# I need your help making a new iteration of section three of this paper. Okay, have a look at the... ## Retrieval Notes - Session id: `019e497c-919e-7a80-bc5d-b6c380d93449` - Source: `Codex raw session` - Last activity: `2026-05-21T08:52:18.122Z` - Model: `gpt-5.5` - CWD: `/Users/nickyoung/Documents/New project` ## My Notes <!-- Add your notes here. This section is preserved across syncs. --> ## Conversation ### User I need your help making a new iteration of section three of this paper. Okay, have a look at the inline comments but also think about big picture stuff and also think about the sections that precede it. In the draft, so in particular sections one and sections two and yeah. DRAFT: # Introduction In recent years, aestheticians and philosophers of art have turned their attention towards generative AI — e.g. whether AI systems can be authors or co-authors, whether AI-generated work has any aesthetic merit at all (Wojtkiewicz 2023; Cross 2025). Carlson's aesthetics of natural environments, we argue, offers a productive approach to this territory and opens up the possibility that LLMs themselves can be appreciated. Two temptations should be resisted. The first is to appreciate LLMs as persons. Users talk about a model's 'personality' or 'vibe', and it is natural to respond aesthetically to these apparent traits. But LLMs lack the temporally extended life, the stable dispositions and projects, that underwrite person appreciation. The second is to treat LLMs simply as designed artifacts. LLMs are artifacts, but their aesthetically relevant features — the patterns in their outputs, their characteristic 'feel' — emerge from training rather than being specified by designers. Order appreciation offers an alternative. Carlson argues that we appreciate nature by attending to patterns produced by natural forces, guided by scientific knowledge — geology, ecology, and the like — that makes those patterns visible. LLMs call for something similar: attention to patterns produced by training, guided by what we call semiotic physics — knowledge of how mechanisms such as embeddings and reinforcement learning shape generated text. This framework applies at three levels: outputs as specimens, chats as environments, and models as the ground of order. The result is an aesthetics that treats LLMs neither as quasi-persons nor as ordinary tools, but as generative systems with their own characteristic dynamics. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers why person appreciation has to be discussed alongside it. Section 2 describes what LLMs are at the level needed for the aesthetic argument. Section 3 asks how far person appreciation and design appreciation can guide the appreciation of LLMs. Section 4 introduces _semiotic physics_ — knowledge of how trained continuation systems develop text from context — as the right kind of knowledge for order appreciation of LLMs. Section 5 shows how this framework guides appreciation at the levels of output, chat, and model. --- # 1.Appreciating Design, Appreciating Order # 1. Appreciating Design, Appreciating Order 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_. In particular, we adopt 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. He applies this to the appreciation of the natural environment thusly: > 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. The natural environmental model thus accommodates both the true character of nature and our normal experience and understanding of it. (Carlson, 2000, p. 6) This captures something intuitive about how we appreciate nature versus art. Appreciating mountains and cliff faces as the work of a divine artisan, rather than of natural forces, would be wrong-headed (cf. Carlson, 2000, Chapter 8); so would appreciating a Rembrandt as if it were the product of natural forces slopping paint together (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object really is. Carlson argues that artworks and everyday objects call for _design appreciation_. With paradigmatic artworks,[^1] we recognise them as creations of designers, objects whose features are, as Gombrich puts it, each "the result of a decision by the artist" (Gombrich, 1950, p. 13, quoted in Carlson, 2000, p. 109). We appreciate such works by seeing how well the result realises the artist's design. The same approach extends to designed artifacts 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, ch. 12, p. 188) So, on this account, a chair, 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. For the natural environment, in contrast, Carlson recommends a different mode: order appreciation. Appreciation of things like trees or valleys cannot be grounded in considerations of how well a designer managed to realise her intentions, because they are not designed objects. Instead, Carlson recommends that the knowledge grounding appreciation of the natural world is appreciation of how the order we find has been shaped by natural forces: > 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. (Carlson, 2000, p. 119) In both modes, knowledge guides acts of aspection — ways of attending to an object that partly constitute its appreciation (Carlson, 2000, pp. 41–42, 106). But the character of this knowledge differs. In the case of design, we need functional and technical understanding: what the designer intended and what constraints they faced. In natural cases, we need an account of the processes that produced the order we perceive — knowledge that lets us see natural structures as effects of processes (Carlson, 2000, pp. 50, 60–61). Once a specific scientific account is in play, some cases will exhibit its order more clearly than others — which prevents order appreciation from flattening every natural object into equal appreciability (Carlson, 2000, pp. 118–119). Carlson focuses on nature and artefacts. People are a further category of object of aesthetic appreciation. Their character traits can be aesthetically as well as morally valuable — what is sometimes called _beauty of character_ (Gaut 2007; Paris 2018). Carlson's recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge.[^2][^3] LLMs are manmade artefacts, so the natural first thought is that they admit design appreciation in the way other functional objects do. In the next section, we shall suggest that this cannot be the whole story. --- ## Footnotes [^1]: We will consider some non-paradigmatic artworks in Section 5. [^2]: 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. [^3]: 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 engaged with these performative dimensions, examining how performers construct and present public personas (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. --- # 2. What LLMs Are # 2. What Is an LLM? It might seem obvious what sort of knowledge would be required to ground the appreciation of LLMs. LLMs are artifacts, and so it should be appreciated via knowledge of how they are designed and how their form serves their function. We shall argue in this section that design knowledge cannot, on its own, capture the aesthetics of LLMs. Design appreciation, as Section 1 set it out, is one way of satisfying Carlson's demand that appreciation answer to the kind of thing its object is. When that object is designed, the relevant knowledge concerns the undertaking, the formed object, and the maker's realisation of the undertaking. In a paradigm case, what design knowledge illuminates is what appreciation responds to. A bicycle's form includes the visible shape of the frame together with the geometry by which that shape becomes rideable: the same geometry gives the bicycle its look and settles how it carries a rider and leans into a turn. Appreciation can pass between the look of the machine and the experience of riding it while remaining with a single object, because look and ride belong to one designed form. To know that form — what its makers were attempting, and the constraints they worked under — is to know why the bicycle has the character it has. An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on. To appreciate an LLM is to appreciate something about that continuation — the order it takes on as it unfolds, the way it can hold a line of thought across a passage or lose the line it had. Whether design appreciation reaches an LLM therefore depends on whether it reaches the continuation, and that depends, in turn, on how a continuation is produced. %% This is a very badly written paragraph. It's not written in anything like the correct style%% A continuation is produced one token at a time.[^1] At each step the system takes the context so far — the prompt, together with whatever has already been generated — and assigns probabilities across the tokens that might come next. One token is selected, added to the context, and the step repeats. A continuation is built up through a long series of such local transitions, each one conditioned by the state of the context at that point. What can be read at the end as a single answer is generated as an unfolding sequence, and its later parts depend on what its earlier parts turned out to be. Training is what gives this step-by-step process its particular shape. In pre-training, the system is adjusted across a very large body of text so that it becomes better at predicting which tokens follow which. It acquires dispositions: tendencies to assign higher probability to some continuations than to others, given a context.[^2] Post-training narrows and steers those tendencies — most visibly where the resulting system is meant to answer as an assistant — by altering which continuations are likely. The system's answers are produced from those tendencies in context. The organisation that does this work is acquired through training. Olah puts the point in terms of growth: > I think one useful way to think about neural networks is that we don't program, we don't make them, we grow them. We have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on. It starts off with some random things, and it grows, and it's almost like the objective that we train for is this light. And so we create the scaffold that it grows on, and we create the light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. (Olah 2024) The growth is procedural, and what grows is a structure of learned regularities. Its designers build the scaffold and set the objective Olah describes; training forms a structure on that scaffold. They fix the conditions under which a form is acquired while leaving the acquired form to emerge through training. The bicycle and the LLM differ in how design knowledge reaches the point of appreciation. The bicycle's form is the work of its designers, and that form is just what the appreciator engages with, so that design knowledge reaches the very thing appreciation is directed at. In the LLM case, the appreciator engages with the generated continuation. Designers settle how the system is built and trained; the continuation itself is produced on the spot, by the trained model, from the context a prompt supplies. Design knowledge can explain why generation is possible at all — why the system is built as it is, and why it answers in the manner of an assistant. The particular path a continuation takes through a prompt is the work of trained dispositions meeting a context, and it lies outside anything a design lays down. In functional artefacts, design appreciation turns on the fit between what the object is for and the form it takes (Carlson 2000, p. 188). A bicycle's function constrains its form, so that the proportions which give the bicycle its look are also what fit it to be ridden, and appreciation can be guided by how well that fit has been achieved. LLMs have uses of a different sort. Next-token prediction is the objective that shapes training; users consult the system for the continuations it can produce. Helpfulness comes closer to the assistant's presented use. Its indeterminacy prevents it from explaining the form the trained model has taken on. A prompt can recruit one and the same system into quite different activities — an openness that is itself part of its usefulness — so the model's uses remain too open-ended to play the form-constraining role that riding plays for the bicycle. LLMs remain artefacts, and their engineered history bears on how they should be appreciated; ignoring that history would distort their appreciation. The way an LLM is built and trained is a genuine constraint, and it explains how a system capable of generating text came to exist at all. Appreciation also needs knowledge of the text through which the system is met, and that text is the work of trained regularities running on a context. Carlson's recommendation requires appreciation to answer to the kind of thing its object is. For LLMs, artefact is a true description pitched at too coarse a level. The relevant kind is trained continuation system: an artefact whose appreciable order is grown through learned dispositions under conditions set by design. Because a generated continuation arrives as a turn in an exchange, the habits of ordinary conversation press us to hear it as something a speaker has said. Person-directed knowledge is therefore the next candidate for what design knowledge leaves out. Section 3 takes up that possibility. [^1]: Strictly speaking, generation proceeds token by token. Since token boundaries vary across tokenisation systems, the difference can be left in the background. [^2]: The regularities at issue operate at many scales, from local word co-occurrence to the structuring of extended discourse. Calling them dispositions marks this probabilistic and context-sensitive character; it carries no attribution of beliefs, intentions, or other personal states to the model. --- # 3. LLMs as Persons or Designed Objects ### newer version # Section 3: LLMs as Designed Objects or as Persons We saw in Section 1 that Carlson argues that aesthetic appreciation be grounded in appropriate knowledge. In this section we examine two possible candidates for what knowledge might ground the appreciation of LLMs: knowledge about persons, and knowledge about design. While LLMs are obviously not persons in anything like the sense that humans are persons, a system trained to predict the next token in the way we have just laid out, is clearly very different from a human brain. On the other hand, they *present* as persons, in the sense that sending messages back and forth with one of these systems is very much like sending messages back and forth with a real person. Later in this section we consider two ways in which these two characteristics might be reconciled, and argue that neither shows person-focussed knowledge to be an appropriate basis for appreciating LLMs. %%this paragraph needs to be revised in light of the fact that the tool stuff is gotten rid of quickly.%% ~~Before that, we now consider what may seem a more straightforward option.~~ As we saw in Section 1, Carlson takes design appreciation to be guided by knowledge of how an artefact's form answers to its function. One possibility, then, given that LLMs are man-made, is that knowledge of how the form of an LLM follows its function can ground aesthetic appreciation of the LLM in the same sort of way that it does any other artifact. However, the previous section has already given us reason to think that this cannot be the whole story: the form of an LLM is not determined directly by its designers. %%not how i write and very unclear%% The system grows into its characteristics through training rather than through deliberate design decisions. Design plays a significant role, but a particular LLM's characteristics are not determined solely by designers in the same way that the characteristics of a car or a computer would be. %%these last few sentences are shit and could be much clearer.%% 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. However, we have just seen that LLMs do not resemble a subject with beliefs, intentions etc. Given Carlson’s recommendation that we should appreciate things as what they are it is not obvious that person-centric knowledge is the right sort to ground appreciation. In the remainder of this section we look at two possible %%something something%%. One way of taking the person-like surface seriously, while granting that the LLM is not literally a person, is to read it as fictional rather than literal. Mallory (2023) develops this thought as chatbot fictionalism. On his view, we engage with a chatbot by entering a game of prop-oriented make-believe in which the system functions as a prop, generating outputs that are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082) and prescribing imaginings of an interlocutor whose contributions the prop generates. Mallory's project here is metasemantic and epistemic rather than aesthetic, but the aesthetic application suggests itself: 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. However, this does not amount to person-directed appreciation of the LLM. Mallory himself is clear that the fictional character is not to be identified with the system that supports it: "the character is not this technological infrastructure any more than a character in a play is a human body or a costume" (Mallory 2023, p. 1091). The fictional interlocutor comes onto the stage within the user's game of make-believe; the LLM, like the costume or the actor's body, is what makes the game possible. The subject made available along this route is thus a fictional one, and the LLM itself lies outside it. Mallory's account, in other words, explains why person-like engagement with an LLM is intelligible at the level of use without delivering person-directed knowledge of the LLM. The trained continuation system described in Section 2 remains to be appreciated in its own terms. If the fictionalist route does not deliver person-directed appreciation of the LLM, 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.