# @/Users/nickyoung/Downloads/The Environmental Aesthetics of Generative AI submitted to PPR 11 Jan... ## Skills Used - contemplate - moves-deck ## Retrieval Notes - Session id: `8a6e8c6a-a71d-46b7-bf79-0f88c731f00b` - Last activity: `2026-05-05T11:09:28.823Z` - Files touched: `1` ## Artifacts **Created:** - `/Users/nickyoung/Documents/paper-cuts-options.html` ## My Notes <!-- Add your notes here. This section is preserved across syncs. --> ## Conversation ### User @/Users/nickyoung/Downloads/The Environmental Aesthetics of Generative AI submitted to PPR 11 Jan 2026.md /contemplate Here is a chat that me and chatgpt had about ways of cutting down the word count of the attached paper. What do you think? do you agreee? any other possibilities? any suggestions of chatgpts that you don't like? make sure that you explain your analysis, justification, reasons etc etc. for your answer BEFORE giving me your final answer.Please give me a long, deep, and detailed answer, with lots of options/ideas: as we are in the 'kicking ideas around' stage, I want as much feedback as you can give me so that i have a lot to consider. CHAT: The Environmental Aesthetics of Generative AI submitted to PP/Users/nickyoung/Downloads/The Environmental Aesthetics of Generative AI submitted to PPR 11 Jan 2026.mdR 11 Jan 2026.md File This text is about 12,000 words (I think). How could we cut it down to 10,000, 9,000, 8000? Give us various options. Your brief with each suggestion is the text has to be as faithful to the original as possible, but in each an every suggestion you can make decisions about what counts as the core of the text and what is cutable, or whether it should be thinned out, or cut, or a mixture of the two. and little cuts or whole ideas etc.? you get the idea? obviously make sure that you explain your analysis, justification, reasons etc etc. for your answer BEFORE giving me your final answer. Please give me a long, deep, and detailed answer, with lots of options/ideas: as we are in the 'kicking ideas around' stage, I want as much feedback as you can give me so that i have a lot to consider. I’ll approach this as editorial triage rather than simple line-cutting: map the draft’s core and word distribution, then outline conservative-to-structural routes for 10k, 9k, and 8k. A first pass suggests the main text is closer to 13,000 words than 12,000, with the main cutting pressure falling on Sections 3, 5, and 6. First: my diagnosis before the options I would not treat this as a 12,000-word paper that merely needs a few hundred words shaved off. On my count, the main text is roughly 13,100 words, excluding references, footnotes, and the huge embedded base64 image data in the Markdown file. Including references and notes, it is closer to 14,000 words. So the real cutting task is approximately: Target Approx. main-text reduction needed Editorial meaning 10,000 about 3,100 words Same paper, tightened substantially 9,000 about 4,100 words Same thesis, but some sub-arguments must be compressed 8,000 about 5,100 words A structurally leaner version, not just a shorter draft The manuscript’s core argumentative architecture is clear: Carlson’s distinction between design appreciation and order appreciation; a technical account of LLMs as trained text-generating systems; negative arguments against person appreciation and simple design appreciation; then the positive proposal that LLMs can be appreciated through “semiotic physics” at the levels of outputs, chats, and models. That structure is stated very clearly in the introduction and then developed across the paper. The main issue is not that any one section is obviously irrelevant. The issue is that the paper is currently doing three things at once: It is introducing Carlson’s environmental aesthetics. It is educating readers about LLM mechanics. It is making a new positive proposal about semiotic physics and LLM appreciation. For a 10,000-word version, you can keep all three. For 9,000, you need to decide which one gets less space. For 8,000, the paper has to become less encyclopaedic and more thesis-driven. My overall view: the positive account should be protected. The paper is most original when it develops semiotic physics as the LLM analogue of geology in Carlsonian order appreciation. The most cuttable material is therefore not the semiotic physics material itself, but the extended preparatory apparatus: the LLM tutorial, the person-appreciation background, the detailed treatment of make-believe/thin agency/post-training, the design-aesthetics literature, and some of the analogies. Current word-weight by section Approximate main-text distribution: Section Approx. words Function Diagnosis Introduction 350 States thesis and roadmap Already efficient §1 Carlson / design / order / persons 1,400 Theoretical framework Important, but overdeveloped §2 What LLMs are 1,450 Technical grounding Too tutorial-like for shorter versions §3 LLMs as persons 2,800 Negative argument 1 Strong but too long and repetitive §4 LLMs as artifacts 1,730 Negative argument 2 / bridge Good but could be much tighter §5 Semiotic physics 2,430 Positive theory Core; cut scaffolding, not thesis §6 Levels of appreciation 2,670 Application/payoff Core, but examples and analogies can be thinned Conclusion 200 Summary Fine The biggest reservoirs of words are §3, §5, and §6. But the most painless cuts are probably in §2, §3, §4, and §5.1. Section 6 is long, but it is also where the paper shows why the positive account matters. The core that must survive Before thinking about cuts, I would define the paper’s non-negotiable core as follows: LLMs invite person-like and artifact-like aesthetic responses, but both are misleading if taken as primary. Carlson’s environmental aesthetics gives us a better model: appreciate things as what they are, in light of the kind of knowledge that makes their order visible. For LLMs, that knowledge is semiotic physics: an account of the regularities by which trained models propagate text. This allows us to appreciate outputs, chats, and models as manifestations of emergent semiotic order. Everything that directly serves that claim should stay. Everything else is negotiable. The essential components are: Carlson’s “appreciate things as what they are” constraint. Without this, the rejection of person appreciation and simple design appreciation loses force. The design/order distinction. You need this because the paper’s central move is to classify LLM appreciation as closer to order appreciation than ordinary design appreciation. A minimal account of person appreciation. You need enough to explain why person-aesthetic predicates require temporal depth, stable dispositions, projects, and evaluative commitments. A minimal technical account of LLMs. You need enough to show that LLM behaviour is not the direct execution of designer intentions, but emerges from training. The two negative arguments. You need to reject person appreciation and simple artifact appreciation, but you do not need to give each possible version equal space. Semiotic physics. This is the paper’s signature concept and should be protected. The three levels: outputs, chats, models. This is the payoff. But the level of detail can vary by target length. The most cuttable material Here is the hierarchy I would use. Lowest-risk cuts These preserve almost all intellectual content. 1. Repeated Carlson verdicts Many sections end by restating the same formula: under Carlson, we must appreciate LLMs as what they are, and therefore person/design appreciation fails. This is often helpful rhetorically, but it becomes repetitive. You can usually keep the first and strongest version, then let later applications be briefer. Potential saving: 250–400 words. 2. Over-explained technical examples in §2 The “cat sat on the” example, token IDs, exact probability percentages, temperature explanation, “capital of France” decoding example, doctor/patient example, embeddings, attention, RLHF, and product wrapper all work pedagogically. But together they make §2 feel like an LLM explainer. For this paper, the reader needs only enough technical detail to grasp emergence from training. Potential saving: 500–800 words. 3. Literature exposition that can become one sentence Forsey and Parsons/Carlson in §4 are useful, but the paper does not need a full mini-survey of design aesthetics. The same goes for some of the person-aesthetics material in §1. Potential saving: 300–600 words. 4. Double analogies The paper often gives more than one analogy where one would do: raku and Pollock; chemical physics and geology; video-game physics; farmer/geologist; mountain/divine sculpture plus Rembrandt/natural paint. These are vivid, but stacked analogies cost a lot. Potential saving: 400–700 words. 5. “Future work” paragraphs The culture-mirror paragraph near the end is interesting, but it explicitly says the thought is not developed. In a shorter version, it should go. The conclusion already has enough conceptual closure. Potential saving: 100–150 words. Medium-risk cuts These alter emphasis but not the central thesis. 6. Compress §3 from three sub-arguments to one unified section At present §3 treats make-believe, concessive thin agency, and post-training/chat personae separately. That is philosophically careful, but it creates repeated structure: possible person-like reading; why tempting; why insufficient; Carlsonian verdict. You could combine them into a single section called something like “Why Person Appreciation Fails”. Potential saving: 700–1,100 words. 7. Move Cross out of §3 Cross’s exploration paradigm is useful later for practical acquaintance and interactive aspection, but in §3 it slightly distracts from the person-appreciation argument. Since Cross returns naturally in §5.2 as a way of understanding prompting as exploration, the §3 discussion can be reduced or removed. Potential saving: 250–400 words. 8. Reduce §5.2 Practical Acquaintance The farmer/gardener/forester analogy is good, but it does not need the full elaboration. The key claim is simple: semiotic physics can be held theoretically or practically, just as environmental knowledge can be scientific or practical. Potential saving: 250–400 words. 9. Shorten the two-output contrast in §6.1 The reasoning-output example and the bee-text example both show semiotic physics. The bee text is more distinctive, because it reveals order under apparent chaos. The reasoning-output example is more familiar but less memorable. You can keep both in 10k, compress one in 9k, and probably choose one in 8k. Potential saving: 300–700 words. 10. Shorten the video-game physics analogy in §6.3 The game-physics comparison is helpful, but it is long. It names several games, explains each, then re-applies the point to LLMs. This could become three or four sentences. Potential saving: 250–400 words. Higher-risk cuts These would change the paper’s shape. 11. Drop one of the three levels of appreciation I do not recommend this unless you are forced to 8,000 words or below. The three-level structure — outputs, chats, models — is one of the paper’s strongest organizing contributions. The introduction announces it, and §6 gives the payoff. But if necessary, the most compressible level is chats. Outputs and models are more contrastive: outputs are specimens; models are the ground of order. Chats are important, but they can be folded into the discussion of practical acquaintance. Potential saving: 400–600 words. 12. Make §3 and §4 a single “Two Inadequate Models” section This is attractive for 9k or 8k. Instead of giving “persons” and “artifacts” full independent sections, you could present them as two failed assimilations before the positive account. Possible title: 3. Two Inadequate Models: Persons and Artifacts Then §4 becomes the turn toward emergent order. This would make the paper more streamlined and more obviously driven toward the positive proposal. Potential saving: 600–1,000 words beyond ordinary trimming. 13. Cut most of §2 and assume basic LLM literacy This is viable if the target journal readership is comfortable with contemporary AI. You would keep only: token prediction, training, emergent organization, post-training. The detailed pedagogical examples go. Potential saving: 700–1,000 words. Option set for a 10,000-word version A 10,000-word version can remain basically the same paper. You do not need to reconceive it. You need disciplined compression. 10k Option A: Conservative thinning, same structure This is the safest route. Section Current Target Cut Introduction 350 300 50 §1 Carlson/person framework 1,400 1,050 350 §2 What LLMs are 1,450 900 550 §3 Persons 2,800 2,150 650 §4 Artifacts 1,730 1,350 380 §5 Semiotic physics 2,430 1,850 580 §6 Levels 2,670 2,200 470 Conclusion 200 200 — This gets you to about 10,000 words while preserving the architecture. How to do it In §1, keep Carlson’s two recommendations and the design/order distinction. Compress the mountain/Rembrandt examples. The person-appreciation bridge should be one paragraph, not three plus footnotes. The current discussion of three ways to extend Carlson to persons is intellectually interesting, but for this paper you only need the conclusion: person appreciation requires a temporally extended subject with stable dispositions, projects, and evaluative commitments. In §2, remove most numerical detail. You do not need placeholder token IDs, exact probabilities, and multiple examples of autoregressive decoding. Say: LLMs tokenize text, represent tokens in learned vector spaces, generate continuations by sampling from probability distributions, and acquire their characteristic behaviour through pre-training and post-training. Keep the emergence point and the Olah “grown not programmed” idea, but shorten the quote. In §3, keep Mallory and Frankish, but compress Cross or move him later. The make-believe and thin-agency routes can be presented as two versions of the same temptation: either we pretend the LLM is person-like, or we thin personhood until it fits. Neither yields beauty-of-character appreciation. In §4, keep the conclusion that design appreciation has a foothold but not enough reach. Cut the full Forsey/Parsons exposition down to one paragraph. Keep Pollock because Carlson uses Pollock and it connects directly to order appreciation. Raku can be a clause or footnote. The section’s job is to get us from artifact-design to emergent order, not to survey the aesthetics of design. In §5, protect the positive concept. Cut the mechanistic-interpretability setup by half. Replace the long Wolfram quotation with a paraphrase. The Janus/Picca/Wolfram material is valuable, but currently it delays your own contribution. The reader should arrive at “semiotic physics” faster. In §6, keep all three levels, but make the examples do less total work. The reasoning-output example can be one compact paragraph; the bee-text example can carry the heavier burden because it better shows order emerging under apparently chaotic conditions. Why this is faithful This version preserves every major idea. The loss is mostly explanatory texture. It will feel less pedagogical and less expansive, but it will still be recognizably the same paper. 10k Option B: Protect the positive account; cut the negative setup harder This is my preferred 10k route if you want the paper to feel more original. The idea: do not distribute cuts evenly. Cut more from §§2–4 so that §§5–6 remain rich. Suggested cuts: Area Cut §1 person-appreciation setup 300–400 §2 technical explanation 700–800 §3 person argument 800–900 §4 design argument 500–600 §5 semiotic physics 300–400 §6 levels 200–300 This yields roughly the same total reduction as Option A, but it leaves the positive theory more developed. Why this may be better The paper’s novelty is not “LLMs are not people” or “LLMs are not ordinary artifacts.” Those are important, but they are preparatory. The distinctive contribution is the Carlsonian order-appreciation account, with semiotic physics as the relevant knowledge. So for a 10k draft, I would rather have a slightly brisker negative half and a more satisfying positive half. 10k Option C: Same sections, but remove one “explanatory register” At the moment, the paper alternates between: philosophical exposition; technical tutorial; analogy-rich explanation; literature positioning. The writing is clear, but because it does all four, it expands. A 10k version could keep all sections but decide: we will not teach the reader everything from scratch. That means cutting most “for readers unfamiliar with LLMs” material. This version assumes the reader knows the basics of generative AI and needs only enough to follow the philosophical point. This would especially affect §2 and §5.1. Sample compression of §2’s role Instead of walking through token IDs, probability percentages, temperature, and multiple examples, §2 could be compressed into something like: At the schematic level, an LLM is an autoregressive token predictor. It represents textual inputs as tokens, maps those tokens into learned vector spaces, and generates output by repeatedly sampling a next token conditioned on the preceding context. Pre-training adjusts billions of parameters so that the system comes to approximate regularities in large text corpora; post-training then biases this predictive machinery toward assistant-like patterns of response. The crucial point for our purposes is that the model’s aesthetically salient behaviour is not specified as a set of authored rules. Designers specify architectures, objectives, datasets, and post-training regimes, but the particular organisation of the trained system emerges from optimisation. That captures almost everything needed for the later argument in far fewer words. Option set for a 9,000-word version At 9,000 words, you are no longer merely tightening. You need to decide what kind of paper this is. I would recommend making it more explicitly a positive philosophical proposal, not a comprehensive map of all possible AI-aesthetic stances. 9k Option A: Balanced, still recognizably the same paper Suggested target distribution: Section Target Introduction 300 §1 Carlson framework 1,000 §2 What LLMs are 850 §3 Persons 1,900 §4 Artifacts 1,200 §5 Semiotic physics 1,800 §6 Levels 1,750 Conclusion 200 Total 9,000 What changes? The paper still has the same structure, but each section becomes more argumentative and less expository. The main sacrifices: §2 becomes a schematic technical account, not a tutorial. §3 becomes less exhaustive. §4 drops most design-aesthetics detail. §6 keeps the three levels but compresses the examples. What stays? Carlson’s core distinction. Person appreciation requires temporal/evaluative structure. LLMs are trained/grown rather than micro-designed. Design appreciation is partly applicable but insufficient. Semiotic physics is the right knowledge for order appreciation. Outputs, chats, and models are all appreciable. This is probably the best compromise if the target is exactly 9k. 9k Option B: Merge §§3 and 4 into one section This would give the paper a much cleaner middle. Possible structure: Introduction Carlson: Design and Order What LLMs Are Two Inadequate Models: Persons and Artifacts Semiotic Physics Outputs, Chats, Models Conclusion The merged section would say: Person appreciation fails because LLMs lack temporally extended agency. Simple design appreciation fails because their salient order emerges from training rather than designer specification. These failures point toward order appreciation. Why this works The current §§3 and 4 are both negative arguments. They each say, in effect: “Here is a tempting classification; here is why Carlson’s framework resists it; here is why we need the positive account.” That repetition is structurally useful in a long paper but costly in a shorter one. What to cut inside the merged section Reduce Mallory to one paragraph. Reduce Frankish to one paragraph. Reduce post-training/personae to one paragraph. Reduce Forsey/Parsons to one paragraph. Use Pollock as the main hybrid-order analogy. Remove raku or mention it only briefly. Remove Cross from this section. Potential saving This could save 900–1,300 words while making the paper feel more direct. Faithfulness cost Moderate. You lose some dialectical nuance, but the core claims remain intact. 