The Environmental Aesthetics of Generative AI submitted to be cut down by a few thousand words 8 May 2026.md File Below is a chat with Opus about cutting a paper of mine. In the course of the conversation you'll see I decided I want to cut it down to 8,000 words. Okay. And I've attached the the paper in question. What I would like you to do is evaluate Claude's suggestions about how to achieve 8,000 words. And give me your view on what I should do. It can be as similar or as different to Opus as you like. CHAT: \*\*You\*\* 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. --- \*\*Claude\*\* Right — let me first establish what we're actually working with, then sketch out the editorial logic, and then give you concrete options at each target. ## Where the words actually are The body comes to \*\*13,166 words\*\* (excluding the references, which add another 770). So you're not at 12,000 — you're at roughly 13,000–13,100 of "real" prose. That changes the maths slightly: cutting to 10,000 is ~24%, to 9,000 is ~32%, to 8,000 is ~39%. Fairly aggressive at the lower end. Section-by-section breakdown of the body: | Section | Words | % | | | --- | --- | --- | --- | | Introduction | 363 | 2.8% | tight already | | §1. Design vs. Order | 1,405 | 10.7% | dense scaffolding | | §2. What LLMs Are | 1,468 | 11.1% | technical primer | | §3 intro | 266 | 2.0% | | | §3.1 Make-believe | 985 | 7.5% | Mallory + Cross | | §3.2 Concessive mindedness | 814 | 6.2% | Frankish | | §3.3 Post-training, chat personae | 728 | 5.5% | objection-handling | | §4. Artifacts | 1,739 | 13.2% | heaviest single section | | §5.1 Textual Regularities | 1,777 | 13.5% | tied for heaviest | | §5.2 Practical Acquaintance | 657 | 5.0% | | | §6 intro | 170 | 1.3% | | | §6.1 Outputs | 1,079 | 8.2% | two case studies | | §6.2 Chats | 517 | 3.9% | tight | | §6.3 Models | 906 | 6.9% | several digressions | | Conclusion | 212 | 1.6% | tight | §4 and §5.1 together are 3,516 words — over a quarter of the paper. §3 in toto is 2,793 — over a fifth. These are your big targets. ## Diagnostic: what's load-bearing and what's not Before suggesting cuts, here's how I'd map the argument's spine against the prose: \*\*The six-move spine of the paper:\*\* 1. Carlson distinguishes design from order appreciation (§1) 2. LLMs are token-predictors trained on text; their organisation emerges from training (§2) 3. Person-based aesthetics is wrong for LLMs (§3) 4. Pure design aesthetics is wrong for LLMs (§4) 5. Order appreciation, guided by \*semiotic physics\*, is right (§5) 6. It applies at three levels: outputs, chats, models (§6) Every section earns its keep in principle, but several sections do their work two or three times over, and a few do work that the spine doesn't strictly need. \*\*Most expandable (highest cut-yield-to-damage ratio):\*\* - \*\*§3.3 (728 words).\*\* This reads as a recently-added objection-handler. The substantive claim — that post-training chat personae are recurring response profiles, not subjects with lives — is already implied by §3.1 and §3.2 plus the post-training apparatus introduced in §2. The section adds rhetorical reinforcement and the Farrell/Gopnik/Shalizi/Evans quote, but the spine survives without it. Cuttable in whole or substantially trimmed. - \*\*§4 (1,739 words).\*\* Has noticeable redundancy. The "form follows function" point is rehearsed three times; the move from Carlson to Forsey to Parsons-and-Carlson is set up at length but they all converge on the same claim; the raku/Pollock analogies pull in the same direction (one would do); the "design has its place but the action is in emergence" upshot is restated in three different paragraphs. Probably 400–600 words of slack here without touching anything load-bearing. - \*\*§5.1 (1,777 words).\*\* Has a similar shape. The mechanistic-interpretability detour (line 173) is useful but expensively staged via the chemistry/geology analogy; Janus, Picca, and Wolfram are each quoted when one extended treatment plus a note on the others would suffice; the Pollock parallel reappears here when it could live just in §4; the aspection-shift paragraph (line 189) and the language-as-object paragraph (line 191) restate the shift in two passes. - \*\*§6.1's two case studies.\*\* The reasoning case and the bee text both illustrate the same thesis (semiotic physics makes order visible). Strategically you might keep just the bee text — it's the more striking and revealing illustration, and Carlson's "some cases reveal order better than others" remark gives you cover for asymmetric treatment. - \*\*§6.3's diversions.\*\* The video-game analogy (~150 words), the "distinguishing appreciation from capability/safety/benchmarking" paragraph (~120 words), and the "mirror of culture" future-work flag (~120 words) are all detachable. None is on the spine. - \*\*Quotation overhead.\*\* The long Carlson quote on order appreciation (line 33), the form-follows-function quote (line 27), the Olah "growing not building" quote (line 71), the Wolfram trajectory-in-feature-space quote (line 177), and the Carlson-via-Janson Pollock quote (line 187) — most could be paraphrased at half the length without loss. \*\*Most load-bearing (touch with care):\*\* - The §1 Carlson framework. You need design vs. order on the table. But §1.x on person appreciation (lines 39–43) is doing optional work — the three options for extending Carlson to persons could collapse to a single sentence, since you don't need to settle which is right. - §2's technical content from "embeddings" through "post-training" — the philosophy depends on it. - §3.2 (Frankish/concessive mindedness). This argument is distinct from the make-believe argument and is the more philosophically substantive of the two negative subsections. - §5.1's central claim (semiotic physics articulates causal forces at the level of perceivable text), §5.2 (practical acquaintance), §6.2 (chats as environments), §6.3 vibe-paragraph. ## Three editorial philosophies You can think of cuts as falling under three increasingly aggressive approaches. The right mix probably uses Approach A throughout and reaches for B and C as the target shrinks. \*\*Approach A — Distributed thinning.\*\* Tighten prose everywhere; paraphrase quotes; remove signposting and recaps; collapse paired examples; reduce hedges. Preserves every argument and section. Realistic yield: 1,200–1,800 words across the paper. Gets you comfortably to 11,500 and within reach of 11,000. Risk: low. Cost: some of the pedagogical breathing space goes; the paper feels denser. \*\*Approach B — Targeted excision.\*\* Identify discrete subsections or whole paragraphs that can be cut without rewriting the spine. Candidates above. Yield: another 1,000–1,500 words on top of A. Gets you to 10,000–10,500. Risk: moderate (some readers may want what's cut). Cost: certain reinforcing moves and counter-objections disappear. \*\*Approach C — Structural compression.