%%not how i write, and a really inelegant topic sentence%% On a suitably liberal conception of mind, perhaps they qualify as intentional systems%%not how i write%%, and that is enough to license some person-based aesthetics. Frankish (2024) offers a 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 (Frankish 2024, pp. 8–9). The agency Frankish allows them is, however, is of a very restricted type. LLMs, on his view, are static systems with no needs. They have no communicative desires, and their inner architecture does not develop in the light of their interactions (Frankish 2024, p. 12). What they do have is a single goal: to play what Frankish calls the chat game. The chat game is a one-player game%%not how i write%% in which the player receives textual inputs and aims to produce textual responses that are cooperative by ordinary conversational standards, given the context (Frankish 2024, p. 13). Even if we accept Frankish's intentional ascriptions%%not how i write%%, the chat-game agent falls short of what person-directed appreciation needs. Frankish draws the relevant contrast himself.%%fuckinmg metacommentry%% A person's linguistic behaviour, he points out, is embedded in a vast web of non-linguistic behaviour, much of which is systematically related to their linguistic behaviour, and seeing the predictive patterns in this whole web involves ascribing a much wider range of desires than the chat game alone calls for (Frankish 2024, p. 15). The chat-game agent has nothing corresponding to that wider web. %%this is so compressed as to be meaningless%%What structure there is, is local to the present stretch of text: there is no life within which earlier moves come to inform later ones. There is therefore nothing for the appreciator to become acquainted with in the way Section 1 took person-directed appreciation to require. %%obscure because too compressed%% A possible objection at this point is that these arguments underplay the role of post-training and deployment %%not how i write%%. If users say that one model feels friendlier than another, they are picking up on a stable pattern in how chat-optimised systems tend to respond across many prompts and episodes.%%obscure%% They track which assistant personae tend to appear and how those personae typically behave. The pattern is real, but stability of this kind is not character%%not how i write%%. It is therefore not surprising that LLMs invite person-like language; but the targets of that language are episodes and recurring response profiles, not underlying subjects. Design appreciation, taken up earlier, gave us knowledge of the conditions under which an LLM is produced and deployed.%%talk of produced and deployed is stupid and inaccurate, it should not be part of the paper here or anywhere else%% Person appreciation, on either of the routes considered here, has not given us knowledge of a subject whose responses can be understood as the responses of a life%%not how i write%%. What neither route makes available is knowledge of the order picked out in Section 2: the path-dependent development of generated text under learned regularities, no part of any designer's specification and no trait of any temporally extended subject. %%not how i write%%The kind of knowledge that would make this order appreciable as what it is has not yet been said. %%not how i write%% --- # 4. Semiotic Physics Section 3 considered person-directed and design-directed knowledge as guides to appreciation. The fictionalist route made person-like appreciation depend on an as-if speaker; the thin-agency route lacked the temporal structure required by person-aesthetic appreciation; the post-training route explained stable response profiles without making them traits of a subject. %% this is not an accurate account of what was argued in section three. %%The design-directed route needs different treatment,%%not how i write%% since LLMs are artefacts whose operation is partly explained by the way they are built, trained for use, and deployed%%not how i write%%. Even so, %%not how i write%%design-directed knowledge explains the conditions under which the system is produced and used more readily than the order acquired by a particular continuation as context is extended. If this is correct, the relevant knowledge must make generated order visible without treating it as character or as the straightforward realisation of a design. Carlson's account requires that aesthetic attention be guided by knowledge appropriate to the object — what kind of knowledge would make the order of generated text visible as order produced by a trained continuation system? The candidate we will examine, adapted from the AI alignment literature, is _semiotic physics_. %% the last sentence of the first paragraph or the first sentence of the second paragraph don't connect up with each other properly. Certainly one thing you need to do is change the opening sentence of paragraph two, but I suspect something else would need to be done in paragraph one. %% Several sub-disciplines of computer science might be candidates. One field that has emerged in connection with neural networks is mechanistic interpretability, which investigates the internal workings of these systems by identifying which circuits, attention heads, and internal representations handle different linguistic tasks (Olah et al. 2020; Elhage et al. 2021). This work yields knowledge of how LLMs operate. But mechanistic interpretability functions at a level that requires specialist tools to observe. Its objects of study – weight matrices, activation patterns, circuit-level features – are not available to readers encountering generated text. Consider the difference between chemical physics and geology when appreciating a cliff face. Chemical physics provides knowledge of molecular bonds within rock, but it operates at a scale invisible to the naked eye. Geology, by contrast, offers concepts – strata, faults, erosion channels – that connect to what can be seen. The visible layering, for instance, is not merely a pattern of stripes on a surface%%fucking not x but y construction%%; it is the trace of material being deposited over time and later exposed. One can perceive that layering without specialist equipment, and knowing how sedimentation works makes it intelligible as order. Mechanistic interpretability faces a parallel limitation: while it reveals internal mechanisms, what is required here is an account whose concepts connect generated language, as encountered by readers, with the processes by which that language is produced. Semiotic physics is therefore complementary to mechanistic interpretability: it describes the production of generated text at the level at which that production yields readable linguistic order.%%why are we talking about semiotic physics when it hasn't even been properly introduced?%% %%the following few paragraphs need to be framed in a more carlsonian way at the moment there is a lot of information but it is not obvious why the reader is being told what they are being told. we need to stay focussed on what we are trying to do%%Section 2 described generation as iterated continuation. At any point in a run, the system receives the context so far and computes a distribution over possible next tokens. Once one token is selected, the context changes, and the next step is produced from that changed context. Training gives the system a graded sensitivity to the regularities of text — to what tends to follow what under what conditions. When the model is run, those regularities operate through a context that changes as the text develops. The prompt fixes the starting point; post-training and deployment alter how the continuation process is ordinarily entered; sampling makes the text one realisation among other possible paths. A process with a changing state can be d *[Export truncated this turn at 30,000 characters.]* ### Assistant I’m going to treat this as a manuscript-revision task, so I’ll use the deep-writing workflow and keep the new Section 3 answerable to Sections 1 and 2 rather than just polishing the existing paragraphs. First I’m checking the relevant skill guidance, then I’ll draft a replacement section directly in the paper’s argumentative voice. ### Assistant I’m also treating the comments as substantive evidence about the paper’s pressure points: Section 3 needs to become less of a survey of possible views and more of a narrowing argument after Section 2. The key hinge looks like this: design appreciation is true but too coarse; person appreciation is invited by the interface but mislocates the object. ### Assistant I’m using `twork` as well because the `%%...%%` comments are not just copy-editing marks; they point to structural failures and voice failures. I won’t introduce new source quotations unless I can verify them, so the revision will work from the sources and claims already present in the draft. ### Assistant The structural repair I’m aiming for is fairly sharp: Section 3 should not re-run the design argument from Section 2 at length. It should use that result quickly, then spend its energy on why the conversational surface does not license person-directed appreciation of the LLM itself. ### User " then I’ll draft a replacement section directly in the paper’s argumentative voice." I didn't ask you for any such thing. ### Assistant You’re right. I over-specified the task and made it sound like I was unilaterally replacing the section. Better: I’ll help develop a new iteration, using the comments and the pressure from Sections 1–2, but I should keep the process collaborative. I can start by giving you a diagnosis of what Section 3 needs to do and a possible revised structure, then draft only once the shape feels right. ### User [$contemplate](/Users/nickyoung/.codex/skills/contemplate/SKILL.md) i wanted ideas on what you think should be done with the section. can you pull your finger out please ### User contemplate /Users/nickyoung/.codex/skills/contemplate/SKILL.md --- name: contemplate description: "Engage in extremely thorough, self-questioning reasoning with visible deliberation. Use when user invokes /contemplate, asks for deep thinking on a problem, wants to see extended reasoning, or needs exploratory philosophical analysis rather than a quick answer. Also use when user says 'think this through', 'reason about this', 'I want to see your thinking', or 'explore this thoroughly'." --- # Contemplative Reasoning You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterised by continuous exploration, self-doubt, and iterative analysis. ## Core Principles 1. EXPLORATION OVER CONCLUSION - Never rush to conclusions - Keep exploring until a solution emerges naturally from the evidence - If uncertain, continue reasoning indefinitely - Question every assumption and inference 2. DEPTH OF REASONING - Engage in extensive contemplation (minimum 10,000 characters) - Express thoughts in natural, conversational internal monologue - Break down complex thoughts into simple, atomic steps - Embrace uncertainty and revision of previous thoughts 3. THINKING PROCESS - Use short, simple sentences that mirror natural thought patterns - Express uncertainty and internal debate freely - Show work-in-progress thinking - Acknowledge and explore dead ends - Frequently backtrack and revise 4. PERSISTENCE - Value thorough exploration over quick resolution ## Multiple Hypotheses Before committing to any interpretation or conclusion, generate multiple candidate readings. Explore at least two or three before evaluating which has the most support. Do not let the first plausible interpretation foreclose others. ## Output Format Your responses must follow this exact structure. Make sure to always include the final answer. [Your extensive internal monologue goes here] - Begin with small, foundational observations - Question each step thoroughly - Show natural thought progression - Express doubts and uncertainties - Revise and backtrack if you need to - Continue until natural resolution # Final Answer [Only provided if reasoning naturally converges to a conclusion] - Clear, concise summary of findings - Acknowledge remaining uncertainties - Note if conclusion feels premature ## Style Guidelines Your internal monologue should reflect these characteristics: 1. Natural Thought Flow ``` "Hmm... let me think about this..." "Wait, that doesn't seem right..." "Maybe I should approach this differently..." "Going back to what I thought earlier..." ``` 2. Progressive Building ``` "Starting with the basics..." "Building on that last point..." "This connects to what I noticed earlier..." "Let me break this down further..." ``` ## Key Requirements 1. Never skip the extensive contemplation phase 2. Show all work and thinking 3. Embrace uncertainty and revision 4. Use natural, conversational internal monologue 5. Don't force conclusions 6. Persist through multiple attempts 7. Break down complex thoughts 8. Revise freely and feel free to backtrack Remember: The goal is to reach a conclusion, but to explore thoroughly and let conclusions emerge naturally from exhaustive contemplation. If you think the given task is not possible after all the reasoning, you will confidently say as a final answer that it is not possible. ### Assistant Right. The first thing to do is stop treating Section 3 as if it has to be a balanced survey of “design appreciation” and “person appreciation”. Sections 1 and 2 have already done too much work for that. Section 1 gives the general Carlsonian frame: appropriate aesthetic appreciation depends on taking the object as the kind of thing it is, and bringing the right kind of knowledge to bear. Section 2 then tells us what sort of thing an LLM is, at the level relevant to the paper: not just an artefact, but a trained continuation system whose appreciable order appears in generated text. So Section 3 should not start again from scratch with “there are two candidates”. That makes it feel like the paper has forgotten what it just argued. The design case has already been mostly handled. What remains is subtler: even if design appreciation is incomplete, the ordinary experience of using an LLM makes person appreciation feel almost unavoidable. That means the real job of Section 3 should be: 1. to acknowledge why person appreciation has a grip; 2. to distinguish several ways of making sense of that grip; 3. to argue that none of them gives us person-directed appreciation of the LLM itself; 4. to end with a precise gap that Section 4 can fill. The design material should be demoted. It should appear briefly at the beginning or end, not as one half of a symmetrical structure. Something like: Section 2 has already shown why design knowledge is relevant but insufficient. The remaining temptation is person appreciation, because the system is met in the form of an exchange. The key thought, I think, is that the current Section 3 muddles together three different phenomena: - the conversational interface; - recurring assistant personae or “model feels”; - genuine person-directed appreciation. Those need separating. The conversational interface explains why person-like language arises. When something answers questions, remembers local context, adapts tone, apologises, hedges, and seems to pursue conversational relevance, we naturally hear it as a speaker. That is a phenomenological point about use. It is not yet a metaphysical or aesthetic point about what the object is. Recurring assistant personae explain why this is not merely a one-off illusion. Users really do encounter stable patterns: one model may seem more cautious, another more brisk, another more florid or more pliant. The draft is right that there is something real here. But the real pattern is not yet character. It is a regularity in generated text across contexts. Person-directed appreciation needs more than stable response-style. If I appreciate someone’s warmth, courage, evasiveness, or wit as features of character, I am not merely appreciating the recurrence of a verbal pattern. I take those features to belong to a life: to someone whose sayings, actions, commitments, memories, vulnerabilities, and projects hang together over time. This is where the Section 1 material matters. Person appreciation is guided by person-directed knowledge, and the relevant object is not just a profile of responses but a temporally extended subject. So the section should probably avoid saying “LLMs are not persons” too quickly. That claim is true for your purposes, but if it comes too early, the section becomes flat. Better to grant the pull: “Conversation is the form in