9k Option C: Aesthetics-audience version If the paper is going to aestheticians, I would keep more Carlson and less LLM mechanics. Keep Carlson’s design/order distinction. Person appreciation discussion. Pollock/design/order material. Semiotic physics as the analogue of geology. Outputs/chats/models. Cut harder Token IDs and probabilities. Temperature. “Capital of France” example. Some of the embeddings/attention detail. Some RLHF mechanics. Why Aesthetics readers need to be persuaded that Carlson is being extended responsibly. They do not need a full technical primer. The technical section should do one job: establish that the aesthetically salient order is emergent. Likely distribution §1 remains around 1,100. §2 drops to 600–700. §3 and §4 stay moderately developed. §5 and §6 remain strong. This version is faithful to the philosophical project and likely reads more like an aesthetics paper than a philosophy-of-AI explainer. 9k Option D: AI/philosophy-of-technology version If the expected readers already know LLMs but may not know Carlson, do the opposite. Keep A fuller explanation of Carlson. The negative arguments against person/design appreciation. Semiotic physics. Cut Most of §2 technical basics. Some of the Janus/Picca/Wolfram background. Some of the illustrative examples in §6. Why Such readers will already understand token prediction, embeddings, RLHF, and model “vibes.” You can trust them. The paper’s added value is the aesthetic framework. Option set for an 8,000-word version An 8,000-word version must be structurally lean. I would not try to keep the current section-by-section density. You need a sharper version of the paper. 8k Option A: Thesis-first, positive-account version This is my preferred 8k strategy. Suggested target distribution: Section Target Introduction 250 §1 Carlson framework 850 §2 What LLMs are 750 §3 Persons/artifacts as failed models 1,600 §4 Semiotic physics 1,700 §5 Applications: outputs, chats, models 1,550 Conclusion 200 Total 8,000 This version has only five substantive sections, not six. New structure Introduction Carlson’s distinction: design and order Why LLMs are neither persons nor ordinary artifacts Semiotic physics Outputs, chats, and models Conclusion What gets compressed? Current §2 becomes part of the “what LLMs are” setup but is shorter. Current §§3 and 4 become one section. Current §§5 and 6 remain the centre of gravity. §6.1’s examples are sharply reduced. §6.2 chats is folded into the practical-acquaintance discussion. §6.3 models remains, but the video-game analogy is shortened or cut. Why this is faithful The paper still makes the same argument. But it no longer gives every objection and every analogy full development. It becomes more elegant and less encyclopaedic. 8k Option B: Keep all sections, but turn several into “remarks” This keeps the current architecture but radically compresses certain parts. For example: §2 becomes “A schematic note on LLMs.” §3 becomes “Why person appreciation is misplaced.” §4 becomes “Why design appreciation is insufficient.” §5 and §6 remain the main sections. Pros The paper’s current shape remains visible. Cons Some sections may feel underdeveloped. If §3 has three subsections but only 1,500 words, the subsections may look fussy. In an 8k paper, I would probably remove the subsection structure in §3. 8k Option C: One-output example only The current §6.1 uses two contrasting examples: ordinary reasoning-style output and the more chaotic bee text. The contrast is useful, but expensive. For 8k, choose one. If you keep the reasoning example The paper becomes more accessible and less weird. It shows that even mundane assistant prose can be aesthetically appreciable under semiotic physics. If you keep the bee example The paper becomes more vivid and distinctive. The bee example better shows the point that semiotic physics reveals order where a reader might initially see chaos. I would keep the bee example and compress the reasoning example to a sentence or two. The bee case is more rhetorically powerful because it demonstrates the value of the framework. Ordinary reasoning outputs are easier to understand but less revealing. 8k Option D: Drop model comparison and keep model appreciation abstract Current §6.3 says users talk about model “vibes,” then develops an analogy with videogame physics engines, then discusses Claude/GPT/Gemini differences, latent behavioural space, benchmarks, and finally culture as mirror. For 8k, the section could simply say: A model is the ground of semiotic order. Its “vibe” is not personality but characteristic text-propagation dynamics. Appreciating a model is appreciating that stable semiotic profile across possible outputs and chats. This differs from capability evaluation and safety testing. That would preserve the philosophical claim while cutting much of the illustrative machinery. Potential saving 400–600 words. Faithfulness cost Low to moderate. The claim survives; the section becomes less colourful. A modular cut menu Here are specific cut units you can mix and match. Cut Approx. saving Faithfulness cost My view Remove/shorten intro roadmap 70–120 Very low Do it Compress Carlson quote + examples in §1 200–300 Low Do it Reduce person-appreciation background in §1 250–400 Low/moderate Do it for 10k+ Cut footnote on performance personalities 80–120 Very low Do it unless needed Compress LLM token/probability example 300–450 Low Do it Remove “capital of France” decoding example 100–150 Very low Do it Shorten embeddings/attention/RLHF explanation 250–400 Low/moderate Do it carefully Shorten Olah quote 100–150 Low Do it Remove Cross from §3 250–350 Low Do it; maybe reuse in §5.2 Merge make-believe and thin agency verdicts 300–500 Low/moderate Good for 9k Compress post-training/personae §3.3 300–450 Low Do it Reduce Forsey/Parsons design literature 300–450 Low Do it Use Pollock OR raku, not both 250–400 Low/moderate Keep Pollock Compress mechanistic-interpretability/geology analogy 250–350 Low Do it Paraphrase Wolfram instead of quoting 150–250 Low Do it Cut Wolfram’s three-object list 80–120 Low Do it for 9k/8k Reduce “force-talk is literal” defence 150–250 Moderate Keep but compress Compress practical acquaintance/farmer analogy 250–350 Low Do it Shorten reasoning-output example 300–500 Low/moderate Do it Shorten bee-text analysis 200–300 Moderate Only if needed Compress chat-appreciation §6.2 200–300 Low Do it Shorten video-game physics analogy 250–400 Low Do it Cut culture-mirror paragraph 100–150 Very low Do it Prune unused references 100–200 Only if refs count Do it if needed A 10k cut can be achieved almost entirely from the low-risk and medium-risk items. A 9k cut requires several medium-risk cuts. An 8k version requires at least one structural cut: merging sections, dropping an example, or compressing one of the three levels. Section-by-section recommendations Introduction The introduction is already doing the right work: it states the two temptations, introduces order appreciation, announces semiotic physics, and gives the paper structure. I would not cut much here. But I would shorten the roadmap. Readers do not need a full sentence for every section. Possible cuts Current roadmap style: Section 1 sets out... Section 2 describes... Sections 3 and 4 develop... Sections 5 and 6 develop... Compressed version: We first introduce Carlson’s distinction between design and order appreciation and give a schematic account of LLMs. We then reject person-based and simple design-based models before developing semiotic physics as the knowledge appropriate to order appreciation of outputs, chats, and models. That saves maybe 70–100 words and is cleaner. §1: Appreciating Design, Appreciating Order This section is foundational but slightly overbuilt. Keep Carlson’s recommendation: appreciate things as what they are, in light of appropriate knowledge. Design appreciation versus order appreciation. The idea that knowledge guides “aspection.” The person-appreciation bridge. Cut or compress The Rembrandt and mountain examples both make the same point: misclassification distorts appreciation. Keep one, or compress both into one sentence. The person-appreciation discussion is the biggest opportunity. The three possible ways Carlson might accommodate persons are interesting, but the later argument only needs one result: person appreciation presupposes a temporally extended subject with stable dispositions, projects, and evaluative commitments. The current section spends time considering multiple theoretical options before saying you do not need to decide among them. That is usually a sign of cuttable material. Possible target 10k: reduce to 1,050 words. 9k: reduce to 1,000 words. 8k: reduce to 850 words. §2: What LLMs Are This is one of the clearest places to save words. The section currently explains tokenization, token IDs, probability distributions, temperature, autoregressive decoding, pre-training, weights, embeddings, attention, post-training, RLHF, chat products, and emergence. That is all accurate and helpful, but not all necessary. The essential point The only technical claim the argument really needs is this: LLMs are artifacts whose behaviour arises from trained statistical organization rather than from directly specified rules or intentions. Everything else supports that. Keep Tokens and next-token prediction. Training as parameter adjustment over large corpora. Embeddings/attention as learned organization. Post-training as shaping assistant-like behaviour. Emergence from training rather than direct specification. Cut Placeholder token IDs. Exact probability percentages. Extended temperature discussion. “Capital of France” step-by-step decoding. Doctor/patient example if embeddings already do the work. Some repeated claims that the model manipulates numbers, not meanings. Possible target 10k: 900 words. 9k: 850 words. 8k: 750 words, or even 600 if the readership knows LLMs. The shorter version should feel less like “What is an LLM?” and more like “Which features of LLMs matter for aesthetic classification?” §3: Appreciating LLMs as Persons This is the largest single section and therefore a major cutting site. The argument is good: users talk about personality/vibe; make-believe does not justify person appreciation; thin agency does not give us beauty-of-character; post-training creates assistant personae but not temporally extended subjects. The problem is that the section repeats the same dialectical shape three times. Keep The everyday temptation: users experience models as having “personality” or “vibe.” Mallory as the make-believe/fictionalism route. Frankish as the thin-agency route. Post-training/personae as the strongest objection. The conclusion: person-like response profiles are not persons. Cut or compress Cross should probably not be doing work here. His exploration paradigm is more useful later, where you reinterpret interaction as aspection rather than collaboration. The “Carlson gives us a verdict” paragraphs can be shortened. Once the criterion is established, you do not need to fully restate it after each sub-argument. The post-training subsection can be shorter. It is important, because it anticipates the objection that chat-optimized assistants are the relevant objects. But the answer is simple: post-training stabilizes response profiles; it does not create a life, projects, or evaluative commitments. Possible target 10k: 2,100–2,200 words. 9k: 1,800–1,900 words. 8k: 1,500–1,600 words, probably merged with §4. §4: Appreciating LLMs as Artifacts This section is important because the paper must not look as though it denies that LLMs are artifacts. It needs to say: yes, design appreciation applies, but only partially. The current section does this, but it spends a lot of space on design-aesthetics literature and analogies. Keep LLMs are artifacts. Design appreciation has a foothold. But the aesthetically salient order emerges from training. Therefore design appreciation alone is insufficient. Pollock/hybrid cases show why emergent order can require another mode of appreciation. Cut Reduce Forsey and Parsons/Carlson to a compact literature-positioning paragraph. Remove specific current model names unless needed; they will date the paper and cost words. Use either raku or Pollock. I would keep Pollock because Carlson himself uses Pollock, and it ties directly to the paper’s framework. Avoid repeating §2’s details about embeddings, attention, RLHF, etc. You can refer back. Possible target 10k: 1,300–1,350 words. 9k: 1,150–1,200 words. 8k: around 1,000–1,100 words, possibly as part of a merged negative section. §5: Semiotic Physics This is the conceptual heart of the paper. I would cut here carefully. The section currently does several things: distinguishes semiotic physics from mechanistic interpretability; introduces Janus, Picca, Kirchner/metasemi, and Wolfram; defines the level of perceivable textual regularities; defends force-talk; explains how semiotic physics changes aspection; and introduces practical acquaintance. Keep Semiotic physics as the right kind of knowledge. The analogy with geology: it makes order visible at the perceivable level. The focus on textual regularities: semantic attraction, contextual threading, modal inertia, alignment pressure. The claim that force-talk is not personification. Practical acquaintance as the user-level analogue of scientific knowledge. Cut The mechanistic-interpretability comparison can be half as long. The Wolfram quote can be paraphrased. The Janus/Picca/Wolfram literature setup should move faster to your contribution. The “three main objects” from Wolfram can go unless you actively use them later. Practical acquaintance can be shorter and perhaps folded into §6.2 on chats. Possible target 10k: 1,850 words. 9k: 1,800 words. 8k: 1,600–1,700 words. I would not reduce §5 below about 1,500 unless the whole paper is being radically reconceived, because this is where the original term and framework are earned. §6: Levels of Appreciation This is the payoff section. The paper promises that semiotic physics guides appreciation at three levels: outputs, chats, and models. It should deliver on that promise. Keep The three-level distinction. At least one concrete output example. Prompting as intervention/aspection. Model “vibe” as characteristic semiotic order, not personality. Distinction from benchmarking, safety testing, and capability evaluation. Cut The opening analogy — output/tree, chat/forest, model/biosphere — is useful but can be shorter. The reasoning-output example can be reduced substantially. It is familiar and helpful, but the bee-text case is more revealing. The chat section can be compressed. Its core claim is: chats are temporally extended environments in which context accumulates and prompting functions as navigation. The model section should keep the “vibe” point, because that links back beautifully to the rejected person-appreciation model. But the video-game physics analogy can be shortened, and the culture-mirror paragraph should probably be cut in any version under 10k. Possible target 10k: 2,200 words. 9k: 1,750 words. 8k: 1,500–1,600 words. Different philosophical “versions” of the paper Here are several higher-level options, depending on what you want the shorter paper to feel like. Version 1: The full Carlsonian extension paper Best for: 10k This version says: “Here is a careful extension of Carlson’s environmental aesthetics to LLMs.” It keeps: Carlson in some detail. Person appreciation. Design appreciation. Semiotic physics. Three levels. It cuts mostly explanatory detail. Advantage Most faithful to the current manuscript. Disadvantage Still somewhat broad. Version 2: The semiotic physics paper Best for: 9k This version says: “The real contribution is semiotic physics as the right knowledge for appreciating LLM order.” It compresses: person appreciation; design appreciation; LLM technical tutorial. It protects: §5; §6; the analogy between semiotic physics and geology. Advantage Most original and probably strongest as an article. Disadvantage Some readers may want more defence of the rejected alternatives. Version 3: The “not persons, not tools” paper Best for: 9k or 10k This version foregrounds the dialectic: LLMs seem person-like. LLMs seem artifact-like. Both are partly misleading. Order appreciation explains why. It gives the negative arguments more weight and makes the positive account the resolution. Advantage Very readable and dialectically clean. Disadvantage Semiotic physics may feel slightly less developed unless protected. Version 4: The “LLMs as environments” paper Best for: 8k This version makes chats the central object. Outputs and models are discussed, but the focal case is the chat as a generative environment. Structure Carlson on environments. Why LLM chats are not conversations with persons. Why they are not merely tool outputs. Chats as semiotic environments. Semiotic physics as the relevant knowledge. Advantage Very elegant and focused. Disadvantage Less faithful to the current three-level ambition, especially model-level appreciation. Version 5: The model-appreciation paper Best for: 8k if you want the boldest version This version centres the claim that models themselves, not merely outputs, are objects of aesthetic appreciation. The paper would treat outputs and chats as evidence of model-level order. It would compress the output/chat sections and expand the “vibe as semiotic order” argument. Advantage This is probably the most distinctive claim. Disadvantage It sacrifices some of the environmental analogy’s natural richness, because “chat as environment” becomes secondary. My preferred plans If you want 10,000 words Use 10k Option B: protect the positive account and cut the setup. Do not merge major sections. Keep the architecture. But reduce §2, §3, and §4 aggressively. Concrete plan: Cut §2 by 600–700 words. Cut §3 by 700–800 words. Cut §4 by 400–500 words. Cut §5 by 300–400 words. Cut §6 by 300–400 words. Cut §1 by 250–350 words. Remove culture-mirror paragraph and trim roadmap. This will get you very close to 10k without conceptual damage. If you want 9,000 words Use 9k Option B: merge the negative material. I would create one section on failed person/artifact models. This avoids repeated Carlsonian verdicts and makes the paper move faster toward semiotic physics. Concrete plan: Reduce §2 to about 850 words. Merge §§3 and 4 into a 3,000-word section or two shorter back-to-back sections. Keep §5 around 1,800 words. Keep §6 around 1,750 words. Drop culture-mirror paragraph. Shorten the video-game analogy. Compress one of the §6.1 examples. This is probably the best version as an article: substantial, but not baggy. If you want 8,000 words Use 8k Option A: restructure. Trying to keep the current structure at 8k will make every section feel starved. Instead, make the paper cleaner: Introduction. Carlson. What LLMs are and why person/design models fail. Semiotic physics. Outputs, chats, models. Conclusion. Concrete plan: §1: 850 words. §2: 750 words. Person/design critique: 1,600 words. Semiotic physics: 1,700 words. Applications: 1,550 words. Intro/conclusion: 450 words. This is not merely a cut version. It is a new, sharper version of the paper. Final recommendation I would aim for 9,000 words, not 10,000 or 8,000. At 10,000, the paper will be faithful but may still feel like it has too much preparatory machinery. At 8,000, you will probably lose some of the nuance that makes the argument philosophically careful. 