that is especially damaging because the whole argument depends on precise relations between object, knowledge, order, and appreciation. So from here on, when a paragraph needs content, I should not add examples to bulk it out. I should state the dependency more clearly. The other point is just as important. Distillation is useful only if it preserves structure. A shorter version that loses the pressure points, qualifications, or explanatory steps is worse than the longer version. So the next step should not be “simplify the map”. It should be to make the map more exact while avoiding example-lists. I think we should redo the section-level map with a different standard: each section gets a statement of the argumentative problem, the claim it must establish, and the unresolved pressure it passes on. No ornamental examples. No shallow summary. No lists standing in for argument. "I think we should redo the section-level map with a different standard: each section gets a statement of the argumentative problem, the claim it must establish, and the unresolved pressure it passes on. No ornamental examples. No shallow summary. No lists standing in for argument." yep. go for it Here is the revised map. I am treating each section as a move in the argument, with attention to what it inherits, what it establishes, and what pressure it passes on. ## Introduction The introduction should begin from the fact that LLMs have become objects of aesthetic response, although the object of that response is hard to identify. The familiar options are unstable. If we begin from conversation, we are pulled toward person-directed appreciation. If we begin from production, we are pulled toward design appreciation. The paper’s problem is that both routes capture real features of the case, yet neither gives a full account of the order that appears in generated language. The introduction should therefore set up the question in Carlsonian terms: what kind of thing is an LLM, and what kind of knowledge makes its appreciable order available? ## 1\. Appreciating Design, Appreciating Order This section should give the paper its rule of method. Carlson’s claim is not being used as background ornament. It fixes the constraint that governs the whole argument: aesthetic appreciation must be guided by knowledge suited to the object appreciated. Design appreciation and order appreciation matter because they offer two different ways of relating knowledge to form. In design appreciation, knowledge of making and function explains why the object has the form it has. In order appreciation, knowledge of productive regularities explains how order can appear without being the execution of a plan. The material on persons should be made subordinate to this same constraint. Its role is to show that person-directed appreciation, where it is appropriate, depends on knowledge of a subject whose patterns of response belong to a life. The section should leave the paper with a controlled set of possible routes: LLMs might be approached through person-directed knowledge, design-directed knowledge, or order-directed knowledge. The next sections ask which route fits the object. ## 2\. What LLMs Are This section should identify the object before testing those routes. It should inherit Carlson’s constraint and ask what must be true of LLMs if we are to appreciate them as what they are. The answer should be given at the level needed by the aesthetic argument: an LLM is a trained continuation system whose outputs develop from context. The section should explain this slowly enough for the later argument to use it. Generated text is path-dependent because each produced token changes the context from which the next token is drawn. The path is structured because training has made the system sensitive to textual regularities. The user-facing system is further shaped by post-training and deployment, so the encountered object is a trained system operating under conditions that make some forms of continuation more available than others. The section should then introduce output, chat, and model as scales internal to this account. They are not three separate objects added to the theory. They are three ways in which the trained system’s order becomes available. This section should leave Section 3 with a precise target: a made system whose generated order is learned, context-sensitive, and temporally extended in interaction, without yet being the expression of a life. ## 3\. LLMs as Persons or Designed Objects This section should test two familiar forms of knowledge against the object just described. The person-directed route should begin from the actual pull of the case. LLMs produce language in interaction, and this makes person-like appreciation tempting. The section should not treat this temptation as a simple error. It should ask whether the regularities users respond to can be understood as traits of a subject. Mallory’s fictionalism and Frankish’s thin agency can then be treated as two attempts to preserve something person-like without attributing ordinary personhood. The result should be precise: these accounts may explain why person-like language is intelligible in use, but they do not supply the background required for person-directed aesthetic appreciation. A response profile is not yet a character; a conversational pattern is not yet a life. The design-directed route should then be tested under the same Carlsonian pressure. LLMs are made systems, so design knowledge cannot be excluded. The question is how far that knowledge reaches. It explains the conditions under which the system is built, trained, aligned, and offered to users. It also explains some aspects of the user-facing profile. Yet the order of a generated continuation is not specified in the way the form of an ordinary designed object is specified. It is produced by a trained system whose internal organization and output tendencies are acquired through exposure, adjustment, and post-training pressure. The section should not conclude that design appreciation is irrelevant. It should conclude that design appreciation is incomplete when the object of attention is the order manifested in generated language. That leaves the pressure for Section 4: we need a kind of knowledge that tracks trained order as it appears in text. ## 4\. Semiotic Physics This section should answer the pressure left by Section 3. It should define semiotic physics at the start, before comparing it with other approaches. Semiotic physics, for this paper, is knowledge of how a trained continuation system propagates signs through context. It concerns the regularities by which earlier linguistic material changes the space of later continuation. That definition should then be connected back to Carlson: this is the proposed order-directed knowledge for LLMs. It does for generated text what geology does for a landscape in Carlson’s account: it makes a produced order intelligible at the level at which the appreciator encounters it. The mechanistic-interpretability material should be used only to locate this level of explanation. Mechanistic interpretability may explain internal operations of the system, but the aesthetic question concerns the order available in generated language. Semiotic physics is pitched between internal mechanism and person-level interpretation. It connects trained production to readable pattern without treating the model as a speaker with a life and without reducing the generated text to the direct execution of design. The section should also handle the word “physics” carefully. The term is useful only if it is restricted to regularities of sign propagation. The section should leave one result: semiotic physics supplies the relevant knowledge for order appreciation of LLMs, because it identifies the forces or pressures that make generated text develop as it does. ## 5\. Levels of Appreciation The final section should show how the positive account works. It should inherit the claim that semiotic physics supplies order-directed knowledge, and it should ask how that knowledge guides appreciation at the scales already identified in Section 2. At the level of an output, the object is one bounded continuation. Semiotic physics tells us to attend to how the text develops from its prior context, how it stabilizes a direction, how it sustains or loses a register, and how local choices constrain later ones. At the level of a chat, the object is an extended interaction in which context accumulates and earlier turns continue to shape later ones. Semiotic physics tells us to attend to the exchange as a developing configuration, shaped by the user’s interventions and by the model’s learned tendencies. At the level of a model, the object is the trained system considered through patterns that become visible across encounters. Semiotic physics tells us to treat the model’s apparent feel as a stable profile of continuation rather than as a personality. The old final section has the right scale distinction, but it should be rebuilt so that each scale is governed by an act of aspection. The cases should not be decorative. The reasoning-output case should show what trained order looks like under strong conversational and alignment constraints. The bee-text case should show what trained order looks like when those constraints are loosened. Their role is to make the same point from two sides: semiotic physics allows order to be seen both where generated language looks ordinary and where it looks unstable. The final section should therefore give the paper its payoff. LLMs can be appreciated as trained systems whose generated order is available at the scales of output, chat, and model. That appreciation is not person-directed appreciation under another name, and it is not exhausted by design appreciation. It is order appreciation applied to a new kind of artifact.