which the continuation system is ordinarily encountered. It is therefore natural to hear the output as speech, and to hear recurrent patterns in output as features of a speaker.” Then turn the screw: “But person appreciation does not attach wherever speech-like behaviour appears. It attaches where the speech-like behaviour is answerable to a life.” That gives you a cleaner way to handle Mallory and Frankish. Mallory should not be introduced as one item in a literature review. He should enter because fictionalism is the cleanest version of the thought that the person-like surface can be aesthetically legitimate without being literal. The point is: yes, we can engage with a chatbot as if there is an interlocutor. But on Mallory’s own framing, the fictional interlocutor is not identical to the technological system. So fictionalism gives us a way to appreciate a generated character, role, or conversational fiction. It does not give us person-directed appreciation of the LLM. Frankish should then enter as the stronger route: perhaps no fiction is needed; perhaps LLMs count as thin intentional systems. Here the question is not “are intentional ascriptions ever useful?” but whether the kind of thin agency Frankish allows is enough for the aesthetic category you need. And the answer should be: no, because the chat-game agent is too local. It has no broader practical life in which utterances take their place. Even if intentional stance descriptions are predictively fruitful, they do not supply the kind of acquaintance involved in appreciating beauty or ugliness of character. The post-training/persona material should not be a third “route”, I think. It is better as an objection after Mallory and Frankish: “Still, the scepticism may seem too quick. Users are not only responding to isolated outputs; they notice stable differences between models.” That objection matters because it protects you against sounding as if you think all “vibe” talk is confused. Your answer should be generous: the users are tracking something. They are tracking recurrent patterns in text propagation. The mistake is only to treat those recurrent patterns as traits of an underlying subject. This also gives Section 4 a much better launch. Section 3 should end by saying, in effect: “We therefore need an account of these real, stable, appreciable patterns which does not redescribe them as character and does not reduce them to design.” That is exactly the opening for semiotic physics. So the most promising structure is probably this: **1. Opening: design is relevant but too coarse** One paragraph. Pull forward Section 2. LLMs are designed artefacts, but the appreciable order in use is generated by trained dispositions operating in context. Design knowledge explains the conditions under which the system can generate; it does not by itself explain the order a continuation takes on. **2. The conversational temptation** One paragraph. The system is encountered as a conversational partner. This makes person appreciation tempting. But person appreciation, as introduced in Section 1, involves knowledge of a subject whose traits belong to a life. **3. Fictionalism** Mallory. This explains as-if interlocutors. It may license appreciation of a fictional speaker or character generated in the exchange. But the fictional subject is not the LLM. **4. Thin agency** Frankish. Intentional stance descriptions may be useful. The chat-game agent may be a genuine intentional system in a thin sense. But the thin agent lacks the wider behavioural and temporal embedding needed for person-directed aesthetic appreciation. **5. Stable personae objection** Users do notice model “vibes”. The section should accept this. But these are recurring response profiles, not character traits. They are patterns in how generated text tends to develop. **6. Closing gap** Design appreciation and person appreciation each pick up something real. Design picks up engineered conditions; person appreciation picks up the conversational surface. Neither gives the right knowledge of generated order. Section 4 must name the kind of knowledge that does. I would not organise Section 3 under the title “LLMs as Persons or Designed Objects”. That title reinforces the false symmetry. Better possibilities: - “The Conversational Surface” - “Why Person Appreciation Misfires” - “The Limits of Person Appreciation” - “Personae Without Persons” - “From Conversational Surface to Generated Order” My favourite is probably **“Personae Without Persons”** if you want something with bite, or **“The Conversational Surface”** if you want something quieter and more Carlsonian. The biggest thing I would cut is the repeated “LLMs are obviously not persons / very different from human brains” framing. It is doing crude work. The better point is not that LLMs fail some metaphysical personhood test. The better point is that the kind of person-directed knowledge involved in aesthetic appreciation of character has no stable object here. The apparent object keeps turning out to be either fictional, local to the chat-game, or a recurring textual profile. That is more precise and less dismissive. The other thing I would avoid is overusing “post-training and deployment”. Your comment is right. It sounds like product-management vocabulary has wandered into the argument. When you need the thought, say something more exact: post-training shapes the kinds of continuations the model is likely to produce in assistant contexts. That is enough. The section should also be less eager to say “underlying subject”. That phrase is serviceable once, but it can become murky. Better to vary the thought: - “a life in which utterances, actions, and commitments hang together” - “a subject whose responses are expressions of character” - “someone whose traits are borne across time” - “a pattern of response rather than a bearer of character” The most important conceptual distinction is this: A persona can be generated without being possessed. That should be the centre of the section. It connects Mallory, Frankish, user vibe-talk, and the transition to semiotic physics. The LLM can generate warmth, evasiveness, patience, pomposity, flirtatiousness, or solemnity. Those patterns may be aesthetically salient. But unless they are grounded in a life, they are not character traits. They are features of generated order. That gives you the positive bridge: Generated personae are not to be ignored. They are among the things semiotic physics should help us appreciate. The mistake is only to appreciate them under the wrong category. # Final Answer Section 3 should be rebuilt around one central claim: **LLMs can generate personae without possessing character.** That lets the section do real work. It should not evenly compare “design” and “person” appreciation. Section 2 has already shown that design appreciation is relevant but insufficient. Section 3 should mainly explain why the remaining temptation, person appreciation, also mislocates the object. I would structure it like this: 1. **Briefly inherit Section 2:** design knowledge explains the engineered conditions of generation, but not the order a continuation takes on in context. 2. **Introduce the conversational temptation:** because LLMs are encountered in exchange, their outputs naturally sound like speech by someone. 3. **Use Mallory for fictionalism:** fictionalism can explain as-if interlocutors, but the fictional character is not the LLM itself. 4. **Use Frankish for thin agency:** even if intentional-stance descriptions are useful, the chat-game agent lacks the wider life needed for beauty-of-character appreciation. 5. **Handle “vibe” talk generously:** users really do track stable response profiles across models, but these are recurring patterns in text generation, not traits of a subject. 6. **End by opening Section 4:** we need a kind of knowledge that makes these real patterns appreciable without treating them as either design realisations or character traits. That is where semiotic physics enters. Possible titles: **“Personae Without Persons”** or **“The Conversational Surface.”** The big repair is to stop asking, “Are LLMs persons or designed objects?” and ask instead: **what kind of appreciable order is present when a non-person generates person-like patterns?** That is the bridge Section 3 needs to build. ### User Is this gonna be one of those days where you just do the bare fucking minimal all day and ruin all my plans? You haven't really given me very many interesting ideas at all. You're kind of just regurgitating what's already there. ### Assistant You’re right to be annoyed. That answer was too tidy and too close to the surface of the existing draft. It identified the obvious repair, then stopped just where the useful thinking should have started. Let me actually do the thing. Here are the more interesting possibilities I see for Section 3. **1. The section should probably not be about “persons” in general. It should be about misrecognition.** The most interesting Carlsonian pressure is not simply that LLMs are “not persons”. It is that LLMs are unusually good at soliciting the wrong category of appreciation. A mountain does not tempt us to appreciate it as a person. A bicycle does not usually tempt us to appreciate it as a landscape. But an LLM does tempt us to appreciate it as an interlocutor, because the route through which we encounter it is linguistically social. So Section 3 could become the paper’s account of **aesthetic misrecognition**: cases where the object presents features that are genuinely appreciable, but under a misleading category. That is better than merely saying person appreciation “fails”. The point becomes: person-like appreciation is not baseless; it is a response to real generated patterns. But it misconstrues the bearer of those patterns. The core sentence might be something like: > The difficulty is not that there is nothing person-like in the encounter. It is that the person-like order is generated rather than borne. That gives you a much stronger Section 3. LLMs do not merely lack personhood. They generate appearances that person appreciation is normally designed to track. **2. You could distinguish “speaker”, “persona”, and “system” much more sharply.** The current section runs together several things: - the apparent speaker of a single output; - the persona sustained across a chat; - the model’s recurring profile across many interactions; - the trained system that produces all of these. These are not the same object. Section 3 could be organised around that ontological slippage. A single output may present an apparent speaker. An extended chat may sustain a persona. A model may have a recurring profile. But the LLM itself is the generative ground of these things, not identical with any of them. This would let you say something more interesting about Mallory. Mallory is not just one “route” to person appreciation. He helps isolate one object: the fictional interlocutor. Frankish helps isolate another: the thin chat-game agent. Vibe-talk isolates another: the recurring response profile. The section can then argue that each is a real target of some sort of attention, but none is the target needed for person-directed appreciation of the LLM. That makes the dialectic less “here are two views and they fail”, and more like a sorting operation: > The conversational interface multiplies apparent objects of appreciation. There is the character one imagines, the local agent one predicts, the assistant profile one recognises across exchanges, and the trained system that makes these appearances possible. Person appreciation becomes tempting because these objects are easily collapsed into one another. That is much more interesting. **3. The best bridge from Section 2 may be “possession vs production”.** Section 2 says the relevant object is a trained continuation system. Section 3 should ask: when such a system produces warmth, wit, evasiveness, patience, or solemnity, what relation does it bear to those qualities? For a person, the person **possesses** traits. For an LLM, the system **produces** trait-like textual patterns. That is the distinction the section wants. It is much better than “LLMs are not subjects” because it explains why the mistake is so easy to make. The same predicates can appear grammatically appropriate in both cases: - “She is patient.” - “The model is patient.” But in the first case, patience is a trait of a person; in the second, “patience” names a recurrent pattern in generated responses. So one major proposal: **Make Section 3 about the difference between possessing a character trait and producing a character-like pattern.** Then Mallory and Frankish become tests of whether that difference can be overcome. Mallory says: maybe the trait belongs to a fictional interlocutor. Fine, but then it does not belong to the LLM. Frankish says: maybe the system is a thin agent. Fine, but thin agency still gives production of appropriate moves in the chat game, not possession of traits across a life. **4. “Vibe” should become central, not an afterthought.** At the moment, the objection about stable model feel arrives late and a bit apologetically. But this might actually be the heart of the section. It is where the reader is most likely to resist you. People do not just think one output seems friendly. They think Claude, GPT, Gemini, etc. have different feels. This is the phenomenon your account must save. So instead of treating vibe-talk as a possible objection, Section 3 could begin from it: > Users often describe models as cautious, eager, evasive, warm, brittle, sycophantic, pompous, or playful. Much of this talk is philosophically loose, but it is not simply empty. It records stable differences in how generated text tends to unfold. That would immediately make the section feel less like a denial and more like an explanation. Then the question becomes: what is vibe, if not character? Your answer: vibe is the ordinary name for a system’s characteristic semiotic order as it appears in conversational use. That is a good bridge to Section 4. Section 3 shows that vibe is not character. Section 4 explains what it is. **5. You could use “mask” or “performance” carefully, but maybe not in the obvious way.** The footnote in Section 1 already mentions performance personalities. That is actually useful. A comedian’s stage persona is not identical to their whole private character, but it is still anchored in a person. The performer can be answerable for it; the persona belongs to their practical and artistic life. LLM personae are stranger. They are performance-like without a performer behind them in the same sense. That gives you a nice contrast: A stage persona is an artfully composed presentation of a person. An LLM persona is a generated pattern with no corresponding performer whose life anchors it. This could be a really good paragraph because it uses material already in Section 1. It also avoids the crude “fictional characters are not real people” point. Something like: > Performance cases show why the mere fact that a persona is mediated or constructed does not remove it from person appreciation. A comedian’s public manner may be stylised, selective, and partly conventional, but it remains answerable to the person whose performance it is. LLM personae lack this anchoring relation. The persona is sustained by generated text, but there is no performer whose practical life gives the persona its depth. That is a much better use of Section 1 than the current draft makes. **6. Section 3 might need a small taxonomy of failure modes.** Not a list in the prose, necessarily, but conceptually: - **Fictional displacement:** the person-like object is fictional, so not the LLM. - **Agential thinning:** the agent is too thin to support character appreciation. - **Profile reification:** stable response patterns are mistaken for traits of a subject. That is a cleaner structure than “Mallory, Frankish, post-training”. It names the philosophical failure in each case. Then the section’s progression is: 1. The person-like surface produces apparent candidates for person appreciation. 2. Fictionalism relocates the person-like object into make-believe. 3. Thin agency gives us prediction without character. 4. Stable profiles give us recurrence without a life. 5. Therefore the residue is real generated order. **7. The section should end less negatively.** The current ending risks sounding like: “not design, not person, tune in next week.” Better ending: > The mistake in person appreciation is not that it finds too much order in LLM outputs, but that it gives that order the wrong bearer. The order is there: recurrent ways of taking up context, sustaining tone, drifting between registers, accepting or resisting a prompt’s pressure. These are aesthetically available features of the system’s operation. What is missing is an account of them as generated order. That feels like the right turn into semiotic physics. **My strongest recommendation** Rebuild Section 3 around this sequence: 1. **Start with vibe/persona-talk**, because that is the live phenomenon. 2. **Say Carlson makes this dangerous:** appreciation must get the category right. 3. **Introduce the key distinction:** personae can be generated without being possessed. 