9,000 is the sweet spot: it forces you to remove over-explanation, but still leaves enough room for Carlson, LLM emergence, the two rejected models, semiotic physics, and the three levels of appreciation. The highest-value cuts are: Compress §2’s technical tutorial. Merge or heavily streamline §3’s three person-appreciation routes. Reduce design-aesthetics literature in §4. Use Pollock rather than Pollock plus raku. Paraphrase Wolfram and shorten the literature setup in §5. Shorten the reasoning-output example. Shorten or cut the video-game physics analogy. Cut the culture-mirror paragraph. Remove repeated Carlsonian verdicts. Prune footnotes and unused references if the word limit includes them. One small technical note: the Markdown file contains a very large embedded base64 image after the references. That will not matter for ordinary word count, but it bloats the file enormously and may create submission or processing problems. I would replace it with a normal image file reference or omit it from the submitted text file. ### User [Request interrupted by user] ### User @/Users/nickyoung/Downloads/The Environmental Aesthetics of Generative AI submitted to PPR 11 Jan 2026.md here's the paper if you don't have it already The Environmental Aesthetics of Generative AI 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. The paper proceeds as follows. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers how person appreciation might fit into this framework. Section 2 describes what LLMs are at a schematic level: token-based predictors trained on large text corpora and shaped by reinforcement learning. Sections 3 and 4 develop the negative arguments: §3 argues against appreciating LLMs as persons; §4 argues against simple design appreciation. Sections 5 and 6 develop the positive account: §5 introduces semiotic physics as the right kind of knowledge for order appreciation of LLMs; §6 shows how this framework guides appreciation at the three levels. 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. We start with Carlson's general recommendation for aesthetic appreciation: take things as what they are, and look at them in the light of the right kind of knowledge. as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. (Carlson, 2000, p. 6) This captures something quite intuitive about how we appreciate nature versus how we appreciate works of art. Consider what goes wrong when we depart from it. If we accept, as a majority do in the 21st century, that mountains and cliff faces were not items crafted by some divine artisan but by natural forces, then appreciating them as if they were God-crafted artifacts, seems wrong-headed (cf. Carlson, 2000, Chapter 8). Similarly, if someone were to study a painting by Rembrandt, believing that it was in fact the product of natural forces slopping paint together, they would be seen as appreciating the object in question in a sub-optimal way (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object in question really is. Different sorts of thing, Carlson says, require different modes of appreciation. Things like artworks and non-art artifacts, (e.g. laptops, hammers, washing machines), merit what he calls design appreciation. Things which are not designed, primarily for Carlson, the natural environment, warrant what he calls order appreciation. For both works of art and everyday objects, Carlson talks in terms of design appreciation. With paradigmatic artworks, we recognise them as creations of designers – objects where "every one of their features is the result of a decision by the artist" (Carlson, 2000, p. 109). Our appreciation centres on the relationship between the initial design and its embodiment: we consider whether the artist succeeded in their undertaking, how they worked with their materials, what constraints they faced, and whether the outcome realises their vision. This same approach extends to designed 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 a chair, a kettle, or a bridge invite the same style of attentive appraisal as a painting – guided by knowledge of ends, materials, constraints, and the fit between purpose and realisation. In order appreciation, we face objects that show order but have no designer behind them. Natural environments are the main case. Here there are no intentions to recover or evaluate. Instead, we find patterns and structures created by forces – geological, biological, meteorological – operating without purpose. Our task shifts from evaluating success against intention to understanding how these forces have shaped what we observe. Carlson describes its general form: On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them. Moreover, the objects are selected in part by reference to a general nonaesthetic and nonartistic story that helps make them appreciable by making this order visible and intelligible. Awareness and understanding of the key entities – the order, the forces that produce it, and the account that illuminates it – and of the interplay among them dictate relevant acts of aspection and guide the appreciative response. (Carlson, 2000, p. 119) In design appreciation there is a split between the planner and the product: intentions, plans, and constraints precede and shape the artifact. In order appreciation there is no such split. In design, form precedes matter and is imposed upon it; in nature, order is immanent in the matter itself. In both modes, however, appropriate knowledge guides acts of aspection – what to look for, which dependencies matter, where to set boundaries, and how to draw contrasts (Carlson, 2000, p. 50). But the character of this knowledge differs. In designed cases, we need functional and technical understanding: what the designer intended and what constraints they faced. This knowledge shows us how ends and means relate. In natural cases, we need the appropriate scientific account – geomorphology, for instance, reveals how landforms develop over millennia. Any number of natural sciences might serve this role, and they are not mutually exclusive: the same landscape might be illuminated by geology, botany, and ecology together. Without such knowledge, natural structures might look accidental or chaotic; with it, we see them as effects of identifiable processes (Carlson, 2000, pp. 50, 60–61). Selecting a particular viewpoint or timeframe serves only to reveal the order more clearly, not to impose our own design. Once a specific scientific account is in play, some cases will show the relevant order better than others, preventing the worry that everything becomes equally appreciable (Carlson, 2000, pp. 118–119). The fundamental rule remains: do not project a planner where there is none; where something is made to a plan, judge it as such. It could be argued, however, that Carlson’s approach to aesthetics overlooks another important category of object of appreciation: people. In ordinary life we not only admire landscapes and artifacts; we admire people too – their wit, their manner, their steadiness. Some philosophers have taken this practice seriously, investigating the aesthetic appreciation of personality – sometimes termed “beauty of character” – and asking whether traits such as kindness, wit, or courage can be aesthetically as well as morally valuable (Gaut 2007; Paris 2018). Carlson's second recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge – familiarity with a life (real or fictional) and a sense of the values and dispositions that organise it. We do not admire kindness in the abstract, but this person's pattern of generous responses given who they are and what they have faced. As Parsons stresses, such knowledge is typically built up through direct interaction, careful biography, or the more precarious route of gossip (Parsons 2023, 297–299)., Although Carlson does not consider person appreciation, it is not difficult to imagine ways in which his framework might be extended or modified to accommodate it. One option is to treat “persons” as a third category alongside natural items and artifacts, with their own distinctive mode of appreciation anchored in their status as subjects rather than as environments or tools. A second option is to treat the appreciation of character as a special case of order appreciation: we focus on the psychological, social, and biographical forces that shape a life, much as we attend to geological and ecological forces in a landscape. A third option would be to emphasise the ways in which personalities are, at least in part, self-shaped, and to appreciate them as self-designing projects – a thought that has obvious attractions for existentialist traditions. On all of these views, however, Carlson’s second recommendation still applies: aesthetic appreciation is guided by substantive background understanding of what persons are like and how their traits hang together over time. For present purposes, we need not decide which of these options is correct. It will be enough to note that person-based aesthetics, where it exists, presupposes a rich conception of the subject as a temporally extended agent with relatively stable dispositions, projects, and evaluative commitments, grasped under a suitable body of knowledge. In the rest of the paper, when we consider whether we can aesthetically appreciate LLMs “like people”, it is this sort of person-directed appreciation – and this Carlsonian constraint – that will be in the background. 2. What LLMs Are Carlson recommends we appreciate things for what they are. So what are LLMs? In this section we set the ground for appreciation by explaining the technical reality of these systems: how they process text as numerical tokens, calculate probabilities through learned parameters, and generate responses through iterative sampling. In later sections (§3 and §4) we use this reality – which differs in certain respects from that of traditional designed artifacts – to assess whether LLMs can be aesthetically appreciated as persons or as designed artifacts. Consider what happens when an LLM encounters the text "The cat sat on the". The system first breaks this into tokens-discrete units like words or word-parts. Importantly, each token is assigned a numerical ID (in this example, the ID numbers we use are just placeholders): ‘The' might become 464, 'cat' becomes 3857, 'sat' becomes 4521, and so on. The model works entirely with these numbers, not with words or meanings. It then assigns probabilities to possible continuations: token 5687 (which represents "mat") might have a 38% chance of appearing next, token 2931 ('floor') 22%, token 8104 ('chair') 15%, token 9823 ('roof') 8%, with thousands of other possibilities each assigned their own probability. The system does not simply pick the highest-probability token. Instead, it randomly samples from these probabilities. A parameter called temperature which can be set by the user controls how much randomness is involved. When temperature is set to zero, the model always picks the most probable token. This produces text that quickly becomes repetitive – the same phrases appearing again and again. When temperature is higher, around 0.8, the model sometimes picks less probable tokens. This leads to variation that looks creative. But it is randomness, not creativity. The model is rolling weighted dice, not making choices. The system selects one token – say "mat" – and appends this new token to create a longer sequence "The cat sat on the mat". It then calculates entirely new probabilities for what token should follow the extended sequence. Token by token, the system builds what appears to be coherent text through repeated numerical operations. No feasible amount of text could cover all the sequences the model might encounter, and storing all these combinations would be impossible anyway. Instead, the model learns general patterns during an initial pre-training phase: exposure to vast quantities of text – billions of pages from books, websites, and other sources – while learning to predict the next token in each sequence. The model begins with millions of numerical parameters (called 'weights') set to random values. Through repeated exposure, the system learns statistical regularities: which tokens tend to follow other tokens, which token sequences co-occur, how sequences typically unfold. The model stores these patterns as adjustments to its numerical parameters – decimal numbers that shape how strongly different tokens associate with each other. After seeing 'doctor' followed by 'patient' thousands of times, parameters adjust so that token 1245 ('doctor') increases the probability of token 7823 ('patient') appearing nearby. When the model wrongly predicts one token but the actual next token was another, the parameters shift slightly to make the correct token more likely in similar future contexts. After billions of such adjustments during pre-training, the model approximates the statistical patterns of human language. It does not learn that doctors treat patients or that cats are animals; it learns that, in the training distribution, certain number sequences follow others with certain frequencies. No programmer writes rules about grammar or meaning. The patterns emerge from exposure to text. The result is what is called a base model: a large, general-purpose text continuation engine. A key feature of this continuation engine is the embedding. In addition to its numerical ID, each token is represented as a vector – a list of numbers – that positions it in a high-dimensional mathematical space. Tokens that appear in similar contexts end up near each other in this space. 'Cat' sits near 'dog' because both appear after 'the', both can be followed by 'sleeps', both fit in phrases like 'fed my _'. The model learns these positions through pre-training, not from programmed definitions. This is how meaning emerges in the model: not from understanding concepts but from tracking which words appear in similar contexts. The transformer architecture adds a mechanism called attention. This allows the model to connect related words even when they are far apart in a sentence. For instance, in 'The cat that chased the mouse sat on the mat', the model needs to know that 'sat' refers back to 'cat', not to 'mouse'. Through training, different attention heads can specialise in tracking different kinds of relationships. They do so by building specific representations not only for single words but also for sequences of words. Some attention heads track which pronouns refer to which nouns, others connect verbs to their subjects across long sentences. No one programmes these specific functions. They emerge because tracking these relationships helps minimise prediction error. In use, the model generates text through autoregressive decoding: each newly generated token gets added to the context, creating a new, longer sequence for which the model must calculate fresh probabilities. Given an input like 'What is the capital of France?', the model computes probabilities, selects token 464 ('The'), appends it to create 'What is the capital of France? The', recalculates probabilities for this new sequence, selects token 2341 ('capital'), and continues this mechanical process – 'The', 'capital', 'of', 'France', 'is', 'Paris' – until reaching a stopping point. Each step is purely computational: multiply numbers, add numbers, select token, repeat. The pre-training we have described so far teaches the model statistical patterns of language and yields a base LLM. In practice, most chat-oriented systems undergo a further post-training phase. After pre-training, the base model is fine-tuned on examples of instructions and responses, and then adjusted by RLHF, a process in which human raters evaluate the model's responses – rating them for helpfulness, accuracy, appropriate tone. The model then adjusts its parameters to produce more responses like those rated highly and fewer like those rated poorly. Post-training shapes the model's conversational norms: when to express uncertainty ('I'm not sure, but...'), when to decline requests ('I cannot help with...'), how to structure explanations ('Let me break this down...'). RLHF makes responses more consistent, more helpful, and more aligned with human expectations. But it operates through the same fundamental mechanism – adjusting numerical parameters to match patterns in the training data. The model learns which response patterns get high ratings, not why those patterns are appropriate or what social purposes they serve. The result is a chat-optimised model: the same predictive core, now biased towards a certain family of outputs that look like the moves of a cooperative assistant. When this chat-optimised model is embedded in a product – given a system prompt, safety filters, a memory policy, and a user interface – it becomes the chatbot that users encounter. What users describe as a model’s “personality” or “vibe” is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core. At several points in the preceding description, we saw that specific features of how LLMs behave are not programmed but emerge from training. Designers specify the architecture and training objectives, but the organisation of the trained system – the structures that underlie its behaviour – emerges from the training process. With a chair or a bridge, as we noted in Section 1, form precedes matter and is imposed upon it. With an LLM, designers create the conditions under which organisation will emerge, but they do not impose that organisation directly. Chris Olah, a co-founder of Anthropic, captures this vividly: one useful way to think about neural networks is that we don't program them... we don't make them... we kind of 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... 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) Carlson's recommendation to appreciate things for what they in fact are might seem to warn against taking such a comparison seriously – LLMs are not biological organisms, and their 'growth' is a computational process of parameter adjustment, not biological development. But the comparison is apt: the organisation of a trained neural network is not specified by its designers but emerges from a process they set in motion. This distinguishes LLMs from traditional designed artifacts, and, as we shall see, it has consequences for what kind of appreciation is appropriate. 