\*\* Merge sections, drop one of two parallel illustrations, reframe the technical primer in terms appropriate to a sophisticated audience rather than as a tutorial, fold the negative arguments into a single tighter §3. Yield: another 1,000–1,500 words on top of A+B. Gets you to 8,000–9,000. Risk: higher (rewriting required, and the paper's leisurely diagnostic feel is lost). Cost: some readers might find the negative argument too compressed; the tutorial-friendly §2 becomes opaque to non-specialists. ## Concrete cut packages I'll give you several options at each target, varying which approach dominates. ### Target: ~10,000 words (~3,100 words to cut) #### Option 10A — "Conservative thinning" Pure Approach A. Walk through every section trimming 15–25% from each by: - paraphrasing the Carlson order-appreciation quote (line 33) and the form-follows-function quote (line 27); - shortening the cat-on-the-mat worked example in §2; - paraphrasing the Olah quote; - trimming the §1 person-appreciation discussion to a single paragraph saying "if Carlson's framework extends to persons at all, it must do so under his recommendation"; - collapsing the cliff-face / Rembrandt double example in §1 to one; - removing recap clauses ("As established in §2…", "Having set aside…"); - paraphrasing all block quotes over 40 words. What survives: every argument, every example, every named interlocutor. What goes: pedagogical generosity, some breathing room. \*\*Faithfulness: very high.\*\* Best if you want a version someone reading both could see as "the same paper, polished." #### Option 10B — "Light surgery + thinning" Approach A across the board, plus: - \*\*Cut §3.3 entirely\*\* (–728 words). Move one or two sentences about post-training and chat personae into §3.2 and into §2's closing paragraph. The Farrell/Gopnik/Shalizi/Evans quote can move to a footnote in §3 conclusion or §4 opening. - \*\*Trim §4 by ~300 words\*\*, mainly by dropping the explicit setup of Forsey and Parsons-Carlson as separate views (mention them more in passing) and choosing either raku \*or\* Pollock as your hybrid analogy. - \*\*Trim §5.1 by ~300 words\*\*, mainly by paraphrasing the Wolfram quote and folding the "three objects to investigate" gloss into a single sentence; trimming the language-as-object paragraph (line 191), which restates the aspection-shift point of line 189. - Light thinning elsewhere. What goes: §3.3's redundant reinforcement; some of §4's catalogue of design-aesthetic theorists; one of two craft-and-art analogies. What survives: the spine intact; both case studies in §6.1; both negative arguments at full strength; the practical-acquaintance discussion. \*\*Faithfulness: high.\*\* Probably my recommendation if you can spare losing §3.3 (which I'd argue you can). #### Option 10C — "Keep §3.3, surgery elsewhere" For the case where you want to keep §3.3 because it's responding to a specific reviewer or audience concern: - Trim §3.3 by ~250 words (cut the lengthy setup and the closing paragraph; keep the assistant-as-pattern-not-mechanism core). - Cut §6.3's video-game analogy (–150 words) and "mirror of culture" paragraph (–120 words). - Cut or compress the §6.3 "distinguishing from capability/safety/benchmarking" paragraph (–100 words). - Compress §6.1's reasoning case study by ~250 words, keeping the bee text at full length. - Approach A thinning elsewhere. \*\*Faithfulness: high.\*\* Good if §3.3 is non-negotiable. ### Target: ~9,000 words (~4,100 words to cut) At this target you have to choose at least one of: - Sacrificing §3.3 entirely; - Sacrificing one of the two case studies in §6.1; - Substantially compressing §2's pedagogical primer; - Removing §1's person-appreciation discussion (and rebuilding the link to §3 in a sentence); - Removing §6.3's diversions wholesale. #### Option 9A — "Cut the tertiary moves" - Cut §3.3 entirely (–728). - Compress the reasoning case study in §6.1 to a brief paragraph (–250 to –300); keep bee text full. - Cut §6.3's video-game analogy, future-work paragraph on culture, and the capability/safety/benchmarking distinction paragraph (–350 to –400). - Trim §1's person-appreciation digression to one sentence (–250). - Approach A thinning across §2, §4, §5.1 to yield another 1,500–1,800. What survives: spine, both negative arguments at strength, full bee-text discussion, model-level argument intact. What goes: redundant objection-handling, one illustrative pair, three diversions, a side-discussion about persons. \*\*Faithfulness: moderate-to-high.\*\* Probably the cleanest 9K option. #### Option 9B — "Compress the technical primer" - Halve §2 from ~1,470 to ~700 words (–770). For a philosophy of perception/mind audience, the cat-on-the-mat tutorial is overcatered. You can do "tokens, embeddings, autoregressive sampling, RLHF" in 700 words for this audience without losing what the philosophy needs. - Cut §3.3 (–728). - Trim §4 by ~400. - Trim §5.1 by ~400. - Light Approach A elsewhere. What goes: pedagogical accessibility for non-specialists; some §4/§5.1 throat-clearing. \*\*Faithfulness: moderate.\*\* This is the right option if your target venue's readership is technically sophisticated. Risk: if reviewers come from aesthetics-only backgrounds, the compressed §2 may feel jumpy. #### Option 9C — "Merge §3.1 + §3.2 + §3.3 into one tighter §3" - Rewrite §3 as a single argument with three sub-moves rather than three subsections (–500 to –700 vs. current §3.1+§3.2+§3.3 of 2,527 words). The Cross discussion can compress to a footnote; Mallory's chatbot fictionalism gets a tight summary; Frankish gets the slightly longer treatment because his argument is the more demanding; the §3.3 material becomes a closing two-paragraph caveat. - Trim §4 by ~300 and §5.1 by ~300. - Cut §6.3 diversions (–350). - Approach A elsewhere. \*\*Faithfulness: moderate.\*\* This option has the merit of producing a tighter, more readable §3, which is currently a little baggy. It's a deeper rewrite though. ### Target: ~8,000 words (~5,100 words to cut) This is heavy and requires structural moves. Here are three ways to land it. #### Option 8A — "Maximal targeted excision" - Cut §3.3 entirely (–728). - Halve §2 (–770). - Halve one of the §6.1 case studies (–400). - Cut §1's person-appreciation discussion (–250) — replace with one sentence. - Cut §6.3's three diversions (–400). - Trim §4 aggressively to ~1,100 (–600). - Trim §5.1 aggressively to ~1,200 (–600). - Trim §3.1's Cross discussion (–250); keep the Mallory analysis lean. - Approach A thinning elsewhere (~–200). This is brutal but additive: you keep every section but trim each substantially. The paper would feel notably more clipped, but every argument would still be there. #### Option 8B — "Restructure §3 + §4 into a single negative section" - Combine §3 and §4 into one negative section ("Why person-based and design-based appreciation are both wrong") of ~1,800 words instead of the current 4,532. Yield: ~–2,700. - Trim §2 by 400, §5.1 by 400, §6 by 500. - Approach A elsewhere. This reduces the paper's "diagnostic" feel — currently you walk the reader carefully through \*why\* each tempting option fails before turning to your positive view. The merged structure is more efficient but loses the rhetorical patience. #### Option 8C — "Drop one case study; absorb the Pollock/raku analogy into §5" - Cut the reasoning case study from §6.1 (~–400). Bee text alone. Carlson's "some cases reveal better than others" remark covers this. - Cut the raku/Pollock analogies from §4 and develop the Pollock parallel only in §5.1 where it does its real work (–300 net from the move; –250 in §4). - Cut §3.3 (–728). - Halve §2 (–770). - Cut §1's person-appreciation discussion (–250). - Cut §6.3 diversions (–400). - Trim §4 by another ~300 and §5.1 by ~400. - Approach A elsewhere. Probably the most coherent 8K version. The Pollock parallel benefits from being made once, properly, in §5 where it's doing its main work. The reasoning case is probably the more dispensable of the two §6.1 illustrations. ## Cross-cutting prose-level moves you can apply at any target Independent of which package you adopt: - \*\*Quote audit.