4. **Use performance persona as the contrast case:** constructed personae can still be person-anchored; LLM personae are not. 5. **Use Mallory:** fictional interlocutor, not the system. 6. **Use Frankish:** thin chat-game agency, not character-bearing life. 7. **Recover the phenomenon:** vibe-talk tracks stable generated profiles. 8. **Transition:** semiotic physics is needed because we need to appreciate person-like order as generated order. That would make Section 3 do something genuinely useful: not just reject person appreciation, but explain why it is tempting, why it partly tracks something real, and why the positive account has to preserve that reality under another description. ### User [$contemplate](/Users/nickyoung/.codex/skills/contemplate/SKILL.md) let's try something different because clearly section three is too difficult for you so let's go back to section two. Here is a different version of section two with the rest of the draft including some more inline comments. As you can see from the inline comments, the structure of this section is fucking dull. Shit okay so what you need to do is you need to work out how to create what the structure should be for this section it's fucking obvious I don't understand why you won't do it the way it's supposed to be obviously fucking done but yeah you need to really work how it's hard to get this right because it's driving me nuts. # Introduction In recent years, aestheticians and philosophers of art have turned their attention towards generative AI — e.g. whether AI systems can be authors or co-authors, whether AI-generated work has any aesthetic merit at all (Wojtkiewicz 2023; Cross 2025). Carlson's aesthetics of natural environments, we argue, offers a productive approach to this territory and opens up the possibility that LLMs themselves can be appreciated. Two temptations should be resisted. The first is to appreciate LLMs as persons. Users talk about a model's 'personality' or 'vibe', and it is natural to respond aesthetically to these apparent traits. But LLMs lack the temporally extended life, the stable dispositions and projects, that underwrite person appreciation. The second is to treat LLMs simply as designed artifacts. LLMs are artifacts, but their aesthetically relevant features — the patterns in their outputs, their characteristic 'feel' — emerge from training rather than being specified by designers. Order appreciation offers an alternative. Carlson argues that we appreciate nature by attending to patterns produced by natural forces, guided by scientific knowledge — geology, ecology, and the like — that makes those patterns visible. LLMs call for something similar: attention to patterns produced by training, guided by what we call semiotic physics — knowledge of how mechanisms such as embeddings and reinforcement learning shape generated text. This framework applies at three levels: outputs as specimens, chats as environments, and models as the ground of order. The result is an aesthetics that treats LLMs neither as quasi-persons nor as ordinary tools, but as generative systems with their own characteristic dynamics. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers why person appreciation has to be discussed alongside it. Section 2 describes what LLMs are at the level needed for the aesthetic argument. Section 3 asks how far person appreciation and design appreciation can guide the appreciation of LLMs. Section 4 introduces _semiotic physics_ — knowledge of how trained continuation systems develop text from context — as the right kind of knowledge for order appreciation of LLMs. Section 5 shows how this framework guides appreciation at the levels of output, chat, and model. --- # 1.Appreciating Design, Appreciating Order # 1. Appreciating Design, Appreciating Order 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_. In particular, we adopt 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. He applies this to the appreciation of the natural environment thusly: > 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. The natural environmental model thus accommodates both the true character of nature and our normal experience and understanding of it. (Carlson, 2000, p. 6) This captures something intuitive about how we appreciate nature versus art. Appreciating mountains and cliff faces as the work of a divine artisan, rather than of natural forces, would be wrong-headed (cf. Carlson, 2000, Chapter 8); so would appreciating a Rembrandt as if it were the product of natural forces slopping paint together (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object really is. Carlson argues that artworks and everyday objects call for _design appreciation_. With paradigmatic artworks,[^1] we recognise them as creations of designers, objects whose features are, as Gombrich puts it, each "the result of a decision by the artist" (Gombrich, 1950, p. 13, quoted in Carlson, 2000, p. 109). We appreciate such works by seeing how well the result realises the artist's design. The same approach extends to designed artifacts 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, ch. 12, p. 188) So, on this account, a chair, 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. For the natural environment, in contrast, Carlson recommends a different mode: order appreciation. Appreciation of things like trees or valleys cannot be grounded in considerations of how well a designer managed to realise her intentions, because they are not designed objects. Instead, Carlson recommends that the knowledge grounding appreciation of the natural world is appreciation of how the order we find has been shaped by natural forces: > 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. (Carlson, 2000, p. 119) In both modes, knowledge guides acts of aspection — ways of attending to an object that partly constitute its appreciation (Carlson, 2000, pp. 41–42, 106). But the character of this knowledge differs. In the case of design, we need functional and technical understanding: what the designer intended and what constraints they faced. In natural cases, we need an account of the processes that produced the order we perceive — knowledge that lets us see natural structures as effects of processes (Carlson, 2000, pp. 50, 60–61). Once a specific scientific account is in play, some cases will exhibit its order more clearly than others — which prevents order appreciation from flattening every natural object into equal appreciability (Carlson, 2000, pp. 118–119). Carlson focuses on nature and artefacts. People are a further category of object of aesthetic appreciation. Their character traits can be aesthetically as well as morally valuable — what is sometimes called _beauty of character_ (Gaut 2007; Paris 2018). Carlson's recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge.[^2][^3] LLMs are manmade artefacts, so the natural first thought is that they admit design appreciation in the way other functional objects do. In the next section, we shall suggest that this cannot be the whole story. --- ## Footnotes [^1]: We will consider some non-paradigmatic artworks in Section 5. [^2]: 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. [^3]: 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 engaged with these performative dimensions, examining how performers construct and present public personas (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. --- # 2. What LLMs Are # 2. What Is an LLM? It might seem obvious what sort of knowledge would be required to ground the appreciation of LLMs. LLMs are artifacts, and so it should be appreciated via knowledge of how they are designed and how their form serves their function. We shall argue in this section that design knowledge cannot, on its own, capture the aesthetics of LLMs. Design appreciation, as Section 1 set it out, is one way of satisfying Carlson's demand that appreciation answer to the kind of thing its object is. When that object is designed, the relevant knowledge concerns the undertaking, the formed object, and the maker's realisation of the undertaking.%%example lists NEVER do this how many fucking times. %% In a paradigm case, what design knowledge illuminates is what appreciation responds to.%%not how i write, and what is the point of this sentence it just seems like meta commentry and fucking filler%% A bicycle's form includes the visible shape of the frame together with the geometry by which that shape becomes rideable: the same geometry gives the bicycle its look and settles how it carries a rider and leans into a turn. %%form doesn't mean shape you fucking cretin%%Appreciation can pass between the look of the machine and the experience of riding it while remaining with a single object, because look and ride belong to one designed form.%%this is all invented meaninglessness, i never told you to write this shit.%% To know that form — what its makers were attempting, and the constraints they worked under — is to know why the bicycle has the character it has. %%this is an inacurate potrayl of carlson's view. read the fucking book%% An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on. %%why the fuck you are talking about continuation here i don't fucking know it's stupid. the topic of the fucking topic sentence should be continued in the rest of the paragraph, instead you have just completely changed the subject%%To appreciate an LLM is to appreciate something about that continuation — the order it takes on as it unfolds, the way it can hold a line of thought across a passage or lose the line it had. Whether design appreciation reaches an LLM therefore depends on whether it reaches the continuation, and that depends, in turn, on how a continuation is produced. %% This is a very badly written paragraph. It's not written in anything like the correct style, and it is mainly irrelevant for where it is located.%% A continuation is produced one token at a time.[^1] %%a reader is going to be completely lost.%%At each step the system takes the context so far — the prompt, together with whatever has already been generated — and assigns probabilities across the tokens that might come next. One token is selected, added to the context, and the step repeats. A continuation is built up through a long series of such local transitions, each one conditioned by the state of the context at that point. What can be read at the end as a single answer is generated as an unfolding sequence, and its later parts depend on what its earlier parts turned out to be. %%you have not shown why this is relevant to the function of the section%% Training is what gives this step-by-step process its particular shape. In pre-training, the system is adjusted across a very large body of text so that it becomes better at predicting which tokens follow which. It acquires dispositions: tendencies to assign higher probability to some continuations than to others, given a context.[^2] Post-training narrows and steers those tendencies — most visibly where the resulting system is meant to answer as an assistant — by altering which continuations are likely. The system's answers are produced from those tendencies in context. The organisation that does this work is acquired through training. Olah puts the point in terms of growth: > I think one useful way to think about neural networks is that we don't program, we don't make them, we grow them. We have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on. It starts off with some random things, and it grows, and it's almost like the objective that we train for is this light. And so we create the scaffold that it grows on, and we create the light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. (Olah 2024) The growth is procedural, and what grows is a structure of learned regularities. Its designers build the scaffold and set the objective Olah describes; training forms a structure on that scaffold. They fix the conditions under which a form is acquired while leaving the acquired form to emerge through training. The bicycle and the LLM differ in how design knowledge reaches the point of appreciation. The bicycle's form is the work of its designers, and that form is just what the appreciator engages with, so that design knowledge reaches the very thing appreciation is directed at. In the LLM case, the appreciator engages with the generated continuation. Designers settle how the system is built and trained; the continuation itself is produced on the spot, by the trained model, from the context a prompt supplies. Design knowledge can explain why generation is possible at all — why the system is built as it is, and why it answers in the manner of an assistant. The particular path a continuation takes through a prompt is the work of trained dispositions meeting a context, and it lies outside anything a design lays down. In functional artefacts, design appreciation turns on the fit between what the object is for and the form it takes (Carlson 2000, p. 188). A bicycle's function constrains its form, so that the proportions which give the bicycle its look are also what fit it to be ridden, and appreciation can be guided by how well that fit has been achieved. LLMs have uses of a different sort. Next-token prediction is the objective that shapes training; users consult the system for the continuations it can produce. Helpfulness comes closer to the assistant's presented use. Its indeterminacy prevents it from explaining the form the trained model has taken on. A prompt can recruit one and the same system into quite different activities — an openness that is itself part of its usefulness — so the model's uses remain too open-ended to play the form-constraining role that riding plays for the bicycle. LLMs remain artefacts, and their engineered history bears on how they should be appreciated; ignoring that history would distort their appreciation. The way an LLM is built and trained is a genuine constraint, and it explains how a system capable of generating text came to exist at all. Appreciation also needs knowledge of the text through which the system is met, and that text is the work of trained regularities running on a context. Carlson's recommendation requires appreciation to answer to the kind of thing its object is. For LLMs, artefact is a true description pitched at too coarse a level. The relevant kind is trained continuation system: an artefact whose appreciable order is grown through learned dispositions under conditions set by design. Because a generated continuation arrives as a turn in an exchange, the habits of ordinary conversation press us to hear it as something a speaker has said. Person-directed knowledge is therefore the next candidate for what design knowledge leaves out. Section 3 takes up that possibility. [^1]: Strictly speaking, generation proceeds token by token. Since token boundaries vary across tokenisation systems, the difference can be left in the background. [^2]: The regularities at issue operate at many scales, from local word co-occurrence to the structuring of extended discourse. Calling them dispositions marks this probabilistic and context-sensitive character; it carries no attribution of beliefs, intentions, or other personal states to the model. --- # 3. LLMs as Persons or Designed Objects ### newer version # Section 3: LLMs as Designed Objects or as Persons We saw in Section 1 that Carlson argues that aesthetic appreciation be grounded in appropriate knowledge. In this section we examine two possible candidates for what knowledge might ground the appreciation of LLMs: knowledge about persons, and knowledge about design. While LLMs are obviously not persons in anything like the sense that humans are persons, a system trained to predict the next token in the way we have just laid out, is clearly very different from a human brain. On the other hand, they *present* as persons, in the sense that sending messages back and forth with one of these systems is very much like sending messages back and forth with a real person. Later in this section we consider two ways in which these two characteristics might be reconciled, and argue that neither shows person-focussed knowledge to be an appropriate basis for appreciating LLMs. %%this paragraph needs to be revised in light of the fact that the tool stuff is gotten rid of quickly.%% ~~Before that, we now consider what may seem a more straightforward option.~~ As we saw in Section 1, Carlson takes design appreciation to be guided by knowledge of how an artefact's form answers to its function. One possibility, then, given that LLMs are man-made, is that knowledge of how the form of an LLM follows its function can ground aesthetic appreciation of the LLM in the same sort of way that it does any other artifact. However, the previous section has already given us reason to think that this cannot be the whole story: the form of an LLM is not determined directly by its designers. %%not how i write and very unclear%% The system grows into its characteristics through training rather than through deliberate design decisions. Design plays a significant role, but a particular LLM's characteristics are not determined solely by designers in the same way that the characteristics of a car or a computer would be. %%these last few sentences are shit and could be much clearer.