3. Appreciating LLMs as Persons We sometimes appreciate persons aesthetically, responding to traits such as warmth, wit, or steadiness as ‘beautiful’ or ‘ugly’ features of character. Our appreciation of others goes beyond their physical appearance. You might admire or enjoy your friend's warmth or eccentricity, or a stand-up comic's quick wit, or a celebrity's self-deprecating demeanour; you might even appreciate the personalities of fictional characters: Gatsby's enigmatic, dream-chasing idealism; Ron Swanson's libertarian gruffness. It is therefore tempting to think that our appreciation of LLMs might be modelled on our appreciation of people. Many users already talk this way, describing their favourite models in terms of ‘personality’ or ‘vibe’. However, the technical description in the previous section presents LLMs as systems that tokenise text, manipulate numerical vectors, and generate continuations by sampling from learnt probability distributions, with a further post-training phase that biases them towards a certain assistant-like pattern of response. Nothing in that description straightforwardly resembles a subject with beliefs, intentions, or a life-history; there is no obvious place for character traits, projects, or personal development. Given Carlson’s recommendation that we should appreciate things as what they in fact are, and in the light of the right kind of knowledge, it is not yet clear that person-based aesthetic predicates are being applied to the right kind of object. In this section we ask whether, under that recommendation, there is any appropriate person-based aesthetic stance towards LLMs. We consider, in turn, make-believe approaches, concessive mindedness approaches, and a line of thought based on post-training and chat personae, and argue that none yields a satisfactory model of person-based aesthetic appreciation of LLMs themselves. 3.1 Make-believe approaches Start with the make-believe route. If we ask ordinary users whether they literally believe that a chatbot is a person, many will concede that they do not. They may talk to a model as if it were a friend or a colleague, and they may feel heard, reassured, or amused, but when pressed they acknowledge that they are interacting with a computational system rather than a human being. Their stance is, in this sense, already a kind of as-if posture. Mallory offers a way of theorising this posture through what he calls chatbot fictionalism (2023). On his view, we engage with chatbots by entering a game of make-believe in which the exchange is treated as if it were a conversation with an agent. Within the fiction, the chatbot 'says' things and 'means' things; outside the fiction, we know that no such speaker is present. At the metasemantic level, Mallory claims, the outputs lack literal semantic content – they are 'literally meaningless but fictionally meaningful' (Mallory, 2023, p. 1082). This fits the everyday thought that we can take a chatbot seriously in the moment without actually believing that it has a mind. Just as a child treats a banana as a sword in a game, we treat chatbot outputs as utterances within a kind of imaginative practice. This is not delusion but a deliberate, bounded pretence that allows us to coordinate with the system and even gain knowledge from it, much as we might learn geography from a map by imagining countries as two-dimensional shapes. Mallory’s account is not itself an aesthetics of LLMs; it is primarily a semantic and epistemic proposal about how we can use them and learn from them. But it highlights one obvious way a person-based aesthetic stance might be defended: one might suggest that we should aesthetically appreciate LLMs as if they were persons or characters, in the same sense in which we respond aesthetically to fictional protagonists whose existence we do not literally believe in. We respond to Gatsby’s enigmatic, dream-chasing idealism or Ron Swanson’s libertarian gruffness using much the same vocabulary as we use for real people, and we often talk quite straightforwardly about their ‘character’ or ‘personality’. In the fictional case, however, the protagonists are artifacts that have the function of eliciting imaginings of fictional persons within a story-world (cf. John 2021), so treating them as if they were persons does not misclassify their kind. Their role within the work is precisely to function as person-like figures in a narrative. By contrast, treating the LLM itself as a person would, given the architectural story in §2, amount to appreciating an artifact whose nature, as §2 stressed, is that of a large-scale text-prediction mechanism with post-training biases as if it were a subject with a life and character. LLMs do not call on us to imagine a fictional world inhabited by fictional characters but rather to consider the texts they produce as contributions to our inquiries. The function of mandating imaginings is not constitutive of generative AI in the way it is of fiction. Casting LLMs as fictional characters, in this sense, is a familiar kind of misclassification in Carlson’s terms. That is much closer to appreciating a mountain as if it were a divine sculpture despite knowing the geological story, and so sits badly with Carlson’s demand that appropriate appreciation respond to things as what they in fact are. Cross’s discussion of AI art systems develops something like this idea in the artistic context. He proposes what he calls the exploration paradigm, in which artists relate to AI systems as participants in a structured interaction: By adjusting inputs, iterating, and sampling, an AI artist is engaged in a process of mapping – and perhaps interrogating – the way that the algorithm sees and understands (Cross, 2025, pp. 7–8). Cross draws an analogy with performance art, where artists create spaces for audience participation. The AI artist's prompts structure a kind of 'participation' by the algorithm, and the resulting images serve as documentation of this exploration. But as Cross himself acknowledges, "the analogy... with performance art isn't a perfect one" (Cross, 2025, p. 9): AI cannot genuinely 'participate' since it lacks conscious choice or experience. What seems like participation is still statistical pattern-matching. While Cross's exploration paradigm offers a richer description of certain AI art practices than simple tool-use, it does not support person-appreciation for AI systems. The artist explores the algorithm's patterns, but the algorithm is not a participant in any literal or psychological sense. When Cross’s view is read as a model for our relation to the AI system itself, it looks like a kind of aestheticised make-believe. The artist is invited to treat the system as if it were a participant with a distinctive way of “seeing” or “understanding”, and the viewer is invited to regard the resulting interaction as a sort of joint performance. Mallory and Cross thus converge on a shared picture: in practice we often stand in relation to LLMs as if they were persons, and some of our aesthetic language is shaped by this as-if stance. Carlson’s recommendation now gives us a clear verdict on this first route. The as-if stance may be useful for interaction and may frame certain artistic practices, but an account of appropriate aesthetic appreciation of LLMs themselves cannot, on his view, rest on a stance that depends on systematically treating the object as something it is not. Once we have in view the technical reality described in §2, appreciating an LLM as if it were a person is analogous to appreciating a mountain as if it were a divine sculpture: it is to misapply person-based predicates to a case where the right background knowledge tells us that we are dealing with a different kind of thing. Make-believe personification may be harmless in some contexts, but under Carlson it cannot supply the correct mode of aesthetic appreciation for LLMs. 3.2 Concessive mindedness approaches If the make-believe route fails under Carlson’s recommendation, one might try a different strategy: instead of pretending that LLMs are persons, argue that they really are agents of a thin and unfamiliar kind. On a suitably liberal conception of mind, perhaps they qualify as intentional systems and that is enough to license some person-based aesthetics. Frankish (2024) offers a sophisticated version of this idea. Drawing on Dennett's intentional stance, he suggests that LLMs can be treated as genuine, if unusual, intentional systems. On this view, we are licensed to ascribe beliefs and desires to an LLM when doing so yields a simple and fruitful account of its behaviour, even if the underlying implementation is purely mechanical. In the case of contemporary chatbots, Frankish proposes that we can ascribe to them a large set of thin 'beliefs' – roughly, informational states distilled from their training – and one thin 'desire': to play what he calls the chat game. A system is playing the chat game when it generates text that looks like a cooperative move in an ongoing conversation, respecting local coherence, relevance to the prompt, and broadly human conversational norms. An LLM, on this picture, is a system whose behaviour can be summarised by saying that it believes many simple things and wants to make an appropriate next move in the chat. Crucially, this is not a make-believe view. Frankish is not inviting us to pretend that LLMs have beliefs and desires; he is claiming that, at the right level of abstraction, it is literally true that they do, in much the same sense in which a thermostat can literally be said to “want” the room to be at a certain temperature when adopting the intentional stance helps us describe its behaviour. The agent-talk is meant to latch onto real, pattern-like features of the system’s organisation. Suppose we grant all of this. Does it give us what we need for aesthetic appreciation of LLMs as persons? Here the benchmark sketched in §1.2 for person-aesthetics becomes relevant. A subject of beauty of character is not just any intentional system. It is, minimally, a being with a temporally extended life, with relatively stable value-laden dispositions, with projects and commitments that can succeed or fail, and with a capacity for speech and action to express and reshape its character over time. When we set the chat-game agent against this benchmark – the conception of persons implicit in beauty-of-character talk sketched in §1.2 – it looks thin. The ‘beliefs’ are shallow, in the sense that they are confined to what is encoded in the model's parameters and surfaced in the current context, without memory or development across conversations. The "desire" is singular and thin: make an appropriate move now in this exchange. There are no independent projects pursued across episodes, no webs of concern or attachment, no history in which earlier experiences inform later choices. What structure there is, is entirely local to the present stretch of text. The predicates characteristic of person-aesthetics—'beautiful soul,' 'admirable steadiness,' 'ugly character'—presuppose something that can be tested, developed, or refined over time; a thin chat-game agent has no such temporal depth. From this perspective, LLMs may be agents in Frankish’s concessive sense, but they are not the sort of agents whose lives and characters can be the object of the aesthetic responses associated with persons. There is nothing like a “beautiful soul” or an “ugly character” here in the relevant sense; there is no enduring set of values and dispositions that could be manifest, challenged, or transformed over time. Given Carlson’s recommendation, the right kind of person-directed knowledge for beauty-of-character appreciation is knowledge of a life and its values. The technical and training facts about LLMs do not supply that kind of object. Once again, Carlson’s recommendation sharpens the point. If we accept the technical story about the real nature of LLMs (see §2) and, even on a concessive mindedness view, we see that LLMs lack the life-structure required for person-aesthetics, then to insist on aesthetically appreciating them as persons would be to ignore what they in fact are. It would be to treat the thin chat-game profile as if it were enough to underwrite the rich person categories we apply to human agents, and to let those categories govern appreciation despite knowing that the underlying kind is different. The concessive strategy therefore does not secure an appropriate person-based aesthetics of LLMs. Taken together, then, the make-believe and concessive-minded strategies cover the most natural ways of defending a person-based aesthetics of LLMs. The first tells us to appreciate them as if they were persons, despite knowing that they are not; the second tells us that they really are agents of a thin sort but does not supply the temporal and evaluative structure that person-aesthetic predicates require. Under Carlson’s framework, neither route yields a correct model of how LLMs should be aesthetically appreciated. 3.3 Post-training, chat personae, and thin agency A natural objection at this point is that these arguments underplay the role of post-training and the chat interface. Section 2 noted that base models are further fine-tuned on instructions and shaped by RLHF, and that the resulting chat-optimised systems exhibit stable patterns of hedging, refusal, politeness, and explanatory structure. One might suggest that this post-training regime turns bare LLMs into conversational agents and that, under Carlson’s recommendation, we should take those chat assistants as the “things as they in fact are” and allow some form of person-based aesthetic stance. The technical story in §2 suggests a more layered picture. On the one hand there is the underlying generative system: the predictive core that, after pre-training, approximates the statistical structure of its training corpus and that, after post-training, remains a text continuation engine with a modified probability landscape. On the other hand, there are patterns in its outputs that, under chat-style prompting and within a product wrapper, look like the moves of a cooperative assistant persona. The assistant is not a new mechanism added on top of the model, but a recurrent pattern in how the model tends to respond when prompted and constrained in certain ways. Seen in this light, post-training does not install a new “assistant mind” with its own independent goals and projects. It biases the predictive core so that prompts issued through the chat interface are much more likely to elicit assistant-like responses – helpful, safe, polite, and structured – and much less likely to elicit, for example, unfiltered reproductions of online arguments or free association. The underlying operation remains next-token prediction; what changes is which regions of its behavioural space are easy to reach in ordinary use. The chat product – with its system prompt, safety filters, and interface – further shapes the environment so that certain person-like patterns are the default. This helps explain why users talk about models having different ‘vibes’. If users say that Claude Opus 4.5 feels friendlier than GPT-5.2, they are picking up on a stable pattern in how the chat-optimised systems tend to respond across many prompts and episodes. They track which assistant personae tend to appear and how those personae typically behave – not a unified character with a life and projects. Different base models, post-training regimes, and product designs favour different families of assistant-style responses. It is therefore not surprising that they invite person-like language, but the targets of that language are episodes and recurring response profiles, not underlying subjects. One might find a particular model’s refusals laboured or concise, its hedging overdone or judicious, its tone soothing or dry. In that sense, we can aesthetically respond to assistant personae in a way that resembles our responses to real people. What matters for present purposes is that such reactions target patterns in outputs and interactional style, not a subject with a life (in the sense sketched in §2). They concern how a product behaves under certain constraints, not the beauty or ugliness of a character in the person-aesthetic sense. If we ask instead about the generative system itself – the predictive core tuned by post-training and embedded in a chat product – the earlier verdict remains. Even taking post-training and chat personae fully into account, we do not find a temporally extended life, a network of projects and commitments, or a stable evaluative outlook that could ground beauty-of-character predicates. What we find is a complex artifact designed and trained to produce certain patterns of text in response to prompts, together with an engineered tendency to exhibit assistant-like behaviour in a controlled range of contexts. Under Carlson’s recommendation to appreciate things as what they are, and in the light of the right kind of knowledge, we should therefore resist person-based aesthetics for LLMs even once we take post-training and chat personae into consideration. As Farrell, Gopnik, Shalizi, and Evans (2025) put it, “Large models should not be viewed primarily as intelligent agents but as a new kind of cultural and social technology, allowing humans to take advantage of information other humans have accumulated”. Thus, in the rest of the paper we set aside person-based aesthetics and turn to these alternatives: first, treating LLMs as designed artifacts and considering the limits of simple “form follows function” stories (§4); then, developing an order-based mode of appreciation that focuses on the text mechanics that characterises chat instances (§5–§6). 