\*\* Every block quote over 40 words should justify itself. The Carlson order-appreciation block (line 33) is doing real work but could be 40 words instead of 80. The Olah "we grow them" passage is rhetorically nice but the substance is one sentence. The Wolfram "linguistic feature space" quote can become a paraphrase that takes one line. Net yield from this alone is probably 250–400 words. - \*\*Recap clause cull.\*\* Many paragraphs open with "As we established in §2…" or "Having set aside the person-based options…" or "Section 2 already noted that…". These are individually small but collectively account for 200+ words. Most can go. - \*\*Hedging compression.\*\* "It is therefore not surprising that…", "In a sense, of course…", "What we might call…", "It will be enough to note that…". A pass on these alone yields 100–200 words. - \*\*Paired-example collapse.\*\* Most of your examples come in pairs (cliff-face and Rembrandt; chair and bridge; raku and Pollock; reasoning text and bee text; farmer and gardener and forester). One of each pair is usually doing the work. Pick the more vivid and cut the redundant partner. - \*\*Section-opener compression.\*\* Every section currently opens with a paragraph that recaps where we are and announces what comes next. Most could be a sentence. - \*\*The "we shall see" / "as we will argue" gestures.\*\* These are forward-looking promises that the prose doesn't really need; the reader will see when they get there. ## What I'd recommend If you asked me to pick: \*\*Option 10B\*\* is the cleanest 10K landing. \*\*Option 9A\*\* is the cleanest 9K. \*\*Option 8C\*\* is the most defensible 8K. The single highest-yield move is cutting §3.3 — I genuinely think the spine doesn't need it, and what it adds (the chat-persona-as-pattern point, the Farrell/Gopnik/Shalizi/Evans quote) can be redistributed in two or three sentences. After that, the highest-yield moves are paragraph-by-paragraph thinning of §4 and §5.1, and dropping the reasoning case study from §6.1 if you can bear it. The lowest-yield-but-highest-pain moves would be touching §3.2 (Frankish), §5.2 (practical acquaintance), §6.2 (chats), or the conclusion. These are all already lean and load-bearing; cutting here would buy little and cost much. A general sanity check: the paper as it stands has the architecture of a 12–14K paper, and you can get it to 10K without rebuilding anything. Below 10K you're choosing what to lose, not just compressing what you have. So I'd think hard about whether 10K is acceptable for the venue — a lot of the philosophical generosity of this draft is in the diagnostic patience of §3 and §4, and you start to lose that around 9K. Happy to drill into any specific package, or to actually do a sample tightening pass on one section so you can see what Approach A looks like in practice. --- \*\*You\*\* I want to work on getting it chopped down to 8,000 words. Okay, however when you gave me those options of 8A, 8B, 8C, etc. they weren't written clearly enough. me to understand what you were suggesting. --- \*\*Claude\*\* I see the user found my previous explanations of the 8K options too vague—I need to be more concrete about what each version actually looks like and how they differ in substance, not just word counts. Let me reconsider what made those explanations fall short and focus on showing the actual shape and character of each option rather than just listing cuts. I need to be clearer about what each option actually produces. For 8A, I'm keeping the full six-move structure but trimming everywhere — tightening quotes, removing redundancies, compressing examples, and dropping §3.3 entirely. The paper stays the same shape but feels denser and loses some breathing room. For 8B, I'm collapsing the two negative sections (§3 and §4) into a single argument that both person-aesthetics and pure design-aesthetics fail, which shifts weight toward the positive case for semiotic physics but sacrifices the diagnostic patience the current structure provides. For 8C, I'm keeping the original structure but cutting the reasoning case study in §6.1 and keeping only the bee example, removing the redundant Pollock parallel from §4, and trimming various digressions throughout. This preserves the more memorable illustration while losing some analogical depth. I should probably consider a few other directions too — maybe something that focuses the argument even more tightly, or explores whether there's a way to strengthen the positive case without cutting so much. One angle: what if I drop the design-aesthetics critique in §4 entirely and fold its core claim into §5 instead? That would let §5 do the real argumentative work as the contrast, though it's a bit risky since §4 sets up something important. Another direction: what if I just compress the entire setup — §1, §2, and §3 — down to their essentials and save the full strength for the positive argument where it matters most? I'm settling on a clearer structure for presenting these options. Each one needs a straightforward explanation of what gets removed, how the paper reads afterward, and what trade-offs come with it. I'll use focused paragraphs with headers rather than bullet points to match the plain-language style, and keep the language concrete and direct so the choice becomes obvious. For Option 8A specifically, I'm cutting §3.3 entirely and folding its content into nearby sections, trimming the personal digression in §1 down to a single sentence, removing the video-game analogy and related tangents from §6.3, and then systematically reducing the longer sections—halving §2 by dropping the cat-on-the-mat example and streamlining the technical narrative, cutting §4 by keeping only the Pollock analogy and trimming the catalogue material, and condensing §5.1 by paraphrasing the Wolfram quote and consolidating the language-related discussion. The result is the same six-move argument with all section titles and core claims intact, but the prose is noticeably denser and less pedagogical—§2 now reads more like a primer for sophisticated readers, §3 has two subsections instead of three, and §5.1 has fewer interlocutors. The overall reading experience is clipped and demanding rather than generous, with block quotes over 40 words paraphrased and paired examples compressed throughout. What I'm losing is the leisurely diagnostic tone and the extended Cross discussion that thins to a single paragraph; the Wolfram quote becomes a paraphrase rather than a full citation. Now I'm considering Option 8B: merging the two negative sections. Instead of spending 4,532 words across §3 and §4 arguing separately that person-aesthetics and design-aesthetics both fail, I'd combine them into one ~2,000-word section. That saves roughly 2,500 words right there, plus another 400 from trimming §6.3 diversions. The structure would compress to five