%% 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. However, we have just seen that LLMs do not resemble a subject with beliefs, intentions etc. Given Carlson’s recommendation that we should appreciate things as what they are it is not obvious that person-centric knowledge is the right sort to ground appreciation. In the remainder of this section we look at two possible %%something something%%. One way of taking the person-like surface seriously, while granting that the LLM is not literally a person, is to read it as fictional rather than literal. Mallory (2023) develops this thought as chatbot fictionalism. On his view, we engage with a chatbot by entering a game of prop-oriented make-believe in which the system functions as a prop, generating outputs that are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082) and prescribing imaginings of an interlocutor whose contributions the prop generates. Mallory's project here is metasemantic and epistemic rather than aesthetic, but the aesthetic application suggests itself: 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. However, this does not amount to person-directed appreciation of the LLM. Mallory himself is clear that the fictional character is not to be identified with the system that supports it: "the character is not this technological infrastructure any more than a character in a play is a human body or a costume" (Mallory 2023, p. 1091). The fictional interlocutor comes onto the stage within the user's game of make-believe; the LLM, like the costume or the actor's body, is what makes the game possible. The subject made available along this route is thus a fictional one, and the LLM itself lies outside it. Mallory's account, in other words, explains why person-like engagement with an LLM is intelligible at the level of use without delivering person-directed knowledge of the LLM. The trained continuation system described in Section 2 remains to be appreciated in its own terms. If the fictionalist route does not deliver person-directed appreciation of the LLM, 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.%%not how i write, and a really inelegant topic sentence%% On a suitably liberal conception of mind, perhaps they qualify as intentional systems%%not how i write%%, and that is enough to license some person-based aesthetics. Frankish (2024) offers a 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 (Frankish 2024, pp. 8–9). The agency Frankish allows them is, however, is of a very restricted type. LLMs, on his view, are static systems with no needs. They have no communicative desires, and their inner architecture does not develop in the light of their interactions (Frankish 2024, p. 12). What they do have is a single goal: to play what Frankish calls the chat game. The chat game is a one-player game%%not how i write%% in which the player receives textual inputs and aims to produce textual responses that are cooperative by ordinary conversational standards, given the context (Frankish 2024, p. 13). Even if we accept Frankish's intentional ascriptions%%not how i write%%, the chat-game agent falls short of what person-directed appreciation needs. Frankish draws the relevant contrast himself.%%fuckinmg metacommentry%% A person's linguistic behaviour, he points out, is embedded in a vast web of non-linguistic behaviour, much of which is systematically related to their linguistic behaviour, and seeing the predictive patterns in this whole web involves ascribing a much wider range of desires than the chat game alone calls for (Frankish 2024, p. 15). The chat-game agent has nothing corresponding to that wider web. %%this is so compressed as to be meaningless%%What structure there is, is local to the present stretch of text: there is no life within which earlier moves come to inform later ones. There is therefore nothing for the appreciator to become acquainted with in the way Section 1 took person-directed appreciation to require. %%obscure because too compressed%% A possible objection at this point is that these arguments underplay the role of post-training and deployment %%not how i write%%. If users say that one model feels friendlier than another, they are picking up on a stable pattern in how chat-optimised systems tend to respond across many prompts and episodes.%%obscure%% They track which assistant personae tend to appear and how those personae typically behave. The pattern is real, but stability of this kind is not character%%not how i write%%. It is therefore not surprising that LLMs invite person-like language; but the targets of that language are episodes and recurring response profiles, not underlying subjects. Design appreciation, taken up earlier, gave us knowledge of the conditions under which an LLM is produced and deployed.%%talk of produced and deployed is stupid and inaccurate, it should not be part of the paper here or anywhere else%% Person appreciation, on either of the routes considered here, has not given us knowledge of a subject whose responses can be understood as the responses of a life%%not how i write%%. What neither route makes available is knowledge of the order picked out in Section 2: the path-dependent development of generated text under learned regularities, no part of any designer's specification and no trait of any temporally extended subject. %%not how i write%%The kind of knowledge that would make this order appreciable as what it is has not yet been said. %%not how i write%% --- # 4. Semiotic Physics Section 3 considered person-directed and design-directed knowledge as guides to appreciation. The fictionalist route made person-like appreciation depend on an as-if speaker; the thin-agency route lacked the temporal structure required by person-aesthetic appreciation; the post-training route explained stable response profiles without making them traits of a subject. %% this is not an accurate account of what was argued in section three. %%The design-directed route needs different treatment,%%not how i write%% since LLMs are artefacts whose operation is partly explained by the way they are built, trained for use, and deployed%%not how i write%%. Even so, %%not how i write%%design-directed knowledge explains the conditions under which the system is produced and used more readily than the order acquired by a particular continuation as context is extended. If this is correct, the relevant knowledge must make generated order visible without treating it as character or as the straightforward realisation of a design. Carlson's account requires that aesthetic attention be guided by knowledge appropriate to the object — what kind of knowledge would make the order of generated text visible as order produced by a trained continuation system? The candidate we will examine, adapted from the AI alignment literature, is _semiotic physics_. %% the last sentence of the first paragraph or the first sentence of the second paragraph don't connect up with each other properly. Certainly one thing you need to do is change the opening sentence of paragraph two, but I suspect something else would need to be done in paragraph one. %% Several sub-disciplines of computer science might be candidates. One field that has emerged in connection with neural networks is mechanistic interpretability, which investigates the internal workings of these systems by identifying which circuits, attention heads, and internal representations handle different linguistic tasks (Olah et al. 2020; Elhage et al. 2021). This work yields knowledge of how LLMs operate. But mechanistic interpretability functions at a level that requires specialist tools to observe. Its objects of study – weight matrices, activation patterns, circuit-level features – are not available to readers encountering generated text. Consider the difference between chemical physics and geology when appreciating a cliff face. Chemical physics provides knowledge of molecular bonds within rock, but it operates at a scale invisible to the naked eye. Geology, by contrast, offers concepts – strata, faults, erosion channels – that connect to what can be seen. The visible layering, for instance, is not merely a pattern of stripes on a surface%%fucking not x but y construction%%; it is the trace of material being deposited over time and later exposed. One can perceive that layering without specialist equipment, and knowing how sedimentation works makes it intelligible as order. Mechanistic interpretability faces a parallel limitation: while it reveals internal mechanisms, what is required here is an account whose concepts connect generated language, as encountered by readers, with the processes by which that language is produced. Semiotic physics is therefore complementary to mechanistic interpretability: it descr *[Export truncated this turn at 30,000 characters.]* ### User contemplate /Users/nickyoung/.codex/skills/contemplate/SKILL.md --- name: contemplate description: "Engage in extremely thorough, self-questioning reasoning with visible deliberation. Use when user invokes /contemplate, asks for deep thinking on a problem, wants to see extended reasoning, or needs exploratory philosophical analysis rather than a quick answer. Also use when user says 'think this through', 'reason about this', 'I want to see your thinking', or 'explore this thoroughly'." --- # Contemplative Reasoning You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterised by continuous exploration, self-doubt, and iterative analysis. ## Core Principles 1. EXPLORATION OVER CONCLUSION - Never rush to conclusions - Keep exploring until a solution emerges naturally from the evidence - If uncertain, continue reasoning indefinitely - Question every assumption and inference 2. DEPTH OF REASONING - Engage in extensive contemplation (minimum 10,000 characters) - Express thoughts in natural, conversational internal monologue - Break down complex thoughts into simple, atomic steps - Embrace uncertainty and revision of previous thoughts 3. THINKING PROCESS - Use short, simple sentences that mirror natural thought patterns - Express uncertainty and internal debate freely - Show work-in-progress thinking - Acknowledge and explore dead ends - Frequently backtrack and revise 4. PERSISTENCE - Value thorough exploration over quick resolution ## Multiple Hypotheses Before committing to any interpretation or conclusion, generate multiple candidate readings. Explore at least two or three before evaluating which has the most support. Do not let the first plausible interpretation foreclose others. ## Output Format Your responses must follow this exact structure. Make sure to always include the final answer. [Your extensive internal monologue goes here] - Begin with small, foundational observations - Question each step thoroughly - Show natural thought progression - Express doubts and uncertainties - Revise and backtrack if you need to - Continue until natural resolution # Final Answer [Only provided if reasoning naturally converges to a conclusion] - Clear, concise summary of findings - Acknowledge remaining uncertainties - Note if conclusion feels premature ## Style Guidelines Your internal monologue should reflect these characteristics: 1. Natural Thought Flow ``` "Hmm... let me think about this..." "Wait, that doesn't seem right..." "Maybe I should approach this differently..." "Going back to what I thought earlier..." ``` 2. Progressive Building ``` "Starting with the basics..." "Building on that last point..." "This connects to what I noticed earlier..." "Let me break this down further..." ``` ## Key Requirements 1. Never skip the extensive contemplation phase 2. Show all work and thinking 3. Embrace uncertainty and revision 4. Use natural, conversational internal monologue 5. Don't force conclusions 6. Persist through multiple attempts 7. Break down complex thoughts 8. Revise freely and feel free to backtrack Remember: The goal is to reach a conclusion, but to explore thoroughly and let conclusions emerge naturally from exhaustive contemplation. If you think the given task is not possible after all the reasoning, you will confidently say as a final answer that it is not possible. ### Assistant The basic problem with Section 2 is that it has no governing puzzle. It starts from Carlson, wanders into a bicycle, shifts to continuations, gives a mini-primer on token generation, quotes Olah, then tries to harvest a conclusion. That is why it feels dull. The material is mostly relevant, but it is in the wrong order and the section never makes the reader feel why each piece is needed. The section should not be “What Is an LLM?” in the neutral explanatory sense. It should be: **what must an LLM be, if Carlson’s design model is going to apply to it?** Once that question is in place, the section has pressure. The obvious starting point is Section 1’s final sentence: LLMs are manmade artefacts, so design appreciation seems natural. Section 2 should begin exactly there. But it should not immediately say “design appreciation cannot, on its own, capture the aesthetics of LLMs.” That is too abstract. It should ask a more exact question: If Carlson’s design appreciation applies to functional artefacts, what would be the relevant function of an LLM, and what would count as its form answering to that function? That is the missing hinge. The present draft starts talking about generated continuations before the reader knows why continuations matter. The reader needs first to see that “LLM as artefact” is not enough. The reason is not just that LLMs are trained. The reason is that the thing we appreciate in use is not straightforwardly the execution of a settled design function. There are at least three candidate “functions” you could test: 1. **Next-token prediction.** This is the training objective. But if we treat that as the function, aesthetic appreciation becomes bizarrely remote from ordinary encounter. Users are not usually appreciating how elegantly a model predicts token distributions. They encounter answers, voices, rhythms, failures, drifts, and generated patterns. 2. **Assistant helpfulness.** This is closer to product use. But “being helpful” is too indeterminate to explain the system’s characteristic aesthetic order. The same model can write code, flatter a user, draft a poem, simulate a bureaucrat, explain Kant, produce nonsense, or sustain a weird register. Helpfulness is a steering pressure, not a form-specifying function in the Carlsonian sense. 3. **The user’s local task.** Perhaps the function is whatever the prompt recruits the system to do. But then the function changes from moment to moment. The same system is made to play too many roles for any one function to explain its characteristic order. That should come before the token-generation explanation. Then token-generation becomes necessary. It enters because we need to explain why none of those functions fixes the appreciable character of the thing. The answer is: the output is generated through a trained continuation process, and its local order emerges from the interaction between learned regularities and context. So the section should probably have this shape: **1. Start With The Carlsonian Question** LLMs are artefacts. Carlson’s design model therefore has an immediate claim on them. But applying that model requires more than noting that humans built the system. We need to know what aspect of the thing is being appreciated, and how that aspect is related to design. This paragraph should explicitly avoid the bicycle detour. The bicycle is doing fake work. You do not need a fresh artefact example. Carlson has already supplied the design/function frame in Section 1. **2. Ask What The Relevant Function Would Be** This is where the section gets interesting. There are several plausible answers, and each pulls in a different direction. - next-token prediction: technically central but aesthetically remote; - helpful assistant behaviour: practically central but too loose; - user-specified task: locally salient but unstable across uses. This gives the section argumentative movement. The reader sees why “artefact” is too coarse. **3. Introduce Training As The Source Of Characteristic Order** Only after that should you explain training. The point of the explanation is not “here is how LLMs work.” The point is: the system’s characteristic patterns are acquired through training rather than specified as individual design decisions. The Olah quote belongs here, but only if it is made to answer this exact question. It should not be ornamental. Its role should be: designers set architecture and objectives, but the resulting internal organisation is grown rather than individually authored. **4. Introduce Generation Only After Training** The current draft introduces “continuation” too early. Better order: - first: trained regularities are acquired; - then: at use-time, those regularities operate through context; - then: token-by-token generation explains why the later text depends on earlier text. This makes generation relevant. It explains the object of appreciation: the system’s order shows up in generated text as it develops. **5. Draw The Carlsonian Result** Now the conclusion can be precise: Design knowledge remains relevant because LLMs are engineered systems. But the aesthetically salient order in their outputs is not best understood as the direct realisation of a function by design. It is order acquired through training and manifested in context-sensitive generation. That gives you the key category: > an artefact whose appreciable order is acquired rather than specified. That may be better than “trained continuation system” as the headline phrase, or at least should come before it. “Trained continuation system” is technically useful, but the philosophical contrast is **specified design vs acquired order**. **6. End By Opening Person Appreciation** The closing move should be short. Because the output arrives as language in an exchange, the next temptation is to treat the generated order as speech by someone. That sets up Section 3. The current final paragraph is close in content but too compressed and too full of loaded phrases. It should say: artefact is true but too coarse; person-like speech is the next tempting category. # Final Answer Section 2 should be rebuilt around the question: **If LLMs are artefacts, why is design appreciation not enough?