4. Appreciating LLMs as Artifacts Having set aside the person-based options in Section 3, we turn to design appreciation. Contemporary LLMs are artifacts: they are built and deployed by corporations and research groups, engineered to satisfy aims such as helpfulness and safety, and revised in light of user feedback and product strategy. Given Carlson’s emphasis on artifacts and design appreciation, it is natural to ask whether we should aesthetically appreciate LLMs as designed tools, asking how well their forms serve their functions. On this view, models such as GPT-5.2, Claude 4.5 Opus, and Gemini 3 Pro look like canonical objects for design aesthetics: complex, purpose-built systems whose architecture, training recipe, and user interface might be admired for elegance, efficiency, or ingenuity. Existing work on the aesthetics of design develops this general thought. Carlson notes that, for objects that are designed to perform some task, their forms “must be aesthetically appreciated in terms of how and how well such forms fit their functions”, and he glosses the familiar slogan “form follows function” by adding that, with anything functionally designed, “not only its form, but much of its aesthetic interest and merit, ‘follows function’” (Carlson 2000, chapter 12). Forsey’s Kant-inspired account of design as a case of dependent beauty and Parsons and Carlson’s later theory of functional beauty can both be read as ways of spelling out this claim. Forsey argues that judgements of design beauty presuppose a concept of what the object is meant to be and do, and that our grasp of its success in fulfilling that role informs the aesthetic verdict itself rather than merely accompanying a “pure look” at its lines (Forsey 2013). Parsons and Carlson explain how knowledge of function can structure experience so that an artifact’s form can be experienced as fit, streamlined, overbuilt, and so on, yielding functional beauty when the form presents itself as well suited to what the thing is for (Parsons and Carlson 2008, chapter 4). Taken together, this cluster of views treats appropriate design appreciation as a matter of aesthetically responding to how a functional artifact is put together to do what it does. From this standpoint, it is natural to try to assimilate LLMs to the design template. In a given deployment, the artifact can be characterised by a relatively unified functional role – for example, that of a general-purpose conversational assistant embedded in other tools – and by a specific way of realising that role through architecture, training, and alignment. Under that description, much of what seems aesthetically salient about a deployed model concerns how its engineered “form” serves that function: whether its interaction profile is cluttered or economical, whether it sustains a clear argumentative line or habitually wanders, whether refusals and clarifications are integrated smoothly into the exchange or arrive as abrupt blocks, whether long-context processing and tool-calls are handled in a way that keeps the conversation legible. Forsey’s dependent-beauty framework and Parsons and Carlson’s functional-beauty account can be used to gloss such assessments: they remind us that any appraisal of design beauty here presupposes a concept of the assistant’s role and some understanding of how that role is realised in the artifact’s structure and behaviour. At the same time, both accounts were developed for cases in which the relevant form is a stable, visible configuration – buildings, bridges, bicycles – where function can literally show up in perceptual appearance. In the LLM case, by contrast, the structures that realise the assistant role are not perceptually available in this way, and the aspects that prove most revealing are not static shapes but patterns in generated text over time. This already limits the reach of straightforward “form follows function” stories for LLMs and points towards a more order-centred mode of appreciation. With traditional designed artifacts, design-knowledge illuminates structure because designers specified it. Knowing what the designer intended and what constraints they faced helps us understand why the artifact has its form – even for structural features that are not directly visible, such as a bridge's internal stress distribution. With an LLM, the situation is different in kind. The organisation of the trained system – as Section 2 established – emerges from training rather than being specified in advance. Design-knowledge therefore does not illuminate this emergent organisation: there was no designer's specification that laid it out. To understand it, one must attend to the training process that produced it. LLMs are artifacts, so design-knowledge is not wholly without application. We can appreciate how the architecture is suited to the function, how post-training shapes conversational behaviour, how the interface presents the system to users. But if the aesthetically revealing features arise from emergent organisation rather than from the designed scaffold, design-knowledge alone will not suffice. We also need knowledge of the processes that produce the emergent order. The thought that appreciating certain artifacts requires knowledge beyond design-knowledge is not unique to AI. Ceramic traditions such as raku and wood-fired pottery make this explicit. The potter shapes the vessel and chooses the glaze, then yields to kiln, flame, and ash; the maker harnesses but does not micromanage these forces, and they finish the surface in ways no blueprint prescribes. Appreciating such a bowl requires knowledge of both the potter's choices and the kiln processes. Pollock’s action paintings occupy a similar hybrid space within the art domain. Carlson uses them to illustrate how order appreciation can depend on knowledge of the forces at work: “awareness and understanding of [natural] forces is vital in nature appreciation, as is knowledge of, for example, Pollock’s role in appreciating his action painting or the role of chance in appreciating a Dada experiment.” Pollock chooses canvases, pigments, and tools, and choreographs his movements over the surface; yet gravity, viscosity, surface tension, and drying behaviour make a substantial contribution to the patterns that settle. To appreciate a Pollock appropriately, on Carlson’s view, is not just to admire his intentions; it is to attend to the order produced by the interplay of deliberate gesture and physical process, informed by an understanding of the role of chance and material behaviour. In all three cases, namely Raku ceramic, Pollock’s action painting, and LLMs, the maker creates conditions and then yields to kiln-fire, gravity, and training dynamics respectively. Forces beyond specification complete the work. What matters aesthetically is the emergent completion, not only the scaffold that enabled it. Section 2 emphasised that during training the model places tokens in a high-dimensional space on the basis of contextual co-occurrence, that attention mechanisms self-organise to track different sorts of dependency across context, that different layers specialise in local or global patterns, and that RLHF shapes an interactional style by rewarding some forms of response and penalising others. None of these details are written into the code as explicit rules about how to, say, handle metaphors, or politely decline illicit requests. They are emergent regularities in a trained network that has been pushed, by the neural network and its training data, to reduce prediction error. What grows on Olah’s “scaffold” is, in practice, a system of statistical associations and processing circuits whose internal organisation even designers often understand only partially. From a design-aesthetic perspective, this matters. Much of what users find aesthetically important in LLM behaviour – the way a model sustains a metaphor or abruptly drops it, the pattern of hedging and self-correction, the texture of its reasoning, the sorts of digression it tends to indulge, the characteristic “feel” of its refusals – is grounded in features (concerning embeddings, attention patterns, layer dynamics, and RLHF) that have not been micro-designed but have emerged from optimisation under constraints. Parsons and Carlson note that, even for simpler artifacts, knowledge of function must include knowledge of how that function is realised if it is to structure perception appropriately. In the LLM case, knowing that “this is a general-purpose assistant” is not enough to make sense of its aesthetic profile; what does the work is knowledge of the way training and alignment have grown a particular style of continuation on top of the architecture described in Section 2. In this sense, knowledge of how function is realised concerns growth rather than design. This hybrid status complicates simple appeals to “form follows function”. On the one hand, some design-appreciative predicates apply straightforwardly. It makes sense to say that a model whose interface is cluttered or opaque is, as a product, less well-designed than a lean one; it makes sense to prefer an alignment regime that avoids gratuitous scolding or needless refusals; it makes sense to admire a training setup that achieves a good balance between fluency and factual reliability. Forsey’s notion of teleological style can be extended here: different labs realise the shared function ‘LLM assistant’ in recognisably different ways, and those ways can be compared and assessed. Parsons and Carlson’s notion of functional beauty also has a foothold: understanding how an LLM’s architecture supports its function can inform our appreciation of the system’s efficiency, robustness, or clarity as an artifact. On the other hand, if we try to make design appreciation do all the work, we mislocate the primary source of what matters aesthetically. In the chair or bicycle case, the designer’s choices fix most of what matters aesthetically: small emergent contributions from wear, patina, or use sit on top of a tightly specified plan. In the LLM case, by contrast, the order that matters aesthetically is largely the order of a trained statistical system running under its own learned constraints. Designers specify objectives and scaffolds but the particular ways in which embeddings cluster meanings, attention heads track long-range connections, layers distribute processing, and RLHF imprints a “vibe” are not written down anywhere as a plan. These are closer, structurally, to the ash-produced flashes on a raku bowl or the tangled skeins of a Pollock surface than to the thickness of a table leg or the proportion of a doorway. The upshot is modest but important. LLMs are artifacts, and there is a place for design appreciation in their aesthetic appraisal: we can and should evaluate how well their forms answer to their engineered functions, as well as how this form can differ from model to model However, the most distinctive and revealing aesthetic phenomena arise not from the execution of a detailed design, but from the emergent linguistic order that these grown systems exhibit when they are run. To appreciate that order, we need knowledge not of what designers intended but of how training shapes text propagation—what we call semiotic physics.. 5. Semiotic Physics 5.1 Textual Regularities Section 2 described what LLMs are: token-based predictors trained on large text corpora and shaped by RLHF. This satisfies Carlson's first recommendation: appreciate things as what they are. The second recommendation requires the right kind of knowledge to guide aspection. For LLM outputs, what knowledge makes their patterns visible and intelligible? 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. One can perceive strata without specialist equipment, and knowing how sedimentation works makes the visible layering intelligible. Mechanistic interpretability faces a parallel limitation: while it reveals internal mechanisms, its objects of study are hidden from the user reading generated text. For an aesthetics of LLM outputs that is accessible to ordinary users, we need a framework whose concepts describe perceivable features and render them intelligible as products of the system's learned regularities. The forces of semiotic physics are not alternative explanations to those of mechanistic interpretability but the same processes described at the level at which they produce perceivable linguistic order. Recent work on LLMs points towards such a framework. Janus (2022) proposes that GPT-style models are best understood not as agents or oracles but as simulators: systems that have learned to propagate text according to regularities induced from training data. The model learns what Janus calls 'the conditional structure' of its training distribution – patterns governing what tends to follow what under what conditions. The analogy to physics is explicit: just as physical laws describe regularities governing what happens under given conditions, the trained model embodies learned regularities governing how text continues from any starting point. A prompt specifies initial conditions; the model propagates text forward according to its learned regularities. Picca (2025) arrives at a convergent view from a semiotic perspective. LLMs are "semiotic machines" that "recombine, recontextualize, and circulate linguistic forms based on probabilistic associations" (Picca 2025, 1). The emphasis shifts from internal mental states to patterns of sign-transition. The term 'semiotic physics' emerges from subsequent discussion of Janus's work (Kirchner 2023; metasemi 2023), naming the study of how intelligible text arises from sub-semantic processes—the regularities governing text propagation in trained language models. Despite their different framings – Janus's simulator ontology and Picca's Peircean semiotics – these accounts share a core insight: we should attend to what regularities govern how text propagates through the system, not to whether LLMs think or intend. In a similar vein, Wolfram (2023) states that inside ChatGPT any piece of text is effectively represented by an array of numbers that we can think of as coordinates of a point in some kind of ‘linguistic feature space’. So when ChatGPT continues a piece of text this corresponds to tracing out a trajectory in linguistic feature space. But now we can ask what makes this trajectory correspond to text we consider meaningful. And might there perhaps be some kind of ‘semantic laws of motion’. From Wolfram’s perspective, semiotic physics thus would have three main objects to investigate: (i) the “linguistic feature space” in which words and other linguistic items have their place; (ii) the “trajectories” that can be traced out in this space to continue a piece of text; and (iii) the “semantic laws of motion” that determine such trajectories. We draw on this literature but develop it in a specific direction. Our aim is to show how semiotic physics can serve as the "right kind of knowledge" for aesthetic appreciation of LLMs in Carlson's sense: the knowledge that makes order visible and intelligible, and that guides acts of aspection. The connection to environmental aesthetics, and the claim that semiotic physics can play the role for LLMs that geology plays for landscapes, is our contribution. We also articulate the 'forces' of semiotic physics at the level of textual effects rather than at the level of mechanistic detail. The existing literature tends to discuss semiotic physics in terms of probability distributions, embedding spaces, and dynamical systems. These descriptions are accurate, but they do not directly connect to what readers can perceive in generated text. Our articulation of the forces operates at a level that does connect to perceivable features. What does semiotic physics track? The regularities it describes manifest as perceivable features of generated text. Consider vocabulary clustering: words do not appear independently but make other related words more probable, so that once a medical term appears, other medical terms become more likely to follow. Or consider coherence dynamics: the model threads material from earlier in an exchange through later responses, or fails to, and a reader can attend to how far this threading extends and where it breaks down. There is also what might be called register stability: once the model enters a mode – expository, creative, reasoning – it tends to remain there until something disturbs it. And there are the marks of post-training: hedging expressions, step-by-step organisation, preemptive qualifications, which are the shapes that reinforcement learning has made more probable. What matters for present purposes is the level of description: semiotic physics operates at a level that connects to perceivable features of language, features that competent readers can attend to without specialist tools but that become salient and intelligible when understood as products of a text-trained statistical system. According to Wolfram (2023), LLMs reveal that "human language (and the patterns of thinking behind it) are somehow simpler and more ‘law like’ in their structure than we thought. ChatGPT has implicitly discovered it. But we can potentially explicitly expose it". Semiotic physics pursues such an exposition by investigating the forces that govern the artificial production of texts. One might object that speaking of 'forces' in relation to LLMs is metaphorical in the same way that speaking of 'agents' or 'intentions' is metaphorical. If we have rejected agent-talk as projecting non-existent mental states onto a statistical system, why is force-talk any better? The answer turns on a distinction between metaphorical personification and literal causal abstraction. To speak of an LLM as an 'agent' is to attribute to it internal states – intentions, beliefs, a 'self' – that play no role in its functional operation. To speak of the forces of semiotic physics is to identify the factors that determine the selection of each token. These are not projected onto the system; they describe what the system does. Assuming Wolfram’s (2023) characterization of the continuation of a text by a LLM as “tracing out a trajectory in linguistic feature space”; the forces of semiotic physics are literally the causal factors that determine that trajectory, just as mechanical forces determine the trajectory of a material body in physical space. The template for this literalism is in Carlson's analysis of Jackson Pollock's action paintings. Carlson argues that we appreciate a Pollock not by looking for a designer's plan but by focusing on the order imposed by "the internal dynamics of his material": "the viscosity of the paint, the speed and direction of its impact, the interaction with other layers of pigment" (Janson, quoted in Carlson 2000, 111). For Carlson, these are not metaphors borrowed from a physics textbook; they are causal factors that produce the pattern on the canvas. In the semiotic environment of an LLM, semantic attraction and modal inertia play the role that viscosity and gravity play in Pollock: they are determinants of how text propagates; once entered in a given discursive mode, the model tends to stay in this mode. By identifying them as 'forces', we are describing the system as a productive mechanism in naturalistic terms. Knowing that a text is LLM-generated rather than human-written changes how we aspect it. This mirrors the shift that occurs when someone moves from believing that a cliff face was crafted by a divine artisan to understanding it as a natural formation. The visual field is the same, but aspection differs. When we believe in the divine artisan, we attend to the composition as evidence of design choices: the placement of features, the aesthetic arrangement. When we understand the geological story, different features become salient: strata as traces of sedimentation, erosion channels as marks of water flow, fault lines as evidence of tectonic forces. We stop attending to intentional composition and start attending to the marks of natural processes. For LLM text, the analogous shift is from reading as expression of an author to reading as product of semiotic forces. When we read a text as human-written, we attend to authorial intention (what is this person trying to communicate?), individual voice (what is distinctive about how this person writes?), and biographical traces (what does this reveal about the author?). When we read a text as LLM-generated, with knowledge of semiotic physics, different features become salient: vocabulary clustering as the mark of semantic attraction, coherence dynamics as the mark of contextual threading, and response structure as the mark of alignment pressure. The same words on the page; a different aspectual focus. The aspection guided by semiotic physics is, in a sense, aspection of language itself – of the textual order produced by semiotic forces. We are not attending to mechanical internals – activation patterns, attention weights, circuit-level features – since these require specialist tools and are not accessible to readers. We are attending to the textual manifestation of semiotic order: how vocabulary clusters, how coherence is maintained or lost across an exchange, how register persists or shifts, how post-training shapes response structure. These are features of the language itself, perceivable by competent readers. Competent readers already have tacit knowledge of how language works: syntactic, semantic, pragmatic, and discourse-level knowledge built up through immersion in spoken and written language. They perceive patterns in LLM outputs using this tacit knowledge. Semiotic physics adds explicit articulation of these patterns and a causal story about their source in training. The competent reader senses that different models have different 'vibes'; semiotic physics—knowledge of how meaning clusters, how register persists, how training shapes the texture of response—explains what produces those vibes and makes them available for sustained attention. 