moves: Carlson's framework, what LLMs actually are, why both rival aesthetics collapse, semiotic physics as the solution, then the three-level model. The merged negative section would lay out the temptations first, then spend about 700 words dismantling the person-based view (Mallory and Frankish in tighter form), another 700 on pure design-aesthetics, and close with a 200-word bridge to the positive argument. The trade-off is losing the patient three-subsection treatment of person-based options—Mallory gets one paragraph instead of several, Frankish gets two instead of a full subsection, and the chat-based counterargument gets compressed significantly. The reading experience shifts: the negative case moves faster while the positive case gains proportional weight, creating a clearer "here's what fails, here's what works" trajectory. Reviewers expecting careful step-by-step engagement with each rival might find the negative section rushed, but since that case is mostly diagnostic and depends on accepting Carlson's framework, the core argument doesn't require extended treatment. Now I'm mapping out a more aggressive consolidation—dropping the reasoning-style case study entirely, keeping only raku in the aesthetics section while moving Pollock later where it carries more weight, cutting the person-appreciation digression and most of §3.3, halving §2 down to around 700 words, and trimming the diversions in §6.3 about video games and capability concerns. This saves substantial space while keeping the spine intact. So I'm looking at the trade-offs in this 8C version — dropping one of the two case studies in §6.1 means I lose the chance to show that semiotic physics works across both ordinary and unusual outputs. The bee text alone demonstrates that order emerges where it seems absent, but the reasoning case was doing different work, showing the principle applies more broadly. That's a real loss, though the version stays philosophically coherent and is straightforward to execute since it's mostly trimming rather than restructuring. Now I'm considering 8D as a separate option — compressing the setup sections (§1, §2, §3) down to bare essentials in plain prose, then giving the later sections (§4, §5, §6) their full development. This would flip the proportions: thin introduction, thick philosophical and analytical core. the positive proposal feels immediate and direct. The compressed diagnostic might strike some readers as thin, but others will appreciate cutting straight to the substantive argument. I'm thinking I should present the user with several distinct options rather than just one path forward—maybe four approaches with genuinely different philosophies, then flag which one I'd lean toward recommending. The real tension I'm seeing is between 8A and 8C: one keeps both case studies but aggressively trims the setup, while the other sacrifices a case study entirely to preserve the philosophical depth of the middle sections. I should also explore whether there's a way to protect §3 and §4's full argument by cutting more strategically elsewhere. Option 8E is emerging as a possibility: keep the diagnostic core of §3 and §4 intact, but cut hard from the technical primer in §2, drop one of the case studies, trim the model-level discussion in §6.3, and remove the person-appreciation tangent from §1. This would mean reducing §2 to around 600 words, excising the reasoning case study, lightening the bee-text section, compressing §6.3 to focus only on the vibe paragraph while dropping the video-game and capability discussions, cutting the §1 digression, removing §3.3 entirely, and doing light prose tightening across §3, §4, and §5. The resulting paper would have the negative arguments fully present and the positive case intact, but with a bare-bones technical primer, only the bee text in §6.1, and §6.3 as a single argumentative thread without diversions. This works well for readers already familiar with LLMs, though it risks being too thin for a mixed audience unfamiliar with the basics. Now I'm settling on which option to actually present. Given the user's earlier frustration with unclear options, I should narrow this down to 3-4 clear choices, make explicit what each version looks like, and use a consistent template so they're easy to compare. I'm going with four distinct philosophies: an even trim across the board, a structural merge of the negative sections, dropping one case study entirely, and front-loading a thin version. Let me reframe these more clearly—the first approach trims everything proportionally without cutting any subsections entirely, keeping all the moves and examples but tighter overall. The second combines §3 and §4 into a single negative section... Now I need to map out what each version would actually contain, and I should also factor in the user's stylistic preferences—British English, avoiding value-laden language like "important" or "crucial"... The user's style guidance is pulling in a different direction from the earlier preferences. They want plain language, direct and affectless, no academic flourishes—straightforward vocabulary that explains complex ideas without unnecessary latinate constructions or interpretive language. That's more recent and explicit, so it takes priority. Now I'm drafting the response. I want to be concrete about each option, but I also need to respect length. The user asked for clarity, not brevity, so I'll give them the detail they need to understand each path forward, just in plain language. For each option, I'll lay out the editorial idea, show exactly what gets cut and where, and describe what the paper looks like after. I'm thinking a table might work for comparison, but I should write it out first and see if that's clearer than prose. --- \*\*You\*\* "If you want a paper that reads more directly and has a sharper argumentative arc, Option 2 is the better choice. The merged negative section is in some ways more rhetorically effective than the current two separate sections, even though it loses some material."i think something option b flavoured is the best. But I also think that Section 2 could be much improved, and perhaps that this will lead to improved sections 5 and 6. --- \*\*Claude\*\* Good. Option 2 is the right structural move, and you've spotted something real about §2. ## What §2 is currently doing wrong §2 is a tutorial. It walks the reader through tokens, then probabilities, then pre-training, then embeddings, then attention, then autoregressive decoding (with the cat example a second time), then post-training, then product wrapping. By the end the reader understands how an LLM works. That's the wrong design brief. §2 needs to put on the table the technical primitives that the rest of the paper will use. It is currently organised in pedagogical order ("here is what you need to know first, then next") rather than in argumentative order ("here is what §3 will reject, here is what §5 will redescribe, here is what §6 will redeploy at three scales"). The cost of this mismatch is most visible in §5.1. §5.1 takes 1,780 words partly because it has to re-articulate what embeddings, attention, and post-training actually produce in the text — vocabulary clustering, coherence dynamics, register stability, alignment markers. §2 introduced the mechanisms. §5.1 introduces the textual effects. These should be one continuous setup, not two. §6 has a similar problem at smaller scale. Its three levels (outputs, chats, models) are introduced as a fresh conceptual move, with the tree-forest-biosphere analogy doing the work of presenting them. But the three levels are already implicit in §2's technical material: an output is one generation; a chat is context accumulating across turns; the model is the trained system itself. §2 could put the three levels on the table directly, and §6 could lean on §2 rather than reintroducing them. ## What §2 should do instead Restructure §2 around four primitives, in the order the rest of the paper will use them: \*\*1\\. Token-level operation.