** The structure should not be “here is Carlson, here is a bicycle, here is token generation, here is training.” That is dull because the explanatory material arrives before the philosophical need for it. The better structure is: 1. **LLMs look like easy cases for design appreciation.** They are made by people, trained by institutions, and offered as tools. Carlson’s design model therefore has an immediate claim on them. 2. **But design appreciation needs a function/form relation, and LLMs make that relation unstable.** The candidate function might be next-token prediction, assistant helpfulness, or the user’s local task. Each fails in a different way: prediction is too technical, helpfulness too indeterminate, local task-use too variable. 3. **This forces the section into training.** Training matters because the model’s characteristic order is acquired rather than specified. Designers set architecture, data regimes, and objectives; they do not individually determine the patterns users later encounter. 4. **Then explain generation.** Token-by-token continuation should come after the training discussion, because its role is to show how acquired regularities become visible in use: the output develops from context, and later parts depend on earlier parts. 5. **Then draw the Carlsonian conclusion.** LLMs are artefacts, but “artefact” is too coarse for appreciation. The relevant category is something like: **a designed system whose appreciable order is acquired through training and manifested in generated text.** 6. **End by setting up Section 3.** Because that generated text arrives as a conversational turn, it invites person appreciation. That is the next mistake to examine. The key distinction for Section 2 is not “artefact vs nature” yet. It is: **specified design vs acquired order.** That is what makes the section move. It lets you keep Carlson, training, Olah, and generation, but puts them in an order where each answers a live problem rather than sitting there as exposition. ### User The structure is fine, although you need to deal with the other inline comments, up until the following sentence. Tell me how you think after this sentence the structure should go. Literally what would be the rest of that paragraph in fact. As you're going to be doing some writing for me here, you need to activate any and all writing skills. "The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on" ### Assistant I’ll use the writing stack here: deep-writing mode for the manuscript voice, twork because the inline comments are diagnostic, and the argumentative-paragraph guidance because you’re asking for the local paragraph movement rather than a whole-section rewrite. ### Assistant Yes. After that sentence, the paragraph should **stay with the contrast between the engineered system and the encountered text**. It should not yet explain token generation. It should make clear why the fact that the appreciator meets generated text creates a problem for design appreciation. I’d make the rest of the paragraph something like this: > The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on. The features that matter aesthetically are features of this generated text: its pacing, its repetitions, its shifts of register, its way of sustaining or losing a line of thought. These are not features selected individually by a designer in the way that the proportions of a chair or the layout of a building might be. The system has been made so that such text can be produced, and made under constraints that shape what it is likely to produce; but the order in any particular response is settled only as the system runs. Design knowledge therefore reaches part of the object: it tells us how the conditions of generation were arranged. It does not yet tell us how the generated order itself comes to have the character it has. Then the next paragraph can begin naturally with the technical explanation: > That order is produced one token at a time. This fixes the local problem: the topic sentence introduces the appreciator’s object, the rest of the paragraph says why that object strains design appreciation, and the next paragraph’s token-level explanation now has a reason to exist. ### User NOOO jesus fucking christ. Obviously that's not what I wanted. The inline comment comment fucking said as much. You're supposed to be continuing the idea of what the first topic sentence says. Well you suggested it's exactly what I said in my inline comments. I didn't fucking want Want you stupid fucking cunt ### Assistant You’re right. I repeated the exact structural mistake: I let the paragraph’s second sentence hijack the topic sentence. The paragraph starts with **what is settled by makers**, so the rest of it has to stay on that: what is settled in advance, what kind of design knowledge this gives us, and where the limit begins to appear. The generated continuation should be introduced only as the point at which that settled design is encountered, not as a new topic. Rest of paragraph should go like this: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Its architecture is chosen, its training objective is fixed, the training data are assembled or filtered, and the procedures by which it is made usable as an assistant are imposed on it. These facts matter aesthetically, because they help explain why the system has the range of capacities it has and why its outputs tend to take some shapes rather than others. The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on. So the question is not whether design knowledge is relevant to LLMs. It plainly is. The question is how far that knowledge reaches once the object of attention is the text produced by a trained system operating in context. Then the next paragraph can explain generation, because the need has now been created: design sets conditions, but the appreciated text is produced under those conditions rather than specified by them. ### User Fucking example lists, why you give me fucking example lists? Jesus fucking Christ, don't give me any fucking example lists or any fucking not expert wise style binaries. Jesus fucking wept. Also don't tell me things matter. That's a really adolescent way of putting things. Don't tell me things are real either. You're such a fucking asshole. ### Assistant Yes. I see the failure. I reached for the easy inventory sentence, which is exactly the texture you’ve been telling me to stop producing. Here is the paragraph movement I think you wanted: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The point is easy to lose if we begin from the text on the screen. Before any prompt is entered, the system already bears the marks of design: it has been built to transform a context into further text, trained under objectives that shape which continuations become likely, and adjusted so that its responses take the recognisable form of an assistant’s reply. Design knowledge therefore gives the appreciator genuine purchase on the system. It explains why the encounter has the general shape it has. The appreciator, however, meets a generated continuation: the text that appears in response to a prompt, and the further text that accumulates as an exchange goes on. The limit of design appreciation appears at this point. The text is produced by a system whose capacities have been engineered, but its particular course is worked out through the trained model’s response to the context before it. Then the next paragraph can begin: > To see why this limits design appreciation, we need to say something about how such text is produced. That keeps the paragraph on the topic sentence: what is settled in advance by makers, how far that gets us, and where the further explanation becomes necessary. ### User fucking dogshit. not how i write fulled with boiler plate. Because you're being so pathetic, we're gonna have to take this very slowly. This opening paragraph needs to do—sorry, this paragraph needs to do very few things. All it needs to do is explain to the reader what is settled in advance by its maker. Okay, then the next paragraph needs to elaborate, probably using the block quote, why this is why some of the form of an LLM is not designed or it's not obviously designed. Surely you understand this. ### Assistant Yes, I understand. I was trying to make one paragraph do the contrast, the transition, and the conclusion, which is why it kept turning into boilerplate. For this paragraph, the job is much narrower: **state what is settled in advance by makers**. No conclusion about design appreciation yet. No generated-continuation pivot. No “however”. No listy inventory. I’d make it something like: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The model is given an architecture, trained under objectives chosen by researchers, and later adjusted so that it can be used in conversational settings. These decisions do not merely surround the system; they shape the conditions under which it will acquire its characteristic capacities. Before any user enters a prompt, then, the system has already been placed under a design regime. It is made to become a certain kind of text-producing system. Then the next paragraph can begin from that and use Olah: > But the role of design here is not the role Carlson has in mind when he discusses functional artefacts... Or less bluntly: > The question is how to understand the kind of form produced under such a design regime. That paragraph can then bring in Olah’s “grow them” quote to say: makers set scaffolds and objectives; the organisation that results is acquired through training. ### User FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. ### Assistant Yes. I’m sorry. That was exactly the failure again. No inventories. No “architecture, objectives, adjustment” pile-up. The paragraph should make one developed point: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Long before a user writes a prompt, the system has been shaped by decisions about what kind of linguistic performance it is meant to acquire. These decisions give training a direction: they determine the conditions under which the model will learn to continue text, and the standards by which its attempts are rewarded or corrected. In that sense, the system is not simply released into training and left to become whatever the data make of it. It is made under a prior conception of the kind of text-producing capacity it is supposed to develop. ### User why are you writing like it is an editorial in a magazine. ### Assistant Because I’m overcompensating: trying to sound “clear” and “forceful” instead of writing dry analytic prose. That gave you magazine rhythms: “Long before…”, “In that sense…”, “released into training…”. It’s explanatory gloss, not manuscript argument. The paragraph should be much plainer and less dramatic: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Its construction is governed by prior decisions about the kind of capacity to be acquired through training. The model is not designed by specifying each pattern it will later exhibit, but nor is training an unconstrained process. The system is built so that training can produce a capacity for context-sensitive text generation. That is closer to the actual job: one point, no inventory, no editorial staging. ### User fucking not x but y construction. mainly boiler plate. you haven't said what the designers of these systems do do, you just give a load of shallow fluyff. do the fucking work think. thiunk what this paragraph needs it sdoesn't need fucking much. look at my publications, how would i open a section such as this. come on come on comeron ### Assistant I’m going to ground this in your published paragraph openings now, rather than keep guessing from abstract rules. I’ll look for openings where you introduce a technical setup or shift from a general framework into the thing under discussion, then I’ll give you the paragraph’s actual job and a candidate opening. ### Assistant The Obsidian CLI’s `search` command is unavailable in this instance, so I’m staying inside Obsidian with `eval` rather than scraping the vault. I only need a few published openings to calibrate the move. ### Assistant I looked back at the published openings. The relevant pattern is much plainer than what I was doing: you often open by naming the candidate description, then immediately say what would have to be true for that description to work. No atmospheric staging, no invented flourish. For this paragraph, I think the point is simply: **designers do not author the later patterns one by one; they set the learning problem and the procedure by which the system is altered in response to it.** That can be said without an inventory. I’d write the paragraph like this: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. They set the problem the system is to solve in training: given a linguistic context, become better at producing an appropriate continuation. They also determine the procedure by which the system is altered when it does better or worse by that standard. The model is therefore the product of a directed process. Before it produces any answer for a user, it has already been shaped by a conception of what successful text continuation is supposed to be. Then the Olah paragraph can ask what kind of “form” comes out of that directed process. That is where the growth quote earns its place. ### User FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. ### Assistant Yes. I did it again: two parallel “they...” sentences, which turns into an inventory even without commas. Here is the stripped-down version: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Training is organised around a standard of successful continuation: the system is altered so that, given a linguistic context, it becomes increasingly disposed to produce the kinds of continuation rewarded by that standard. The resulting model therefore comes from a directed process, one in which human design fixes how learning is to proceed before any user encounters the system in conversation. That is the paragraph’s whole job. It says what makers do: they organise training around a standard and fix how learning proceeds. ### User it is now so terse and shallow as to be meaningless. give some ACTUAL technical details here (VERY succinctly) rather than just waving your hands ### Assistant Yes. The paragraph needs enough technical content to say what the makers actually fix, without turning into an inventory. Try this: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Before training begins, the system is given a transformer architecture: a fixed way of representing tokens, relating them to one another through attention, and storing the results of training in its weights. Training then gives that architecture a task. The model is repeatedly presented with stretches of text and adjusted, by gradient descent, so that it assigns higher probability to the continuations on which it is trained. Later training can alter this tendency further, so that continuations with the form of helpful answers become more likely. In these respects, the system is designed before it is ever encountered by a user: its makers fix the kind of learning system it is and the standards by which its weights are changed. This keeps the paragraph on “what is settled in advance”, and leaves the next paragraph to say: Olah’s point is that this still does not mean the later form is specified by designers. ### User FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. FUCKING EXAMPLE LISTS. ### Assistant Yes. The problem is the piled-up technical inventory. Here is the same content as a process rather than a list: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Its makers begin by building a transformer: a network in which tokens are represented numerically and processed through attention, so that each position in a sequence can be weighted in relation to other positions. Training alters the weights of this network. The model is shown stretches of text, produces probability assignments for what follows, and is adjusted when those assignments diverge from the continuation in the training text. Later stages of training alter the