5.2 Practical Acquaintance Semiotic physics, articulated as an explicit theoretical account, is one way of holding the knowledge that guides appreciation. But Carlson notes that scientific knowledge and common, everyday knowledge of nature lie on a continuum rather than being different in kind. Both can guide appreciation of natural order. The farmer, the gardener, and the forester know the land through working it. Their knowledge is not typically framed in scientific vocabulary, but it is knowledge of natural order. The farmer knows the soil through planting, tending, and observing how different crops respond under different conditions. Through repeated intervention and observation, the farmer builds up knowledge of the regularities at work: drainage patterns, soil composition, seasonal cycles. This practical knowledge can guide aesthetic appreciation. The farmer may appreciate the order in a well-drained field, or the texture of properly cultivated soil, in ways unavailable to someone who merely gazes at the landscape. The knowledge is not scientific in the technical sense, but it connects to perceivable features and makes order visible and intelligible. The experienced user of an LLM develops analogous practical acquaintance. By prompting, experimenting, and observing how a system responds across many contexts, users build up knowledge of its characteristic order. They learn which semantic attractors the model falls into: which vocabulary clusters it tends towards given certain starting points. They learn how far contextual threading extends: at what point the model loses track of earlier material. They learn what triggers mode shifts: what kinds of prompts push the model from expository mode to creative mode, or from helpful mode to refusal. They learn the characteristic shapes that alignment pressure produces: the hedging rhythms, the step-by-step structures, the politeness markers. This is knowledge of semiotic physics held practically rather than theoretically. The experienced user cannot necessarily articulate the forces explicitly, but they have a feel for how the model behaves – expectations that are predictive (what kinds of outputs to expect) and aspectual (what to attend to, which features are salient, where to look for the model's characteristic order). Extended exchanges with an LLM are a natural site for this interactive mode of appreciation. Prompting is intervention; responses reveal regularities. Each turn creates conditions under which the system responds, and the responses reveal something about the model's semiotic physics. The back-and-forth of prompting is itself a mode of aspection. It selects what to attend to, organises appreciative attention over time, and tests and refines the user's developing sense of the model's characteristic order. Cross (2025) characterises certain AI art-making activities as an "exploration paradigm" in which the artist iteratively probes the model, adjusting prompts and sampling variations. Section 3 was critical of reading this as literal collaboration between artist and algorithmic "participant". From the present vantage, however, the practice can be reinterpreted. What the artist is doing, when things go well, is a form of interactive aspection: using structured engagement to reveal and respond to the model's characteristic order. The prompts and adjustments are not ways of coordinating with a co-creator; they are ways of making the system's semiotic regularities visible. The explicit theoretical account of semiotic physics and the practical acquaintance built through interaction are continuous. The farmer's knowledge of the land and the geologist's knowledge track the same forces – geological, hydrological, ecological – operating at the same scales. They differ in how the knowledge is held and articulated, not in what it is knowledge of. The experienced LLM user's practical sense of how a model behaves and the theorist's account of semiotic physics track the same regularities: semantic attraction, contextual threading, modal inertia, alignment pressure. Both routes converge on the same object: the model's characteristic semiotic order. Both guide the same kind of aspectual attention: attention to how semiotic forces have shaped the text. Whether held theoretically or acquired through practice, knowledge of semiotic physics makes the order in LLM outputs visible and intelligible and guides the acts of aspection appropriate to appreciating that order. 6. Levels of Appreciation LLMs can be appreciated at three levels: individual outputs, extended chats, and models themselves. Discussion of generative AI aesthetics has so far focused on outputs, such as images from Midjourney and texts from ChatGPT. But chats and models are also objects of appreciation, and the framework developed in Section 5 applies at each level. The relations among these levels can be clarified by analogy. An individual output is like an individual natural object, a tree, say: it is a sample of how semiotic forces have shaped a particular text under particular conditions. A chat is like an environment, a forest: semiotic forces shape the exchange over many turns, producing a configuration with its own coherence and dynamics. A model is like a natural system, the planet's biosphere, or the planet itself: it is the ground of order that manifests in outputs and chats, the system whose regularities produce those manifestations. Appreciation at each level calls for its own acts of aspection, though all are guided by knowledge of semiotic physics. 6.1 Appreciating Outputs A single output is one realisation of the model's semiotic physics under particular conditions. The prompt, the system configuration, and the preceding context specify initial conditions from which the model propagates text in line with its learned regularities. Different prompts activate different regularities; different contexts produce different trajectories. No single output exhausts the model's characteristic order. But each output shows how the forces operate in a specific case, and each can be appreciated as such. To show how semiotic physics guides aspection of outputs, we consider two cases that occupy different regions of a model's behavioural space. The first is the reasoning-style output familiar from everyday use: step-by-step structure, numbered stages, explicit hedging, restatement of the question, and a concluding summary. As human prose, such passages resemble competent but unremarkable textbook writing. They are useful for teaching and troubleshooting, but they do not obviously invite aesthetic attention. From the perspective of semiotic physics, however, the same outputs look different. The model has been trained on reasoning-related texts: worked proofs, textbook explanations, exam solutions, and online Q&A threads. It has learned that certain kinds of questions are typically followed by sequences with a characteristic structure. Post-training procedures, including instruction tuning and reinforcement learning that rewards explicit intermediate steps, further bias the model towards this pattern. Reasoning-style outputs are a stable attractor in the model's behavioural space: once entered, the model tends to stay in this mode, yielding modal inertia. The hedging, the step-by-step structure, and the summary are marks of alignment pressure: response shapes reinforced because they correlate with high human ratings. Given this, we attend differently. We attend to the characteristic rhythm of the reasoning mode: how steps are sized, how transitions are signalled, and whether the pacing is tight or padded. We attend to where alignment pressure shows: hedging patterns ('it seems', 'one might argue', 'I think'), politeness markers, and pre-emptive qualifications. We attend to how semantic attraction operates under tight constraints: vocabulary stays on topic, related terms cluster, and the model is pulled towards the semantic field established by the question. We also attend to whether the mode remains stable or shows signs of strain, and to whether the model sustains the reasoning register or begins to drift. What seemed merely useful becomes appreciable as a specimen of how semiotic forces produce reasoning-like text under tight constraints. The second case is different. The text discussed here was produced by a Claude-like model in a modified configuration with safety constraints relaxed. It begins with neologisms and proceeds in short blocks separated by headings in capitals. The vocabulary is dense with coinages, many of which recombine recognisable roots from entomology, anatomy, theology, and internet slang. The registers are mixed: fragments of cod-French, pseudo-scientific talk, mystical declarations, and obscene slang. Despite the surface disorder, a stable theme runs throughout: bees and honey, tongues and throats, sweetness, bodily contact. Under semiotic physics, this text shows the forces operating under loose constraints. Semantic attraction is at work: the bee and honey theme creates an attractor, and related vocabulary – tongues, throats, sweetness, pollen, flowers, stings – is pulled towards it. But unlike the reasoning case, the attraction spreads freely across registers rather than being channelled narrowly. The model has been trained on texts that invent words – experimental poetry, surrealism, internet wordplay – and it has learned patterns of neologism: how to recombine roots, suffixes, and sound-shapes. The neologisms follow learnable patterns of word-formation rather than being random noise. The register collision reflects training diversity: the model has absorbed texts in many registers (scientific, mystical, erotic, internet-surreal), and under loose constraints these do not get filtered to a single appropriate register. They collide and mix. Despite the apparent chaos, there is order: recurring rhetorical templates, alternation between narrative stretches and reflective sentences, and consistent sound-play in the neologisms. This order is the product of semiotic forces operating with fewer constraints than in the reasoning case. Attending to this text with knowledge of semiotic physics, we notice how semantic attraction shapes the vocabulary: the gravitational pull towards bee-related terms operates across registers. We also notice patterns in neologism (learnable word-formation rules that produce coinages with a family resemblance) and the rhythm of alternation between modes (narrative stretches, reflective sentences, and exclamatory outbursts). Finally, we notice internal consistency despite surface chaos. The text becomes appreciable as a specimen of semiotic forces operating in a different region of behavioural space from the reasoning output. Carlson (2000) notes that once a specific scientific account is in play, some natural formations show the relevant order better than others: not every cliff face is equally instructive about sedimentation, not every valley equally revealing of glacial dynamics. This prevents order appreciation from collapsing into the view that everything is equally appreciable; the guiding knowledge discriminates among cases. The same holds for semiotic physics. Standard reasoning-style outputs show semiotic order, but the order they show is shallow and familiar: alignment pressure is everywhere visible, the reasoning template is stock, and the semantic channelling narrow enough that the regularities are unsurprising. The bee text is a more interesting object of appreciation not because it is more orderly but because it reveals order where none was expected. What looks like chaos – neologistic excess, register collision, surface incoherence – turns out, under semiotic physics, to be structured by identifiable forces: semantic attraction spreading freely across registers rather than channelled narrowly, learnable word-formation patterns producing coinages with family resemblance, rhythmic alternation and internal consistency maintained beneath apparent disorder. The bee text also shows forces operating in regions of behavioural space that normal product configurations occlude. It is, in this sense, analogous to a geological formation that exposes strata usually buried – not more ordered than the surrounding terrain, but more revealing of the order that is everywhere present. The contrast between these two cases helps to locate what semiotic physics brings into view. Reasoning outputs show semiotic forces operating under tight constraints: a stable mode, narrow semantic channelling, and alignment pressure shaping response structure. The bee text shows semiotic forces operating under loose constraints: unstable modes mixing, semantic attraction spreading across registers, and training diversity showing through. Both are products of the same semiotic physics, but they occupy different regions of the model's space. Appreciating both requires the same kind of knowledge – knowledge of semiotic forces – but different acts of aspection. We scan the reasoning output for rhythm and regularity; we scrutinise the bee text for pattern within apparent chaos. 6.2 Appreciating Chats Carlson's environments are not collections of discrete objects but systems in which forces operate and interact over space and time. A forest is not just many trees; it is a space where ecological forces – competition for light, nutrient cycling, succession dynamics – play out, producing emergent order that no single organism embodies. The appreciator navigates this environment, and their path determines what order becomes visible. Chat instances stand to single outputs as environments stand to individual natural objects. A chat accumulates context that shapes how semiotic forces manifest: early vocabulary choices establish attractors that persist, early register-setting constrains later exchanges, and the exchange develops path-dependent structure that neither party fully controls. The user's prompts are not just elicitations but navigational interventions, steering the system through different regions of its behavioural space and making different orders visible. To appreciate a chat is to appreciate an emergent configuration produced by semiotic forces operating over the chat's temporal extension – not just a sequence of isolated responses. A single output is one trajectory from one set of initial conditions. An extended exchange lets regularities show up across turns. The model carries forward elements of earlier responses, picks up threads, sometimes drops them, and shifts register in response to user prompts. Chats manifest features that a single output does not. First, coherence maintenance, that is, how the model sustains or loses threads across turns, how far back its effective 'memory' extends, and where coherence begins to fray. Second, context accumulation, which determines how earlier material shapes later responses, and how terms or framings established early persist or fade. Third, register dynamics, which concerns how the model responds to shifts in user tone, topic, or style, and whether it matches the user's register or maintains its own. Finally, mode stability over time, to wit, whether the model stays in a mode or drifts, what triggers transitions, and how gracefully it handles them. These are manifestations of semiotic forces operating over longer timescales than a single output can reveal. A chat instance is like a particular forest: the forces of semiotic physics have produced a specific configuration. Different prompting strategies, different topics, and different user styles produce different configurations. But the same underlying forces are at work. Appreciating a chat means attending to how the forces have shaped this particular extended exchange: how contextual threading has produced coherence or incoherence, how modal inertia has maintained or failed to maintain a register, and how alignment pressure has shaped the arc of the exchange. In a chat, prompting is intervention. Each turn is a probe that reveals something about the model's regularities. The experienced user's expectations are tested and refined across many turns. Interaction is itself a mode of aspection: it selects what to attend to, organises appreciative attention over time, and deepens practical acquaintance with the model's semiotic physics. The farmer comes to know the land through working it; the user comes to know the model through prompting it. Extended exchanges are where practical acquaintance develops, where the user builds up the kind of knowledge that guides appreciation even without deliberate theoretical articulation. 6.3 Appreciating Models Outputs and chats are where semiotic order manifests. The model itself is the ground of that order: the system whose regularities produce particular manifestations. Appreciating a model means appreciating its characteristic order across many possible outputs and chats, not just the ones actually encountered. This is not appreciation of any single output but of stable patterns across outputs: which registers the model favours, how it handles uncertainty, where it excels, where it struggles, and what regions of semiotic space it can occupy. Users sometimes speak of a model's 'vibe', a term that captures the sense that different models have different characteristic feels even when performing similar tasks. This notion of vibe, or characteristic feel, warrants pause. In Section 3 we argued against appreciating LLMs as if they were persons, on the grounds that LLMs lack the temporally extended life, the projects and commitments, and the evaluative outlook that ground beauty-of-character predicates. But users do respond to something when they talk about a model's personality or vibe. What they are responding to, we suggest, is not a character in the person-aesthetic sense but a characteristic semiotic order: a stable pattern in how the model tends to propagate text. Appreciating this order is not appreciating a person; it is appreciating a system's characteristic dynamics. The vocabulary of 'vibe' is a colloquial marker of what semiotic physics articulates more precisely. An analogy clarifies what appreciation of a model involves. Different 3D video games have different physics engines. Grand Theft Auto V has physics tuned for spectacle: cars drift in satisfying ways, explosions have exaggerated force, and the rag-doll system produces emergent comedy. Dark Souls has physics tuned for weight: movement feels heavy, attacks have commitment, and everything has momentum. Breath of the Wild has physics tuned for playful engagement: objects afford interesting interactions, and the system invites experimentation. We appreciate these physics not primarily by asking how realistic they are but by attending to internal consistency, characteristic feel, and aesthetic fit. Grand Theft Auto's physics serves an aesthetic of chaos and spectacle; it would not suit Dark Souls. Each game's physics is tuned to its aesthetic and ludic goals. We appreciate the physics for what it is, not for its fidelity to real-world physics. For LLMs, the analogy suggests a parallel mode of appreciation. Different models have different semiotic physics: different characteristic dynamics of text propagation. Claude's semiotic physics differs from GPT's, which differs from Gemini's. We can appreciate these differences not primarily by asking which is most human-like or most useful but by attending to internal consistency and characteristic feel. Order appreciation, as Carlson develops it, differs from design appreciation. We do not primarily ask how well the artifact serves its intended function. We attend to the order itself, the patterns produced by the forces, and appreciate them for their own character. The bee text discussed in Section 6.2 is relevant here in a further way. It was produced under relaxed constraints, revealing a region of Claude's