\*\* The system generates text by repeatedly sampling next tokens from learned probability distributions. One paragraph. No worked example. \*\*2\\. The trained landscape.\*\* Pre-training produces three features that matter for later sections: embeddings (which produce semantic clustering), attention (which tracks long-range dependencies), and a probability distribution over continuations that reflects regularities in the training corpus. Two paragraphs. \*\*3\\. Post-training shapes the landscape.\*\* RLHF biases the system toward assistant-like patterns. Certain regions of the behavioural space become easy to reach; others become hard to reach. One paragraph. \*\*4\\. Grown, not designed.\*\* Designers specify the architecture and training objective; the organisation emerges from training. Olah's point. One paragraph. Then a closing paragraph that previews the three scales at which the trained system can be characterised: a generation as one trajectory through the landscape; a chat as path-dependent context accumulation across turns; the model as the underlying landscape that produces both. Target: about 700 words, down from 1,470. ## What this does for §5 If §2 has named embeddings as producers of semantic clustering, attention as the mechanism behind long-range dependencies, and post-training as the source of assistant-like patterns, then §5.1 does not need to reintroduce any of this. §5.1's job becomes: (i) These features have textual signatures the reader can perceive — vocabulary clustering, contextual threading, register stability, alignment markers. (ii) "Forces" is the right word for these factors: they are causes of how text propagates, not metaphorical projections. (iii) Knowledge of these forces guides aspection in the way geological knowledge guides aspection of a landscape. Three moves. About 1,000 words instead of 1,780. The chemistry/geology detour about mechanistic interpretability becomes much shorter, because the question "why work at the textual level rather than the circuit level?" has a direct answer once §2's primitives are already textual-effect concepts. The Janus, Picca, and Wolfram material compresses to one paragraph that names the literature as the source of the term "semiotic physics" rather than three blocks each developing a different framing. §5.2 (practical acquaintance) is mostly fine as it stands and only needs light tightening. ## What this does for §6 §6's introductory paragraph (170 words) and the tree-forest-biosphere analogy can become two sentences, because §2's closing paragraph has already put the three scales on the table. §6.3 ("Appreciating Models") currently spends time explaining what it means to appreciate the underlying model rather than its outputs. With §2 having named the model as the learned landscape, §6.3 moves straight to characterising what model-level appreciation involves. The video-game analogy and the capability/safety/benchmarking paragraph come out. The "mirror of culture" paragraph comes out (it is a flagged future-work gesture). §6.2 (chats) gains less directly but still benefits: path-dependence is already a feature named in §2, not a fresh introduction. §6.1's two case studies remain the question. With the §2 reorganisation, the reasoning case becomes more obviously redundant — the reader already has the conceptual primitives, and the bee text alone can carry the illustrative work. Cut the reasoning case. ## The merged negative section, with the new §2 in place Merging §3 and §4 gives you about 2,000 words for the negative argument. With §2 doing more conceptual work, this is workable. A shape that hangs together: \*\*Setup (150 words).\*\* §2 has described what LLMs are. We now ask whether the natural ways of approaching them aesthetically — as persons or as designed tools — fit what they are. \*\*Against person-based aesthetics (700 words).\*\* Three compressed sub-moves: make-believe (Mallory and Cross together, ~250 words); concessive mindedness (Frankish, ~350 words); chat personae as recurring patterns rather than subjects (the §3.3 point in ~100 words). The first and third move can be one paragraph each; Frankish gets two. \*\*Against pure design aesthetics (900 words).\*\* Take the design-aesthetic view seriously as a starting position; identify the gap (the order in an LLM is grown, not imposed); use raku alone as the hybrid-craft analogy and let Pollock travel to §5.1 where it does its real work on the "forces" claim. Forsey and Parsons-and-Carlson get one sentence each rather than full setup paragraphs. \*\*Transition (250 words).\*\* The two negative arguments converge: what matters aesthetically is the emergent linguistic order. §5 develops an aesthetics that takes that order as its primary object. ## Revised section budget | Section | Current | Target | | --- | --- | --- | | Introduction | 363 | 300 | | §1 (Carlson's framework) | 1,405 | 1,000 | | §2 (What LLMs Are) | 1,468 | 700 | | Merged negative section | 4,532 | 2,000 | | Semiotic physics (was §5.1 + §5.2) | 2,434 | 1,400 | | Three levels (was §6) | 2,672 | 1,600 | | Conclusion | 212 | 200 | | \*\*Total\*\* | \*\*~13,100\*\* | \*\*~7,200\*\* | That gives 800 words of headroom against the 8,000 target. You will probably need some of it back when individual paragraphs resist compression. The §1 cut is the one I'm least confident about; if it stays at 1,200 instead of 1,000, the total is 7,400, still under target. ## Order of work Do these in sequence: \*\*1\\. Rewrite §2 first.\*\* Don't compress the existing version paragraph by paragraph. Write a fresh §2 organised around the four primitives, with the closing paragraph that previews the three scales. Aim for 700 words. \*\*2\\. Draft the merged negative section.\*\* Once §2 is settled, the negative argument can be written as a continuous piece rather than as a stitched-together §3 and §4. The make-believe and concessive-mindedness compressions are the only parts that need careful surgery; the design-aesthetic material has obvious slack once it isn't framed as a standalone section. \*\*3\\. Trim §5 and §6 last.\*\* With §2 doing more work, both sections need adjustment anyway. Easier to do this after §2 is settled than speculatively now. The §2 rewrite is the move that unlocks the rest. It changes what §5 and §6 have to do, which makes the cuts in §5 and §6 easier to identify and justify. If you want, I can take a first pass at the rewritten §2 — keeping the technical content faithful to your current draft, but reorganised around the four primitives and with the new closing paragraph. That gives you something concrete to react to before you draft the merged negative section. --- ## 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,\[^1\] 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).