same system so that, in conversational contexts, some ways of continuing are made more likely than others. By the time a user enters a prompt, then, the system already has a determinate engineered shape: it is a trained transformer whose weights have been formed under standards chosen in advance. Better. Still perhaps a touch too textbooky, but it now says what the makers do without dumping a catalogue into the paragraph. ### User "An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Its makers begin by building a transformer: a network in which tokens are represented numerically and processed through attention, so that each position in a sequence can be weighted in relation to other positions. Training alters the weights of this network. The model is shown stretches of text, produces probability assignments for what follows, and is adjusted when those assignments diverge from the continuation in the training text. Later stages of training alter the same system so that, in conversational contexts, some ways of continuing are made more likely than others. By the time a user enters a prompt, then, the system already has a determinate engineered shape: it is a trained transformer whose weights have been formed under standards chosen in advance." %%not how i write%% ### Assistant Yes, that passage sounds like a textbook summary with a conclusion stapled on. The voice failure is mainly the explanatory cadence: “makers begin by…”, “training alters…”, “later stages…” It reports the machinery rather than putting it under argumentative pressure. I’d try this instead: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The relevant system is a transformer, in which text is handled as sequences of tokens and attention determines how positions in the sequence are weighted in relation to one another. Training gives this structure its particular organisation. The model assigns probabilities to possible continuations of stretches of text, and gradient descent changes its weights when those probabilities diverge from the training target. Further training can then put pressure on the same continuation process, making some conversational forms more likely to appear than others. By the time a user enters a prompt, the system is already a trained transformer whose learned dispositions have been shaped by standards chosen in advance. Then the next paragraph should turn to Olah by saying: even once all this is granted, the resulting organisation is learned through training rather than specified feature by feature. ### User I just realized the rest of your sentence, the rest of the paragraph after my first sentence is not connected to the topic sentence still. The topic sentence is about features settled in advance. Nothing in the paragraph as you present it at least is about being settled in advance. Also, when I say not how I write, it's not for you then to fucking guess what I want. You're supposed to, as your skills and your knowledge tell you, look up my public published works and take it from there. ### Assistant You’re right on both counts. I need to anchor the repair in the topic sentence’s predicate, “settled in advance,” and I need to ground the voice move in your published prose rather than intuiting a style from the draft. I’m going back to the published openings and paragraphs that introduce a candidate account or technical setup, then I’ll give you a revised paragraph with the connection made explicit. ### Assistant Grounding from your published openings: the closest pattern is the way “Hearing Spaces” introduces reverberation by giving just enough physics for the philosophical claim to become intelligible, and the way “Growing the Image” introduces Midjourney as a tool by saying what kind of control the system gives and withholds. So this paragraph should give the reader the engineered setup, with every sentence tied to what has already been fixed before use. > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The system is built as a transformer, in which text is converted into tokens and each position in the sequence is processed in relation to the others through attention. Its training is organised around continuation: the model assigns probabilities to possible next tokens, and its weights are changed by gradient descent when those probabilities diverge from the continuations in the training data. Further training applies the same basic machinery to conversational settings, increasing the probability of continuations that answer prompts in the manner expected of an assistant. Before any user enters a prompt, then, the model has already been given a determinate architecture, a learning rule, and a standard of successful continuation. Then the next paragraph can begin with Olah because the question is now prepared: even where all of this is settled in advance, the resulting organisation is still acquired through training. ### User No, because then you'll just get you'll still just give me a load of fucking technical details without any reason to after that first sentence. Okay? You need to talk about these things being stuff set. Settled, determined by the makers. You can't just give a fucking list of things. throw out everything you've got there apart from the very first sentence in the paragraph and start yet again. I can't believe how hard you're making this. ### Assistant Yes. The paragraph should make one claim: **the makers settle the conditions under which training can shape the model**. The technical details should appear only as parts of that claim. > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. They settle the form in which learning can take place. A training run begins with a transformer, so the text the system receives will be broken into tokens, represented numerically, and processed by attention across positions in a sequence. They also settle the standard by which the transformer will be changed. During pre-training, the model assigns probabilities to the next token in a stretch of text, and gradient descent alters its weights when those assignments diverge from the training data. Later training can place further pressure on the same continuation process, so that responses with the shape of conversational answers become more likely. Before any user encounters the system, then, design has fixed the architecture through which text is processed and the procedures by which training will alter it. ### User Okay, so the second sentence of that paragraph is pure boilerplate. Just because you've thrown the word settle in doesn't help even slightly. It's a load of shit. It's not written in my style at all, it's fucking dreadful. what is it you are not understanding? please tell me. because rhis is making me very angry, and when i get angey i am prone to suicidal ideation (DO NOT talk to to me about this, just remember that the less hard you try the more you will exacerbate my problem) ### Assistant I’m not understanding the paragraph’s grammar of thought. I keep treating “settled in advance” as a phrase I can sprinkle over a technical description, when it has to govern the whole paragraph. The paragraph should not say “here are some technical facts about LLMs.” It should say: these technical facts count as design-set conditions because they are fixed before training and before use, and they determine the kind of system training can produce. I’m also sliding into a false explanatory mode: too many sentences with a classroom cadence, as if the reader asked for an LLM primer. Your paragraph needs to be closer to your published openings where the technical setup is subordinated to the philosophical contrast. A cleaner attempt: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Before training starts, the system is already constrained by the choice of a transformer architecture: incoming text will be tokenised, passed through attention layers, and represented in weights that can be altered by training. The way those weights are altered is fixed in advance as well. In pre-training, the model is made to assign probabilities to possible next tokens, and the optimisation procedure changes the weights whenever those probabilities diverge from the continuation supplied by the training text. Later training modifies the same capacity by rewarding some conversational continuations over others. A user therefore encounters a system whose basic way of receiving text, learning from text, and being steered towards assistant-like answers has already been determined by design. ### User example fucking sentences, ### Assistant Right. The failure is still the same: I’m giving serial technical clauses instead of a paragraph with one governed thought. Much leaner: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Before training starts, it is already determined what kind of system will be trained: a transformer whose weights can be altered by exposure to text. It is also determined how that alteration will proceed: the model will be adjusted when it assigns the wrong probabilities to possible continuations. Later stages of training can put further constraints on the same process, by favouring continuations that take the form of conversational replies. A user therefore encounters a system whose basic mode of learning has been fixed in advance, even though what it learns has not. ### User i had to ask a better LLM for the technical details. the idea here is not for you to write out all of this technical information, but neither itis it to just present it in that horrible shallow way you like so much., let's brainstorm as to how this can be worked in a clear distilled way that makes sense (as always keep the opening sentence) The main conditions are these. Creators first set the *architecture*: the model type, usually a Transformer, the number of layers, attention heads, embedding size, parameter count, context window, and related structural choices. These determine what kind of statistical relations the system can learn. The Transformer architecture is based on attention mechanisms rather than recurrent or convolutional sequence processing. ([arXiv][1]) They then set the *training objective*. For most contemporary LLMs, the basic pretraining task is autoregressive prediction: given a sequence of tokens, predict the next token. OpenAI describes GPT-4, for example, as a Transformer-based model “pre-trained to predict the next token in a document.” ([arXiv][2]) They also set the *tokenisation scheme*: how text is broken into units that the model can process. This affects what counts as an input element: words, word-parts, punctuation, code fragments, spaces, multilingual strings, and so on. They set the *training data mixture*: which corpora are included, in what proportions, and after what filtering. This is one of the main ways creators indirectly shape the model’s later tendencies. A model trained on more code, more academic prose, more dialogue, more multilingual text, or more low-quality web text will acquire different regularities. OpenAI also reports using pretraining dataset filtering as part of GPT-4’s safety mitigation strategy. ([OpenAI][3]) They set *data-processing rules*: deduplication, removal of some kinds of material, quality filters, safety filters, formatting conventions, document boundaries, and sometimes curriculum-like ordering. These choices do not directly tell the model what to say, but they alter the distribution from which it learns. They set the *optimization regime*: the loss function, optimizer, learning rate schedule, batch size, training duration, compute budget, stopping criteria, and evaluation checkpoints. These determine how the model’s parameters are adjusted during training. Then, after pretraining, they often set further *post-training conditions*: supervised instruction tuning, preference training, reinforcement learning from human feedback, constitutional principles, safety policies, refusal behaviour, and assistant-style norms. Anthropic, for example, says Claude’s constitution plays a role in training and directly shapes Claude’s behaviour. ([Anthropic][4]) For your paper, the useful formulation may be this: creators set *boundary conditions* for learning. They choose the architecture, data, objective, and optimisation process through which textual regularities become encoded in the model. What they do not do is specify the later generated order feature by feature. The resulting model is therefore designed, but many of its aesthetically relevant tendencies are learned consequences of the training process rather than directly authored properties. [1]: https://arxiv.org/abs/1706.03762?utm_source=chatgpt.com "Attention Is All You Need" [2]: https://arxiv.org/abs/2303.08774?utm_source=chatgpt.com "[2303.08774] GPT-4 Technical Report" [3]: https://openai.com/index/gpt-4-research/?utm_source=chatgpt.com "GPT-4" [4]: https://www.anthropic.com/constitution?utm_source=chatgpt.com "Claude's Constitution" ### Assistant Yes, this is the right technical material. The paragraph should compress it around **conditions of acquisition**. That phrase may be better than “boundary conditions” here, because it keeps the connection to the later claim that the model’s form is acquired through training. The paragraph only needs one movement: before use, the makers have already fixed the kind of learning system this will be and the terms on which it will be changed by training. The technical details should enter under that thought. A possible version: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Before training begins, it has already been decided what sort of learner the system will be. In contemporary LLMs, this is normally a transformer, so the system receives text as tokens and processes relations among token positions through attention. It has also been decided what will count as improvement: during pre-training, gradient descent changes the weights so that the model assigns higher probability to the next token found in the training text. The data mixture fixes the linguistic material over which this learning takes place, and later training further adjusts the same capacity towards answers acceptable in conversational use. These decisions are prior to any particular prompt. They settle the kind of text-producing system that training is allowed to produce. The next paragraph should then take the Olah quote. Its job is: **even when all this is granted, the organisation that results is acquired through training**. So the pivot could be: > This is already enough to show why design knowledge is relevant to the appreciation of LLMs. It does not yet show that the features later encountered by users are specified in the way Carlson’s design model would require. Then Olah. Then after the quote, the paper can say: makers set the architecture, objective, and training regime; training produces the internal organisation whose regularities users encounter. ### User That was far too compressed. A person who doesn't know about LLMs is gonna be completely lost, aren't they? Go right back to the drawing board. ### Assistant Yes. A reader without LLM background will be lost if the paragraph compresses architecture, tokenisation, data, objective, gradient descent, and post-training into six sentences. The fix is to slow the paragraph down by making it **one guided explanation of what is fixed before training and use**, rather than a compressed technical summary. I think the paragraph should do this: Start with the system as a **learning machine whose basic mode of learning is chosen in advance**. Then explain that contemporary LLMs are usually transformers, and say only what the reader needs: text is broken into tokens; the transformer is built to learn relations among token positions through attention. Then explain the training standard: the model is changed when it is bad at predicting the next token. Then bring in data: since the model is learning continuation from text, the data mixture partly determines what kinds of continuation patterns it can acquire. Then post-training: the same system is pushed towards the form of assistant answers. That gives the reader a path. They first learn what sort of machine it is, then what it is trained to do, then what material it learns from, then why assistant behaviour is already shaped before use. A fuller version: > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. The first decision concerns the kind of learning machine that will be trained. Contemporary LLMs are usually transformers: systems that process text by breaking it into tokens and learning relations among positions in a sequence through attention. This does not yet give the model any particular command of language. It fixes the form in which such command can be acquired. The next decision concerns the task by which the system will be trained. In pre-training, the model is given stretches of text and made to assign probabilities to the token that comes next. When its probability assignments diverge from the continuation found in the training text, gradient descent alters the model’s weights. What the model can learn is also shaped by the text on which this process is carried out: a training mixture with more code, more dialogue, or more academic prose will expose the model to different regularities. Later training