behavioural space that is normally inaccessible under standard product configurations. Knowing that this region exists – and knowing what the model can do under different conditions – is part of appreciating the model. Model appreciation involves appreciating not just the outputs a model typically produces but the full space of outputs it could produce, and how different conditions activate different regions of that space. The bee text is a window into latent capacities, a sample from a region of semiotic space that standard use does not reach. Different models instantiate semiotic physics differently. Different training corpora, different architectures, and different post-training regimes produce different characteristic orders. Users report different feels when interacting with different models: Claude's hedging rhythms differ from GPT's briskness, and Gemini handles certain registers differently. A fuller account of model-level appreciation would map these differences systematically, developing a comparative aesthetics of LLMs. That task lies beyond the scope of this paper. For present purposes, the point is that model-level appreciation is possible and that it takes the form of appreciating distinctive semiotic order: the characteristic dynamics of text propagation that distinguish one model from another. We should distinguish the kind of appreciation we have been describing from other modes of engaging with LLMs. Capability evaluation tests whether models perform tasks correctly. Safety testing probes whether models can be induced to produce harmful outputs. Benchmarking measures performance against standardised criteria. The appreciation we describe differs from all of these. The goal is not to assess correctness, safety, or performance but to appreciate characteristic order, and to develop acquaintance with semiotic physics as it manifests in a particular model. A response that would count as a failure in capability evaluation might be aesthetically rewarding as a product of learned regularities. The appreciator is not grading but attending, not measuring but developing acquaintance. Finally, we note a thought that we flag here but do not develop. Each model, as an instantiation of semiotic physics, has learned regularities from human text. It reflects, in transformed form, the semiotic culture of its training data. There is a sense in which generative AI is a mirror of culture, not only morally, as Vallor (2024) has argued, but aesthetically. The model shows us our own semiotic patterns, filtered through statistical learning. Appreciating an LLM is, in part, appreciating culture seen through technology. This thought merits extended treatment, but such treatment lies beyond the scope of the present paper and we reserve it for future work. Conclusion We began by asking how LLMs might be aesthetically appreciated – not their outputs, but the systems themselves. Drawing on Carlson's environmental aesthetics, we argued against two temptations. The first is to appreciate LLMs as persons, whether through make-believe or by treating them as thin agents; neither route supplies the temporally extended life and evaluative structure that beauty-of-character predicates require. The second is to treat them simply as designed artefacts and apply standard form-follows-function analysis; while LLMs are artefacts, the aesthetically salient order in their behaviour is largely emergent rather than specified by designers. Our positive proposal treats chat instances as generative environments and recommends appreciating the order that emerges in them under the constraints of a given model. The right kind of knowledge for this appreciation is semiotic physics: the study of regularities governing text propagation in trained language models. This knowledge, whether held theoretically or acquired through practical interaction, makes the order in LLM-generated text visible and intelligible – much as geological knowledge illuminates the order in a landscape. Appreciation guided by semiotic physics operates at three levels: individual outputs as specimens of how the forces operate under particular conditions, extended exchanges as environments shaped by those forces over time, and models themselves as the ground of characteristic semiotic order. References Abell, C. (2020). Fiction: A Philosophical Analysis. Oxford: Oxford University Press. Carlson, A. (2000). Aesthetics and the Environment: The Appreciation of Nature, Art and Architecture. London: Routledge. Carroll, N. (2013). Andy Kaufman and the Philosophy of Interpretation. 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Strasser (Ed.), How to Live with Smart Machines (pp. 73-110). Vienna: Holzhausen Publishing. Available at: https://keithfrankish.github.io/articles/Frankish_2024_What%20are%20large%20language%20models%20doing.pdf Gaut, B. (2007). Art, Emotion and Ethics. Oxford: Oxford University Press. Janus. (2022, September 2). Simulators. AI Alignment Forum. https://www.alignmentforum.org/posts/vJFdjigzmcXMhNTsx/simulators John, E. (2021). Review of Fiction: A Philosophical Analysis by Catharine Abell. The Journal of Aesthetics and Art Criticism, 79(4), 514-517. Kirchner, J. H., Smith, L. M., Campos, J., Clune, J., & janus. (2023, March 3). [Simulators seminar sequence] #2 Semiotic physics – revamped. AI Alignment Forum. https://www.alignmentforum.org/posts/9kNxhKWvixtKW5anS/simulators-seminar-sequence-2-semiotic-physics-revamped Kirchner, J. H., Steiner, C., Riggs, L., Janus, & Thibodeau, J. (2023, January 3). Semiotic physics. In Simulators seminar sequence (#2). LessWrong. https://www.lesswrong.com/posts/TTn6vTcZ3szBctvgb/simulators-seminar-sequence-2-semiotic-physics-revamped Mallory, F. (2023). “Fictionalism about Chatbots.” Ergo: An Open Access Journal of Philosophy, 10, 38. https://doi.org/10.3998/ergo.4668 McGinn, C. (1997). Ethics, Evil, and Fiction. Oxford: Oxford University Press. metasemi. (2023, March 20). A note on 'semiotic physics.' LessWrong. https://www.lesswrong.com/posts/AdXzZDoYFqHCfupDB/a-note-on-semiotic-physics Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020). Zoom in: An introduction to circuits. Distill, 5(3). https://doi.org/10.23915/distill.00024.001 Olah, C. (2024, November 11). In D. Amodei, A. Askell, & C. Olah, Interview by Lex Fridman. Lex Fridman Podcast #452. Available at: https://lexfridman.com/dario-amodei-transcript/ Paris, P. (2018a). The empirical case for moral beauty. Australasian Journal of Philosophy, 96(4), 642-656. https://doi.org/10.1080/00048402.2017.1411374 Paris, P. (2018b). On form, and the possibility of moral beauty. Metaphilosophy, 49(5), 711-729. Parsons, G. (2023). Imperfection and Beauty of Character. In P. Cheyne (Ed.), Imperfectionist Aesthetics in Art and Everyday Life (pp. 296-309). New York: Routledge. Parsons, G., & Carlson, A. (2008). Functional beauty. Oxford University Press. Picca, D. (2025). Not minds, but signs: Reframing LLMs through semiotics. arXiv preprint arXiv:2505.17080. https://arxiv.org/abs/2505.17080 Saito, Y. (2008). Everyday Aesthetics. Oxford: Oxford University Press. Vallor, S. (2024). The AI mirror: How to reclaim our humanity in an age of machine thinking. Oxford University Press. Wojtkiewicz, K. (2023). How Do You Solve a Problem like DALL-E 2? The Journal of Aesthetics and Art Criticism, 81(4), 454-467. https://doi.org/10.1093/jaac/article/81/4/454/7571331 Wolfram, S. (2023, February 14). What is ChatGPT doing … and why does it work? Stephen Wolfram Writings. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ ### Assistant OK, let me think about this carefully. Nick's asking me to engage with a long ChatGPT analysis of how to cut his PPR submission down. He wants my agreements, disagreements, additions, and things I don't like — with reasoning before the final answer. He's in kicking-ideas-around mode. First, let me get the lay of the land before responding. The paper is around 12-13k words. ChatGPT counts 13.1k main text. I haven't counted myself but the structure ChatGPT lays out matches what I read: clean intro, ~1.4k §1, ~1.45k §2, big ~2.8k §3, ~1.7k §4, ~2.4k §5, ~2.7k §6, tiny conclusion. Section 3 is the biggest single block; sections 5 and 6 together are the conceptual heart. What's the paper actually doing? The core architecture: Carlson distinction between design and order appreciation, then arguing LLMs aren't appreciable as persons (§3) or as ordinary designed artifacts (§4), then the positive proposal that semiotic physics is the right knowledge for order-appreciating LLMs (§5), applied at three levels (§6). The original contributions are: (a) extending Carlson to LLMs at all, (b) the semiotic-physics-as-geology analogy, (c) the three-level structure (outputs/chats/models), and most distinctively (d) the dissolution of the "vibe-as-personality" temptation by reframing vibe as characteristic semiotic order. OK, now let me work through ChatGPT's recommendations one at a time and form my own view. CHATGPT'S DIAGNOSIS: that the paper is doing three things at once (intro to Carlson, education on LLMs, positive proposal on semiotic physics) and that the positive proposal is what should be protected. Hmm... yes I think that's right. The paper IS doing three things and the third is what's distinctive. So as a heuristic for cutting — defend §§5–6, compress §§2–4 — I agree. This is a clean priority rule. But wait. Let me complicate this. The negative arguments aren't just preparatory clutter. The §3 person-rejection is what makes the §6.3 "vibe as characteristic semiotic order" move land — without the prior rejection of person-appreciation, "vibe is not character but semiotic order" loses its bite. So §3 is doing real work that comes back to fruition in §6.3. That said, §3 still has internal redundancy (three parallel sub-dialectics each with the same shape: tempting view → why insufficient → Carlsonian verdict). So compress §3, but don't gut it. THE LOW-RISK CUTS CHATGPT PROPOSES. Let me go through these. 1. Repeated Carlson verdicts — yes, agree. Once the criterion is established, it doesn't need a fresh restatement after every sub-argument. The "Once again, Carlson's recommendation sharpens the point..." paragraph at end of §3.2 basically says what §3.1 already said. 2. Over-explained technical examples in §2 — strong agree. The "cat sat on the" example with placeholder token IDs and exact percentages, the "capital of France" autoregressive walk-through, the doctor/patient example for embeddings — all of these are pedagogical machinery that PPR readers don't need at this density. The argument really only needs ONE point: emergence from training rather than direct designer specification. Everything in §2 is in service of that. So §2 can radically shrink. 3. Literature exposition in §4 — agree partially. The Forsey + Parsons/Carlson recap is doing some scene-setting but mostly establishes that there exists a design-aesthetics literature you could try to apply. Two paragraphs could become one. 4. Double analogies — yes, this is one of ChatGPT's sharpest diagnoses. The paper has: raku + Pollock together; chemical physics + geology; video-game physics + LLMs; farmer/gardener/forester. Each pair gives one analogy more than is structurally necessary. But — important caveat — Pollock is doing TWO separate jobs in the paper (illustration in §4, philosophical template in §5.1 for "force-talk is literal"). So when ChatGPT says "use Pollock OR raku, not both" — fine, drop raku, keep Pollock — but Pollock should still appear in §5.1 with its full argumentative role. The §4 use is the one that's compressible. 5. Future-work paragraphs — agree, easy cut. The culture-mirror paragraph in §6.3 explicitly says it's not being developed. Cut it. The Olah quote can shrink. THE MEDIUM-RISK CUTS. 6. Compress §3 from three sub-arguments to one section — partial agreement. The make-believe / concessive / post-training trichotomy is structurally similar enough that the parallels could be tightened. But the post-training discussion in §3.3 is doing distinctive work because it's the strongest objection (chat personae are stable, so why not appreciate THOSE as persons?). I'd keep it as a separable move but trim the parallel "and Carlson again gives us the verdict" wrap-ups in each sub-argument. 7. Move Cross out of §3 — disagree. ChatGPT says Cross "distracts from" the person-appreciation argument in §3 and "returns naturally" in §5.2. But the §5.2 reinterpretation of Cross ("what the artist is doing... is interactive aspection") is doing nice dialectical work — it takes a view that was earlier critiqued as aestheticised make-believe and re-uses it under a different framework. That move REQUIRES the reader to remember that Cross was first introduced as person-appreciation-adjacent. If you cut Cross from §3, the §5.2 move becomes "let me introduce Cross to reinterpret him in my framework" which is much weaker. So either keep Cross in both places (compressed) or cut him in both places. I'd keep him in both because the dialectical re-purposing is one of the elegant structural moves of the paper. 8. Reduce §5.2 Practical Acquaintance — agree it can shrink. The farmer/gardener/forester triple is overkill. One agricultural practitioner is enough. But — see additional thought below — I'd actually go further and consider folding §5.2 into §6.2 entirely. 9. Two-output contrast in §6.1 — partial disagreement. ChatGPT recommends keeping the bee text and compressing the reasoning example, possibly dropping one for 8k. I think the CONTRAST is doing essential work: tight-constraints vs loose-constraints shows that semiotic physics applies across the model's behavioural space, not just to weird outputs. If you drop one example, semiotic physics becomes either "the framework for ordinary outputs" (if reasoning stays) or "the framework for transgressive outputs" (if bee stays). The whole point is that it works for both regimes. So both examples should stay. Compress both, but keep both. The reasoning example can become much terser — its job is comparative. The bee example carries the rhetorical weight, so it gets more space. 10. Shorten video-game physics analogy in §6.3 — agree. Three games is one too many. GTA for spectacle and Dark Souls for weight gives a clean contrast; Breath of the Wild adds nuance but at significant cost. Drop one game, name fewer mechanics, get to the point: "physics engines are appreciable as physics, not just for realism." THE HIGHER-RISK CUTS. 11. Drop one of three levels — strongly disagree. The three-level outputs/chats/models structure is announced in the introduction and is a major organising claim. Cutting one level breaks a promise made to the reader. Plus models-level appreciation is exactly where the most distinctive philosophical move happens (vibe-as-semiotic-order). I'd resist this even at 8k. 12. Merge §3 and §4 into "Two Inadequate Models" — disagree, with a caveat. The verdicts are asymmetric: §3 says person-appreciation FAILS (with no remainder); §4 says design-appreciation has a foothold but is INSUFFICIENT. That's not "two inadequate models" — that's "one rejection plus one partial endorsement that needs supplementing." The asymmetry matters because design-appreciation's partial validity feeds into the §6 framework (we still appreciate models AS artifacts at some level; the question is what additional knowledge we need). Flattening this loses the philosophical nuance. Caveat: if Nick must hit 8k and one structural cut is needed, maybe shorter §3 + shorter §4 could TOGETHER be presented as a single "preliminaries" arc — but with the asymmetric verdicts preserved. 13. Cut most of §2 and assume basic LLM literacy — agree with the spirit but with a different framing. Don't just cut §2; redistribute what survives. The bits §2 needs to deliver are: tokens, training-as-parameter-adjustment, post-training shaping conversational behaviour, and emergence from training rather than direct specification. The embedding/attention/RLHF detail isn't doing work in §2 itself; it's doing work in §4 (to make the "emergent organisation" point) and §5 (to identify the "forces" that get appreciated). So one option: shrink §2 hard but bring back the relevant detail at the point of use in §§4–5. This makes the technical material work-for-its-keep rather than front-loaded as a tutorial. WHAT CHATGPT MISSED OR UNDER-WEIGHTED. a) §5.2 / §6.2 redundancy. The §5.2 move ("prompting is intervention; responses reveal regularities") and the §6.2 move ("In a chat, prompting is intervention. Each turn is a probe...") are the SAME claim. The Cross-as-interactive-aspection paragraph in §5.2 also overlaps with §6.2's claims about chats as environments. One of these is doing the work the other is also doing. My suggestion: drop §5.2 as a separate subsection. Keep the practical-acquaintance claim (knowledge of semiotic physics can be held practically OR theoretically) — that's one paragraph. Fold the Cross reinterpretation into §6.2 where it more naturally lives. This removes the structural redundancy and tightens the move from "what is semiotic physics" to "how does it apply at three levels." b) Pollock appears twice. The §4 use ("Pollock as hybrid case where forces beyond specification complete the work") and the §5.1 use ("Pollock as the template for treating force-talk literally") are different but partly overlap. The §5.1 use is the philosophically important one (it grounds the "force-talk is literal causal abstraction not metaphorical personification" defence). The §4 use can shrink to a clause: "as in Pollock, forces beyond specification complete the work — see §5 for the philosophical implications." c) Footnote consolidation. Footnote 1 (forward-reference to non-paradigmatic artworks) — cut, the reader doesn't need warning. Footnote 2 (Carlson on the science/practical-knowledge continuum) — already covered in §5.2; cut or fold into body. Footnote 3 (performance personalities, Carroll on Andy Kaufman) — interesting but not load-bearing; cut. Footnote 4 (assistant as recurrent pattern, not new mechanism) — keep, this is doing work. Net saving: 2-3 footnotes. Useful if word count includes footnotes (PPR often does). d) References pruning. McGinn 1997, Davies 2012, Saito 2008 — quick scan suggests these aren't actively cited in the body. Abell 2020 appears via "cf. John 2021" rather than directly. If word count includes references, prune unused ones. Even if not, a shorter reference list signals discipline. e) Section-internal scaffolding sentences. The intro roadmap is the obvious case ChatGPT flagged, but each section also has its own mini-roadmap: §2 begins with "In this section we set the ground for appreciation by explaining..."; §3 begins with "In this section we ask whether..."; §6.1 has "To show how semiotic physics guides aspection of outputs, we consider two cases..."; §6.3 has "This notion of vibe, or characteristic feel, warrants pause." These are signposts that disciplined readers don't need. Each is small but they accumulate. f) The Farrell-Gopnik-Shalizi-Evans quote at the end of §3.3. Nice corroboration but the argument doesn't depend on it. Cut the quote, keep the citation. g) The Janus / Picca / Wolfram setup in §5.1 has internal redundancy — three sources are introduced with their distinct framings, then collapsed into a "shared core insight." The shared insight is the point. Could be one paragraph rather than two: "Recent work (Janus 2022; Picca 2025; Wolfram 2023) converges on treating LLMs as systems whose text-propagation regularities can be studied like physical laws." Then build on that. h) Kirchner 2023 / metasemi 2023 — these