\[^2\], \[^3\] 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.\[^4\] 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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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. \[[https://doi.org/10.1093/jaac/article/81/4/454/7571331](https://doi.org/10.1093/jaac/article/81/4/454/7571331)\]([https://doi.org/10.1093/jaac/article/81/4/454/7571331](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. \[[https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/)\]([https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/)) \[^1\]: We will consider some non-paradigmatic artworks in Section 5. \[^2\]: Carlson stresses that ordinary descriptions of environments and more theoretical scientific, historical, and functional descriptions lie on a continuum, so that scientific and historical knowledge can deepen rather than displace practical familiarity as a basis for aesthetic appreciation (Carlson, 2000). By analogy, one might speculate that the sciences of mind and behaviour could relate to folk-psychological and biographical understanding in a similar way, so that in some cases empirical work on personality, emotion, or cognition might feed into the aesthetic appreciation of persons alongside the more everyday forms of knowledge stressed in the main text. \[^3\]: This personal appreciation also scales up to what we might call performance personalities. We respond to a comedian’s improvisational skill or an orator’s gravitas much as we respond to character in our friends, but now filtered through a public persona – genuine to the individual, yet artfully composed for performance. Recent scholarship has engaged with these performative dimensions, examining how performers construct and present public personas (Carroll 2013). Here too, Carlson’s knowledge requirement bites: to appreciate such personas we need to understand both the individual and the conventions of the performance context. \[^4\]: This way of distinguishing between an underlying generative system and the agent-like patterns it instantiates in particular episodes draws on work that treats GPT-style models as \*simulators\* of text worlds capable of generating agent-like \*simulacra\* without themselves being agents (Janus, 2022; Bereska et al., 2023). We do not use that terminology in the main text, but the present discussion adopts a similar two-level picture. I’ll treat Claude’s advice as a proposal about the paper’s argumentative architecture, not just as a word-count exercise. I’ll first check the uploaded draft against the suggested restructuring, then give a concrete 8,000-word plan and say where I think Claude is right or wrong. I’ve checked the uploaded draft against Claude’s diagnosis. The broad structural diagnosis is right, but the proposed 7,200-word target is too optimistic: it underestimates how much connective tissue has to be rebuilt once §3 and §4 are merged. Claude is right about the *direction*, but not quite right about the *best execution*. The paper should become an *Option B+* version: merge §§3–4 into one negative section, rewrite §2 from scratch so that it sets up the later argument, and then use that rewrite to simplify §§5–6. We should not simply cut one case study, trim §3.3, and compress §5.1. That would reduce words, but it would leave the deeper structural problem in place. I count the uploaded draft at roughly 13,080 body words, excluding references and the embedded image data. So an 8,000-word version needs about 5,000 words removed. That is not a trimming task; it is a restructuring task. The current draft announces a six-part structure: Carlson, LLMs, anti-person appreciation, anti-design appreciation, semiotic physics, and three levels of appreciation. For 8,000 words, that architecture is too spacious. My recommendation is this: | Part | Current | Target | | --- | --- | --- | | Introduction | c. 360 | 250–300 | | §1 Carlson framework | c. 1,400 | 850–950 | | §2 What LLMs are | c. 1,450 | 800–900 | | New §3: against person/design appreciation | c. 4,550 | 2,000–2,200 | | New §4: semiotic physics | c. 2,430 | 1,400–1,600 | | New §5: levels of appreciation | c. 2,675 | 1,800–2,000 | | Conclusion | c. 210 | 180–220 | That gives a realistic landing point of about 7,700–8,200 words, depending on how compressed the examples become. The main disagreement with Claude is that §3.3 is not merely redundant. It should not survive as a full subsection, but it does crucial work. It explains why “vibe,” “persona,” and “assistant-like behavior” do not amount to personhood. That point is needed later when model-level appreciation returns to “vibe.” So we should *relocate* §3.3, not simply cut it. Part of it belongs in the rewritten §2, where post-training is explained as shaping a recurrent response profile; part belongs in the merged negative section, where we say that such profiles are not lives, characters, or subjects. The second disagreement is that Claude misses a major repetition between §5.2 and §6.2. Both sections use practical interaction, prompting, and the farmer/geologist analogy to explain practical acquaintance with order. We should not keep both at full length. Fold §5.2 into the end of semiotic physics or into the chat-level discussion. That saves space while improving the structure. The third disagreement concerns the examples. Claude suggests cutting the reasoning-output case and keeping only the bee text. We should not do that entirely. The reasoning case should be reduced to a short paragraph, because it shows how the account applies to ordinary, boring, everyday LLM prose. The bee case can then do the more vivid work of showing order where the surface looks chaotic. If we cut the reasoning case completely, the account risks looking as though it only becomes aesthetically interesting in freakish edge cases. On §2, Claude is very right. The present §2 is a tutorial: tokens, probabilities, pre-training, embeddings, attention, autoregression, RLHF, product wrapper. It teaches the reader how LLMs work, but it does not yet organize the technical material around the later philosophical claims. The new §2 should instead be called something like *LLMs as trained text-propagation systems*. It should introduce only the technical primitives needed later: token-by-token continuation, learned probability landscapes, embeddings as semantic clustering, attention/context as dependency tracking, post-training as making certain response profiles easier to reach, and the difference between a single output, a chat, and a model. The new §2 should end by giving us the vocabulary for the rest of the paper: an output is one trajectory through the learned landscape; a chat is a path-dependent sequence of such trajectories; a model is the learned landscape itself. Once that is in place, §§5–6 no longer need to introduce those levels from scratch. For the merged negative section, the shape should be: First, person-based appreciation fails. We do not need separate long treatments of Mallory, Cross, Frankish, and post-training. We need one continuous argument: make-believe personification is