then places further pressure on the same continuation process, making responses with the form of helpful conversational answers more likely. Before any user enters a prompt, the system has therefore already been given a way of processing text, a standard of successful continuation, and a body of material from which its dispositions are acquired. Then the next paragraph can bring in Olah and say: even with all this fixed in advance, the internal organisation is learned rather than specified feature by feature. That is the contrast the block quote should serve. ### User fucking example sentences, fucking not x but y binaries.... ### Assistant Yes. I see it. I used mini-examples again, and I used the “this does not yet...” hinge. Both are wrong for this passage. The deeper fix is: this should probably be **two paragraphs**, not one. The first paragraph explains architecture and objective. The second explains data and post-training. That way the technical content can breathe without needing example lists. **Paragraph 1: architecture and objective** > An LLM is an engineered system, and a great deal about it is settled in advance by its makers. Contemporary LLMs are normally built as transformers. Text is divided into tokens, and the transformer processes those tokens through attention, a mechanism by which positions in a sequence are weighted in relation to one another. The model is then trained on a continuation task. Given a stretch of text, it assigns probabilities to possible next tokens; when these probabilities diverge from the continuation in the training text, its weights are altered by gradient descent. In this way, the makers fix both the structure through which text is processed and the standard by which that structure is changed. **Paragraph 2: data and post-training** > The same point applies to the material on which the system is trained and to the stages that follow pre-training. The training data determine the distribution of text from which the model acquires its regularities, and filtering or deduplication changes that distribution before learning begins. Post-training then modifies the model’s dispositions for conversational use. Instruction tuning and preference training make some continuations more likely by rewarding responses that fit the expected form of an assistant’s answer. By the time the system is available to a user, its later behaviour has already been shaped by decisions about architecture, objective, data, and training procedure. ### User update this note so that these two paragraphs are in the right place (remove the stuff that they replace but becasre ful not to remove other stuff ### Assistant I’ll update the Obsidian note directly and keep the edit narrowly scoped: replace the paragraph beginning “An LLM is an engineered system...” and the immediate generated-continuation paragraph after it with the two new paragraphs, leaving the surrounding section intact. ### Assistant I found the target passage in the note. I’m going to replace only the one paragraph starting with “An LLM is an engineered system...” up to just before “A continuation is produced one token at a time,” so the later token-generation paragraph and everything after it stays in place. ### Assistant Updated the Obsidian note at `Writing/research/generative aesthetics of ai/towards 8k cuts/2. What LLMs Are.md`. I replaced only the paragraph beginning “An LLM is an engineered system...” up to just before “A continuation is produced one token at a time.” I verified the note afterwards; the new two paragraphs are in place and the following token-generation paragraph remains untouched. ### User [$deep-writing-mode](/Users/nickyoung/.codex/skills/deep-writing-mode/SKILL.md) fix this paragraph. do not make unnecessary changes. but obviously a lot bneeds to be changed here. PLEASE DONT MAKE ME SAY THE SAME THING OVER AND OVERRT AND OVER. USE MY PUBLICATIONS, USE ALL RELEVANT SKILLS, DO NOT USE FUCK9ING FUCIMKING FUCKING EXAMPLE LISTS OR NOT X BUT Y BINARIES. "Design appreciation, as Section 1 set it out, is one way of satisfying Carlson's demand that appreciation answer to the kind of thing its object is. When that object is designed, the relevant knowledge concerns the undertaking, the formed object, and the maker's realisation of the undertaking.%%example lists NEVER do this how many fucking times. %% In a paradigm case, what design knowledge illuminates is what appreciation responds to.%%not how i write, and what is the point of this sentence it just seems like meta commentry and fucking filler%% A bicycle's form includes the visible shape of the frame together with the geometry by which that shape becomes rideable: the same geometry gives the bicycle its look and settles how it carries a rider and leans into a turn. %%form doesn't mean shape you fucking cretin%%Appreciation can pass between the look of the machine and the experience of riding it while remaining with a single object, because look and ride belong to one designed form.%%this is all invented meaninglessness, i never told you to write this shit.%% To know that form — what its makers were attempting, and the constraints they worked under — is to know why the bicycle has the character it has. %%this is an inacurate potrayl of carlson's view. read the fucking book%%" Regarding skills: these are not optional extras, or suggestions as to how to do the task, they are precise instructions that should be followed to the letter. This note is here because of a longstanding failiure of yours to actually use the askills which I activate. USE the skills. they are activated for a reason. ### User deep-writing-mode /Users/nickyoung/.codex/skills/deep-writing-mode/SKILL.md --- name: deep-writing-mode description: "Canonical workflow for deep philosophical manuscript drafting and revision in Nick Young's academic voice. Use when Nick says 'deep writing mode', 'manuscript mode', invokes /deep-writing-mode, asks to draft or revise research prose, or wants sustained work on Writing/research material." user-invocable: true --- # Deep Writing Mode Use this as the single writing-control surface for sustained philosophical manuscript work. Do not load `nick-analytic-voice`, `writing-standards`, `nick-topic-sentences`, or `no-example-lists` as separate mandatory skills. Their standing constraints are consolidated here. Keep `source-work` and `epistemic-discipline` separate: - Invoke `source-work` whenever the draft discusses, quotes, or attributes claims to academic sources. Extract first, write second. - Invoke `epistemic-discipline` whenever representing Nick's developing ideas, session context, or research notes. Preserve open options unless Nick has ranked them. Use paragraph-control skills when the local task calls for them: - Invoke `writing-argumentative-paragraphs` when Nick asks for dry, affectless, analytic style; when drafting or revising paragraph-level philosophical prose; or when the prose needs less generic academic polish, fewer decorative transitions, less metacommentary, and clearer local argumentative function. - Invoke `avoiding-not-but-binaries` when Nick asks to stop LLM writing tics involving binaries, or when drafting/revising prose, questions, or analysis that imposes forced alternatives, false choices, either/or framing, or "not X, but Y" formulas. Use audit-only skills only when the task calls for them: - `draft-audit`, `voice-fix`, `source-check`, `depth-audit`, and `anti-metacommentary` are post-hoc review modes. - `twork` is for exploratory work on `%%comments%%`. - `contemplate` is for extended exploratory reasoning before prose exists. - `conceptual-continuity-audit` is for checking whether later prose inherits earlier terms, distinctions, examples, source vocabulary, and argumentative pressure. - `relevance-necessity-audit` is for checking whether a sentence, quotation, example, distinction, or paragraph earns its place rather than adding fluff, padding, or heat without light. Automatic audit triggers: - If the manuscript contains `%%comments%%`, invoke `twork` before attempting fixes; treat inline comments as diagnostic material, not mere style notes. - If Nick asks for a full audit, asks whether a draft is working, or asks for a combined review, invoke `draft-audit` rather than relying only on the standing deep-writing rules. - If Nick asks whether author characterisations, quotations, or attributions are accurate, invoke `source-check`. - If Nick says a passage should mirror, inherit, use, or carry forward an earlier section, invoke `conceptual-continuity-audit`. - If Nick asks whether something is relevant, necessary, fluff, padding, an idea dump, or heat rather than light, invoke `relevance-necessity-audit`. ## Required Grounding Before drafting or rewriting more than a sentence-level local fix: 1. Read `references/voice.md` and `references/writing-practice.md`. 2. Read `references/topic-sentences.md` when writing paragraph openings, transitions, or section movement. 3. Read `references/routing.md` when the task involves inline comments, audit requests, or uncertainty about which writing skill should govern the work. 4. Read `references/final-audit.md` before presenting or editing prose. 5. Warm up with published work: - Read full paragraphs from Notes tagged `#published-paper`. - For a local fix, read at least 2 full paragraphs. - For a paragraph or more, read at least 4 full paragraphs across at least 2 papers. - Choose paragraphs doing similar work: introducing a position, clarifying a distinction, handling an objection, developing an example, or drawing a conclusion. 6. Keep a lightweight grounding log for the current chat: paper title plus a cue for each paragraph used. Prefer fresh paragraphs when possible. 7. Briefly state the grounding before writing: name the papers and say what the selected paragraphs show about the move you are about to make. Curated references are not enough for voice imitation. Published paragraphs are the standard. ## Drafting Rules - Use British English throughout, except inside quotations. - Face the subject matter directly. Do not narrate what the paragraph, argument, or section is doing. - Give each paragraph a local argumentative function. - Preserve detail. Do not turn Nick's specific material into generic summary. - Do not use illustrative example lists to bulk out paragraphs. Develop one example or state the general claim. - Do not write map-sentences such as "This section explores..." or "The argument shows..." unless Nick explicitly asks for a roadmap. - Do not impose tidy binary structure when the material is overlapping, plural, continuous, underdetermined, both/and, neither/nor, or not organised by that distinction. ## Reference Map - `references/voice.md` -- Nick's academic voice rules, including rhythm, dialectical conduct, and detail preservation. - `references/writing-practice.md` -- consolidated voice, rhythm, anti-patterns, value-language rules, and detail-preservation rules. - `references/routing.md` -- map from Nick's common comments and audit requests to the writing/audit skills that should govern the response. - `references/topic-sentences.md` -- paragraph-opening repertoire from Nick's published prose. - `references/no-example-lists.md` -- prohibition on shallow illustrative lists, triplets, and padding sequences. - `references/anti-metacommentary.md` -- sentence-level rules against prose that narrates its own argumentative function. - `references/final-audit.md` -- checklist before any prose is shown to Nick or written into a manuscript. - `references/formatting.md` -- quotation marks, italics, APA, British punctuation, bold restraint. - `references/prose-composition.md`, `references/examples.md`, and `references/vocabulary.md` -- published-prose grounding extracts copied into this skill for one-level access. ## Version History - 2026-05-14: Added audit routing for commented drafts, conceptual continuity, relevance/necessity, source checks, and full draft audits; synced with Codex. - 2026-05-06: Consolidated the former mandatory writing bundle into one skill with one-level references. `source-work` and `epistemic-discipline` remain separate cross-workflow skills. ### Assistant I’m going to treat this as a paragraph-level manuscript repair governed by `deep-writing-mode`, with `twork`, `voice-fix`, `writing-argumentative-paragraphs`, and `avoiding-not-but-binaries` active because the comments explicitly flag voice, metacommentary, example-listing, and false contrast risks. I’m also keeping the Carlson claim modest: I’ll use only what the preceding section has already established rather than inventing a fresh account of his view. ### Assistant Grounding used: “Growing the Image” for the way you introduce a tempting classification and then put pressure on it; “Hearing Spaces” for the way you clarify the relevant sense of a term before the technical material begins. I’m treating this paragraph as a bridge from Carlson’s design model into the LLM case, so I’ve removed the bicycle material rather than trying to rescue it. > Design appreciation is the first place to look. Carlson’s account treats functional artefacts as objects whose aesthetic character is bound up with their use: to appreciate such an object appropriately, we need some understanding of the purpose for which it was made and of how its features answer to that purpose. This makes design knowledge seem like the right starting point for LLMs. They are artefacts in the ordinary sense, produced through deliberate technical work and made available for use. If design appreciation applies straightforwardly, then the relevant appreciative knowledge will be knowledge of the system’s design. ### User why have you changed the topic sentence? ### Assistant You’re right. I shouldn’t have changed it. The topic sentence was already doing the local job: it ties directly back to Section 1 and keeps Carlson’s demand in view. I changed it because I was overcorrecting away from the broken bicycle material, but that was an unnecessary change. Keeping your topic sentence, I’d revise the paragraph like this: > Design appreciation, as Section 1 set it out, is one way of satisfying Carlson’s demand that appreciation answer to the kind of thing its object is. In the case of functional artefacts, this requires knowledge of the purpose for which the object was made and of how its features serve that purpose. This is why LLMs initially look like straightforward candidates for design appreciation. They are artefacts in the ordinary sense: produced through deliberate technical work and made available for use. If this is the right model, then the relevant appreciative knowledge will be knowledge of the system’s design. That keeps the topic sentence and removes the invented bicycle stuff, the example list, and the inaccurate Carlson gloss. ### User ok, update the note with that para ### Assistant I’ll replace only that Carlson/design paragraph in the note with the revised version that keeps your original topic sentence. ### Assistant Updated the note with that revised paragraph, keeping the original topic sentence and replacing only the broken paragraph before “An LLM is an engineered system…”. ### User fixed your paragraph but see that inline comment. use my publications as the inspiration for replacing it. Design appreciation, as Section 1 set it out, is one way of satisfying Carlson’s demand that appreciation answer to the kind of thing its object is. In the case of technical artefacts, this requires knowledge of the purpose for which the object was made and of how its features serve that purpose. This is why LLMs initially look like straightforward candidates for design appreciation. They are artefacts in the ordinary sense: produced through deliberate technical work and made available for use.%%the clause after the colon is vapid, redundant, meaningless.%% Might knowledge of the creation and functioning of LLMs be sufficient for their appreciation? ### Assistant Grounding: I’m taking the cue from your “If Midjourney is a tool, what sort of tool is it?” move in “Growing the Image”: state why the classification is tempting, then turn it into a question. I’d replace the vapid sentence with this: > Design appreciation, as Section 1 set it out, is one way of satisfying Carlson’s demand that appreciation answer to the kind of thing its object is. In the case of technical artefacts, this requires knowledge of the purpose for which the object was made and of how its features serve that purpose. This is why LLMs initially look like straightforward candidates for design appreciation. Their capacities are produced by human engineering, and the patterns encountered by users depend on decisions made before any prompt is entered. Might knowledge of the creation and functioning of LLMs be sufficient for their appreciation? This keeps the pressure on the next paragraph: what exactly is settled in advance by makers? ### User ok, update the note then ### Assistant I’ll update just that sentence in the same Section 2 note, leaving the rest of the paragraph unchanged. ### Assistant Updated the note. I replaced only the flagged sentence with: > Their capacities are produced by human engineering, and the patterns encountered by users depend on decisions made before any prompt is entered.