are both cited for the term coinage of "semiotic physics." Could probably be one citation. i) The "Different sorts of thing... merit design appreciation... warrant order appreciation" framing in §1 is a good place to land the distinction without quite as much preceding setup. The Rembrandt / Mountain mismatch examples could compress to one (the mountain example is more thematic to the paper because it links to natural-forces appreciation later). j) §4 uses "raku ceramic" alongside Pollock to make the point that some artifacts require knowledge beyond design-knowledge. ChatGPT noted this. I'd add: the §4 raku example could go entirely, and the parallel point could be made by saying "Pollock's action paintings (which we'll discuss more fully in §5) occupy this hybrid space" — a one-line gesture forward. k) Re-arrangement option that ChatGPT didn't propose: what if §2 ("what LLMs are") and §1 ("Carlson framework") were merged, with §1 setting up Carlson's distinction and §2 immediately doing the technical setup as a shorter follow-through? This would tighten the "preliminaries" into one block and let §§3–4 (the negative arguments) start sooner. l) An alternative re-arrangement: the §5.1 defence of force-talk-as-literal could move EARLIER in §5 or even into §4, where it would help bridge from "design appreciation is insufficient" to "we need a different kind of knowledge." Right now §5 does scene-setting → semiotic physics introduction → force-talk defence, when the force-talk defence is more naturally a hinge between §4 and §5. m) The "vibe warrants pause" paragraph in §6.3 is a meta-comment that pauses to remind the reader of the §3 rejection. The reminder could be much briefer: "Recall §3's rejection of person-appreciation: vibe is not character but characteristic semiotic order." One sentence. WHERE I'D PUSH BACK ON CHATGPT'S OVERALL ADVICE. - ChatGPT's "9k is the sweet spot" recommendation is presented as if there's an objective optimum. There isn't — there's the journal's word limit and Nick's tolerance for cutting. PPR submissions have specific limits (often 12k including refs, sometimes 10k). Nick should target the journal limit, not an aesthetic optimum. - ChatGPT spends a lot of energy on "philosophical version" reframings (Versions 1–5) which slide from "cut words" to "re-conceive paper." Those are interesting, but Nick's question was about word cuts, not about re-conceiving the paper. The reframings risk pushing him into rewriting rather than editing. - ChatGPT keeps suggesting cuts that smooth out the philosophical care of the paper. The paper's careful three-route critique in §3 (make-believe → thin agency → post-training/chat personae) is dialectically responsible. Compressing it to one move ("person-appreciation requires temporal depth, LLMs lack it") would be quicker but would also leave the paper open to the obvious counter-objection that it didn't engage with the strongest version of the opposing view. - ChatGPT's "if necessary, the most compressible level is chats" — disagree. Chats are arguably the most distinctive level given the environmental-aesthetics framing (chats = environments, in the literal Carlsonian sense). Outputs are the obvious case (single objects). Models are the abstract case (the ground of order). Chats are the case that most closely realizes the "environmental aesthetics" framing in the title. Cutting chats would undermine the title. - The "drop the reasoning example, keep the bee" advice for 8k loses the contrast that shows semiotic physics applies under both tight and loose constraints. A META-OBSERVATION ABOUT THE ADVICE PATTERN. ChatGPT's recommendations skew toward homogenisation: merge sections, drop one of two examples, cut multiple analogies down to one. This is the natural editorial reflex when cutting words. But sometimes the asymmetry, the doubleness, the parallel-but-not-quite-same is doing philosophical work. The two-example contrast in §6.1 is a case in point. So is the asymmetric verdict between §3 (rejection) and §4 (partial endorsement). When evaluating cuts, ask: is this redundancy, or is this purposeful pairing? ANOTHER THING. The paper's voice is patient and exact. The ChatGPT cuts mostly preserve that voice. The "make several sections into 'remarks'" suggestion would NOT preserve it — labelling §2 as "A schematic note on LLMs" would shift register sharply. Better to compress §2 in voice than to relabel it as a note. LET ME ALSO THINK ABOUT FOOTNOTES PROPERLY. The paper has at least four footnotes. PPR's word count typically includes footnotes. So aggressive footnote pruning is genuinely valuable. Footnote 3 (performance personalities, Andy Kaufman, Carroll) is a textbook case of "interesting digression that doesn't bear on the argument." Footnote 1 (forward reference) is weight without value. Footnote 2 (continuum between scientific and folk knowledge of persons) anticipates §5.2 and could fold there or go. OK I think I have enough to write a response. Let me organise. The response should: 1. Briefly accept the diagnosis (without re-summarising it). 2. List agreements with ChatGPT (with my brief reasoning). 3. List disagreements (with explicit reasoning — these are where I add value). 4. List things ChatGPT missed (the redundancies, footnotes, references, scaffolding sentences, re-arrangements). 5. Push back on the framing (no objective "right" word count; protect asymmetries; chats matter). 6. End with a note on what to actually do next given Nick's "kicking ideas around" stance. Bullets throughout. No bold. Italian sentence at end. Let me make sure this isn't too long but is genuinely substantive. Quick framing before the substance: ChatGPT's overall heuristic — protect §§5–6, compress §§2–4 — is right, but several specific moves it recommends would cost the paper philosophical work that's worth more than the words it saves. And there are larger families of cuts it didn't notice. Since you're in kicking-ideas-around mode, I'll lay out agreement, disagreement, additions, and a few framing pushes — not a single answer. ## Where I broadly agree with ChatGPT - §2 over-explains. The placeholder token IDs, exact probability percentages, the "capital of France" autoregressive walk, the doctor/patient embedding example — these are tutorial machinery a PPR readership doesn't need at that density. The paper only needs §2 to deliver one point: emergence from training rather than direct designer specification. Everything else there is in service of that. - The "two parallel registers" diagnosis. The paper is doing philosophical exposition + LLM tutorial + analogy-rich illustration + literature positioning all at once. Good diagnostic. Cuts that thin one register without amputating any are the highest-yield ones. - Repeated Carlsonian verdicts can go. Each sub-argument in §3 currently lands with its own "and so under Carlson…" paragraph. Once the criterion is established, restating it after each sub-argument is friction. - The culture-mirror paragraph at the end of §6.3 should go in any version under 10k. The paper itself flags it as "beyond scope." - The Olah quote can drop everything except "we kind of grow them" and the scaffold/light image. - One agricultural practitioner is enough — the farmer/gardener/forester triple in §5.2 is overkill. - The video-game analogy in §6.3 is too long for the work it does. Two games for the contrast (GTA for spectacle, Dark Souls for weight), not three. - "Reduce Forsey/Parsons literature exposition in §4 to one paragraph" — yes, this is scene-setting, not argument. ## Where I disagree with ChatGPT (or am uneasy) - Cutting Cross from §3.1. ChatGPT calls Cross a "distraction" there. But the §5.2 move that re-interprets Cross's exploration paradigm as "interactive aspection" is one of the elegant dialectical moments in the paper — and it depends on the reader remembering that Cross's view was earlier critiqued as aestheticised make-believe. Pull Cross out of §3 and the §5.2 move loses its point. Compress him in both places, don't excise him from one. - Merging §3 and §4 into "Two Inadequate Models" (recommended for 9k). The verdicts are asymmetric: §3 rejects person-appreciation outright, §4 grants design-appreciation a foothold but says it's insufficient. That asymmetry matters because §6 still treats LLMs as artifacts at some level — design-appreciation isn't dropped, it's supplemented. Flattening the two sections into "two failed models" misrepresents what the paper actually argues. Compress them in parallel, but keep them separate. - Dropping one of the §6.1 examples at 8k. The contrast between the reasoning output (tight constraints) and the bee text (loose constraints) is doing real argumentative work — it shows semiotic physics applies across the model's behavioural space, not just to outlier cases. Keep both, compress both. The reasoning example can become much terser since its job is comparative, but losing it leaves semiotic physics looking like a niche framework for transgressive outputs. - Dropping one of the three levels (especially chats). Chats are the level that most directly realises the "environmental aesthetics" framing the title promises. Outputs are individual objects, models are abstract grounds — chats are the actual environments. Cutting chats would undermine the title. - Recommending 9k as "the sweet spot." There's no objective optimum here; there's the journal's word limit and your tolerance for cutting. PPR has its own rules. Pick the target from the constraint, not from an editorial intuition. - "Reduce force-talk-is-literal defence" — flagged "moderate" cost. I'd say higher than that. This is the move that blocks the obvious objection that semiotic-physics is just personification by another name. The Pollock template + the trajectory/feature-space framing earns the literalism. Compress yes, but don't think of this as merely scaffolding. - The "convert §2 into 'a schematic note on LLMs'" / make-sections-into-remarks suggestion — this would break the paper's voice. Better to compress §2 in voice than to relabel it. - The Versions 1–5 reframings at the end of ChatGPT's reply slide from "cut words" to "re-conceive the paper." Useful as a thought experiment, but you asked about word cuts, not about rewriting. Don't let them push you into a more radical project than you signed up for. ## What ChatGPT missed (or under-weighted) - §5.2 / §6.2 redundancy. Both sections make the same move: prompting is intervention, each turn is a probe, the user comes to know the model through prompting it. This is the largest single redundancy in the paper. My suggestion: drop §5.2 as its own subsection, keep one paragraph on "knowledge of semiotic physics can be held theoretically or practically," and fold the Cross-as-interactive-aspection move into §6.2. Tightens the move from "what is semiotic physics" to "how does it apply at three levels." - Pollock appears twice. §4 (illustrative) and §5.1 (philosophical template for force-talk-as-literal). The §5.1 use is doing argumentative work and should keep its space; the §4 use can shrink to a one-line forward-pointer: "Pollock's action paintings, discussed more fully in §5, occupy a similar hybrid space." - Footnote consolidation. There are at least four footnotes, and PPR usually counts them. Footnote 1 (forward reference to "non-paradigmatic artworks") — pure scaffolding, cut. Footnote 2 (Carlson on continuum between scientific and folk knowledge) — already redundant with §5.2, cut or fold. Footnote 3 (performance personalities, Carroll on Andy Kaufman) — interesting but doesn't feed the argument, cut. Footnote 4 (chat persona as recurrent pattern, not new mechanism) — keep, doing work. - References pruning. Quick scan suggests McGinn 1997, Davies 2012, Saito 2008 aren't cited in the body. Abell appears only via "cf. John 2021." If the word count includes references, this is a free saving. - Section-internal scaffolding sentences. The intro roadmap is the obvious case ChatGPT flagged, but each section has its own micro-roadmap: "In this section we set the ground for appreciation by explaining…", "To show how semiotic physics guides aspection of outputs, we consider two cases…", "This notion of vibe, or characteristic feel, warrants pause." Each is small. They add up. - The Farrell-Gopnik-Shalizi-Evans quote ending §3.3. Nice corroboration; the argument doesn't depend on it. Cut quote, keep citation. - The Janus / Picca / Wolfram setup in §5.1 introduces three sources with distinct framings then collapses them into a shared insight. Could be one paragraph: "Recent work (Janus 2022; Picca 2025; Wolfram 2023) converges on treating LLMs as systems whose text-propagation regularities can be studied like physical laws." - Kirchner 2023 and metasemi 2023 are both cited for the same term-coinage. One could probably go. - Rembrandt + mountain mismatch examples in §1. Both make the same misclassification point. The mountain example is more thematic to the paper because it links forward to natural-forces appreciation; keep the mountain, drop or reduce the Rembrandt. - Re-arrangement options ChatGPT didn't propose. (i) The force-talk-as-literal defence in §5.1 could move forward to be a hinge between §4 and §5, since it's where the paper transitions from "design isn't enough" to "we need different knowledge." (ii) The technical detail front-loaded in §2 (embeddings, attention, RLHF) could be redistributed to the points of use (§4 for emergence, §5 for forces) so it earns its keep where it works rather than being declared in advance. (iii) The "vibe warrants pause" reminder in §6.3 could compress to a single sentence: "Recall §3's rejection of person-appreciation: vibe is not character but characteristic semiotic order." ## On the meta-question of which target to pick - The "right" number is your journal's word limit, not an editorial sweet spot. If PPR allows 12k inclusive, the paper basically already fits and the work is light tightening. If 10k, ChatGPT's Option B (protect positive account, cut setup harder) is the right shape. If you're being asked for 8k, that's a different paper and you should know that — don't pretend you're tightening when you're restructuring. - The "protect the positive account" heuristic is correct and worth treating as a hard constraint when making cut decisions. §§5–6 are where the original moves happen. The semiotic-physics-as-geology analogy and the vibe-as-characteristic-semiotic-order move are what the paper is for. - A general editorial instinct to flag: ChatGPT's recommendations skew toward homogenisation — merge two sections, drop one of two examples, collapse multiple analogies down to one. Sometimes the doubleness is doing philosophical work (the §6.1 contrast, the §3/§4 asymmetry, Pollock-twice). When evaluating any cut, ask: is this redundancy, or purposeful pairing? ## A small thing on the file itself - The .md file has a 158k-character base64 image embedded near the references. ChatGPT noted this would bloat the file but won't affect word count. It would also probably mangle a journal submission system. Worth replacing with a normal image link before submission. ## What I'd actually do next - If you want, I can do an actual word count on the paper rather than relying on ChatGPT's estimate, so you know what gap you're closing. - I could also draft a tight version of any one section (§2 is the highest-yield candidate) so you have a concrete sample to react to rather than abstract suggestions. - Or — given you're kicking ideas around — we could pick one of the disagreements above (e.g. "do §3 and §4 stay separate?") and think it through more carefully before you commit to a cut strategy. *Tagliare un saggio è come potare un albero: la forma giusta non si vede tagliando di più, ma capendo quali rami portano frutto.* ### User I want you to discard any obvious non starters from chatgpt. then i want you to use the activated skill to produce a very very very detailed slide deck that me and my coauthor can go over to examine all the options available. yours and chatgpts. ### User [Request interrupted by user] ### Assistant Quick note on the health-context hook: it fired on a false-positive keyword match — this task is editorial work on a paper, not health-related. I won't read the health file since it's not relevant here. Now let me invoke the deck workflow you use. ### User that is the wrong skill. this is what i want. ### Assistant OK, I have what I need. Building a Flexoki Vault deck in portable mode (so the coauthor can view it without iA Writer fonts installed). Saving to `~/Documents/` per the export-path convention. Discarding these ChatGPT non-starters first: - Drop one of the three levels (outputs/chats/models) - Merge §3 and §4 into "Two Inadequate Models" - Cut Cross from §3 only (asymmetric excision) - Drop one of the §6.1 examples - Convert sections to "remarks" - Treat 9,000 as the editorial sweet spot Each non-starter gets its own slide explaining why. Then everything else (agreements, additions, structural options, targets, principles, workflow, open questions) gets a slide too. Building now. ### Assistant Deck is live in the Launch preview panel — should be visible there now. What's in it: - 62 slides, Flexoki Vault style, portable font mode (so your coauthor doesn't need iA Writer installed) - Part I: the six discarded non-starters, each on its own slide with reasoning - Part II: 8 ChatGPT moves agreed with (low/medium risk) - Part III: 10 additions ChatGPT missed - Part IV: 4 structural options (higher risk) - Part V: 5 word-count target scenarios (12k → 8k) - Part VI: 4 decision principles (asymmetry test, doubleness test, etc.) - Part VII: 6-step workflow - Part VIII: 4 open questions for you and your coauthor How to use it: - Arrow keys / space / page up-down / home / end for navigation - Nav dots fade in on the right edge when the cursor is over the page - Each option is self-contained — you can land on any slide and assess it in isolation, no need to read sequentially - Yield estimates and risk flags on most option slides If you want it shareable as a link rather than a file, I can push it to a secret gist next. *Tagliare a quattro mani richiede prima un foglio condiviso: ogni opzione su una sua diapositiva così che ognuno possa puntare il dito senza confondere il taglio con la struttura.* ### User Can you give me something about a third the size so i can see the main points clearly ### Assistant Building a condensed version (~19 slides). Same Flexoki Vault style, just the headlines. ### User [Request interrupted by user] ### User i also wasn't asking you to make slides about what you agree with and don't agree with. I just wanted you to give me what you considered the best options with some details and justification. it should not betto wordy either. Please start the task(s) again completely from scratch, keeping this in mind. ### Assistant Got it — best options only, with brief justification, no agree/disagree framing. Building from scratch. ### Assistant Updated deck is in the preview panel. 18 slides, organised as: - 1 framing slide (the principle) - 1 section-sizes orientation - 9 best options (each on its own slide with yield + risk indicator) - 1 word-count target table - 1 decision principles, 1 workflow, 1 pre-submission housekeeping - 1 open-questions slide for the conversation with your coauthor - Title + closing The nine options are ranked roughly by tier: Options 1 and 8 are the highest-yield, lowest-risk moves; Options 2 and 9 are the structural ones to discuss together. *Meno diapositive, più mordente: ogni opzione deve poter essere giudicata in dieci secondi.*