useful but misclassifies the object; thin agency does not supply the temporally extended life needed for beauty-of-character appreciation; post-training produces recurrent assistant profiles, not subjects. Second, pure design appreciation fails. Here we keep the thought that LLMs are artifacts, but insist that ordinary design appreciation does not reach the aesthetically revealing order. The designers specify architecture, training objectives, and interfaces; they do not specify the particular regularities that emerge in generated text. This is where the bridge to order appreciation belongs. On the analogies, we should keep *Pollock* and probably cut *raku*. Pollock is better because it is Carlson’s own kind of case: an artifact whose appreciable order depends on forces not reducible to deliberate design. Raku is vivid, but it costs space and requires extra explanation. If we need it, it can go in a footnote. Pollock should appear once, not in both §4 and §5. For semiotic physics, the Janus/Picca/Wolfram material should be compressed. The current §5.1 spends too long establishing the literature before giving the paper’s own claim. We need one paragraph saying that existing work treats LLMs as systems that propagate text according to learned regularities; then we turn quickly to the philosophical contribution: semiotic physics supplies the right kind of knowledge for Carlsonian order appreciation. The mechanistic interpretability contrast should stay, but in compressed form. The present geology/chemical-physics analogy is good, but it takes too much space. The point is simple: mechanistic interpretability may explain internal computation, but aesthetic appreciation of outputs needs a level of description connected to perceivable textual features. That can be done in one paragraph. For §6, remove the tree/forest/biosphere analogy or reduce it to one sentence. It is clear enough once §2 has already introduced the three scales. Keep the three levels, but make each leaner. Outputs get the ordinary reasoning paragraph plus the bee case. Chats absorb the practical-acquaintance material. Models keep the “vibe” discussion but lose the video-game analogy, the benchmarking/safety contrast except perhaps one sentence, and the “mirror of culture” future-work paragraph. So the working plan should be: 1. Rewrite §2 first, not by compressing the current text but by replacing it with a more argumentative version. 2. Merge §§3–4 into one section, using §2’s new vocabulary. 3. Rewrite §5 only after §2 is fixed, because §5 should no longer need to repeat the technical setup. 4. Fold §5.2 into the chat discussion or into the end of §5. 5. Cut §6.3 hard: keep model-level “vibe” as characteristic semiotic order, cut the video-game analogy and future-work material. The version we want is therefore not Claude’s 8B exactly. It is closer to this: *Carlson gives us the distinction between design and order appreciation. LLMs are trained text-propagation systems whose organization is grown rather than directly specified. This rules out both person appreciation and simple design appreciation. What remains is order appreciation, guided by semiotic physics. That order can be appreciated in outputs, chats, and models.* That is the 8,000-word paper. The present draft has all the material for it, but the new version should be rebuilt around that spine rather than shortened paragraph by paragraph. "On §2, Claude is very right. The present §2 is a tutorial: tokens, probabilities, pre-training, embeddings, attention, autoregression, RLHF, product wrapper. It teaches the reader how LLMs work, but it does not yet organize the technical material around the later philosophical claims. The new §2 should instead be called something like LLMs as trained text-propagation systems. It should introduce only the technical primitives needed later: token-by-token continuation, learned probability landscapes, embeddings as semantic clustering, attention/context as dependency tracking, post-training as making certain response profiles easier to reach, and the difference between a single output, a chat, and a model. The new §2 should end by giving us the vocabulary for the rest of the paper: an output is one trajectory through the learned landscape; a chat is a path-dependent sequence of such trajectories; a model is the learned landscape itself. Once that is in place, §§5–6 no longer need to introduce those levels from scratch." this all sounds great. I agree with you about a lot of this. Something I struggled with when I drafted this originally was not sort of preempting semiotic physics. Okay. Okay, so I kinda wanted to give a neutral description which then different types of appreciation could be led over the top, so design or persona or environmental in the final case. And a slight concern with what you're saying here is I wonder if I could If if we did it the way you're suggesting here, would we be open to the criticism that we're sort of biasing everything in favour of ourselves? I'm really not sure, but yeah, this is definitely a a thought I've been having. That worry is real. The solution is not to make §2 blandly technical again, but to distinguish *neutrality of description* from *argumentative usefulness*: §2 can be organized around the facts later needed by all three candidate views without already naming those facts as “semiotic physics.” Yes, this is exactly the right worry. I would put it like this: §2 should be *neutral between modes of appreciation*, but it should not be *neutral between accurate and inaccurate descriptions of LLMs*. Those are different kinds of neutrality. The danger in my earlier formulation is that phrases like “text-propagation system,” “learned landscape,” and “trajectory through the landscape” already sound like the vocabulary of semiotic physics. They are not necessarily wrong, but if they appear too early, the reader may feel that the positive view has been smuggled into the supposedly neutral setup. That would weaken the dialectic, because §§3–4 would then look like a staged defeat of rivals rather than a genuine comparison. So I would slightly revise the plan. §2 should not yet say: *LLMs are text-propagation systems, and outputs/chats/models are trajectories through a learned landscape*. That is too close to the later positive account. Instead, §2 should give a *minimally committed technical description* that makes all three later options available. The section should say something like this: LLMs are designed artifacts: they have architectures, training objectives, post-training procedures, system prompts, interfaces, and product constraints. That gives design appreciation a real foothold. LLMs generate text token by token, by using learned statistical relations among tokens, contexts, and continuations. That gives the later semiotic-physics account its material, without yet describing those relations as “forces” or “order.” LLMs are encountered through chat interfaces in which post-training and product design produce relatively stable response profiles. That explains why person-like language is tempting, without yet deciding whether such profiles are persons, personae, quasi-agents, or merely patterns. The current draft already says that what users call “personality” or “vibe” is a stable pattern under post-training and product conditions rather than a separate inner subject; that is a good thought, but in §2 it should be presented more cautiously, so that the full anti-person argument still has work to do. Then §2 can end by saying that LLMs can be considered at three scales: single outputs, extended chats, and models. But again, the language should stay neutral. I would avoid “trajectory,” “landscape,” and perhaps even “text propagation” in the final paragraph of §2. Those can come later, when semiotic physics is introduced. In §2, use plainer terms: *generated output*, *extended exchange*, *trained model*. Then §5 can redescribe these as trajectories, environments, and grounds of order. So the ideal version of §2 is not “semiotic physics before semiotic physics.” It is a *constraint-setting section*. It gives the facts that any acceptable mode of appreciation has to respect. Those facts include design, post-training, product presentation, generated text, statistical learning, and scale. Then the next section can ask: given this object, should we appreciate it as person, artifact, or environment-like generative system? That also improves the fairness of the dialectic. The design theorist gets to say: look, these systems are clearly designed artifacts. The person-appreciation theorist gets to say: look, users encounter stable assistant-like profiles. The environmental/order theorist gets to say: look, the most revealing patterns are emergent regularities in generated text. §2 does not decide among these. It simply gives each view something to work with. I would therefore rename §2 less aggressively. Not *LLMs as trained text-propagation systems*. Better: *What LLMs Are* or *The Object of Appreciation: LLMs, Outputs, and Chats* or *LLMs as Trained Generative Systems* The last one is probably best. It is not as inert as “What LLMs Are,” but it does not yet commit us to semiotic physics. It says enough: these are trained systems that generate things. It does not yet say that their aesthetic appreciation should be modeled on the appreciation of order. A good final paragraph for §2 might say something like this: > This schematic description leaves open how LLMs should be aesthetically appreciated. They are artifacts, since their architectures, training regimes, interfaces, and deployments are designed. They also invite person-like responses, since post-training and product design produce stable assistant-like profiles across interactions. At the same time, what users encounter most directly are generated texts and extended exchanges whose patterns are not specified in advance by designers but arise from the trained system’s learned dispositions. The following sections ask which mode of appreciation best fits this combination of features. We will consider three possible objects of appreciation: individual outputs, extended chats, and the trained model itself. That paragraph is doing exactly what we need. It does not preempt semiotic physics. It creates the problem to which semiotic physics later becomes the answer. So the adjustment is: keep the *organizational* insight from Claude and from my earlier response, but change the rhetoric. §2 should be organized by the later argument, but it should not use the later argument’s evaluative vocabulary. The positive terms — *semiotic physics*, *forces*, *order*, *trajectory*, *aspection of language*, maybe even *learned landscape* — should be held back until §5. "What LLMs Are" this is the original title. we are keeping it. Please provide a bullet point plan of what you think section 2 should be, include all reelvant details, don't just allude to them. give me the argument, and the quotations where necessary. afterwards, explain how your plan for section 2, would effect and enhance sections 5 and 6. I’m going to make the plan quite concrete rather than giving another abstract rationale. The key design choice is that §2 should give each later view something to use: person, artifact, and order appreciation should all be live options at the end of it. Yes. We keep the title *What LLMs Are*. The section should not sound like it has already accepted semiotic physics. It should instead present LLMs as a mixed case: designed artifacts, trained generative systems, and chat products that invite person-like responses. The later sections then ask which mode of appreciation fits this mixed object. Here is the plan I would use. ## Plan for §2: What LLMs Are - *Aim of the section* - The section should begin by saying that Carlson’s framework requires us to identify the object before deciding how to appreciate it. So §2 gives only a schematic account of LLMs, but one pitched at the right level for the later aesthetic argument. - It should explicitly say that this description is not yet a theory of aesthetic appreciation. That will prevent the worry that we are smuggling in the semiotic physics view. - The key point should be: LLMs are not simple tools, not persons, and not natural environments; they are trained computational artifacts encountered through generated texts and chat interfaces. - Suggested argumentative opening: > Carlson’s recommendation that we appreciate things as what they are requires a schematic account of what LLMs are. The aim of this section is not to settle how they should be aesthetically appreciated, but to identify the features that any such account must respect. LLMs are designed artifacts; they are trained on large bodies of text; they generate outputs token by token; and, in ordinary use, they are encountered through chat interfaces that produce relatively stable assistant-like profiles. - This opening is better than the current one because the current §2 says it will explain “how they process text as numerical tokens, calculate probabilities through learned parameters, and generate responses through iterative sampling,” which makes the section sound like a technical primer rather than an argumentative setup. The current draft then moves into a long “The cat sat on the” example, with invented token IDs and probabilities. That should go or be reduced to two or three sentences. - *Paragraph 1: LLMs as artifacts* - Start with the fact that LLMs are artifacts. This gives design appreciation its foothold. - Say that models are built by research groups and companies; they have architectures, training objectives, data-selection practices, post-training regimes, system prompts, safety filters, interfaces, and deployment contexts. - But immediately add that this does not yet show that ordinary design appreciation is enough. It only establishes that design appreciation is a serious candidate. - The paragraph should not yet say “design appreciation fails because the real order is emergent.” That belongs later. - Argumentative function: this paragraph gives §4, or the design part of the merged negative section, something real to work with. We are not setting up a straw man. LLMs really are designed artifacts. - Suggested content: > Contemporary LLMs are artifacts. They are built by research groups and companies, trained under specified objectives, shaped by further post-training procedures, and embedded in products with system prompts, safety filters, memory policies, and user interfaces. Under one description, then, they are functional technologies designed to serve purposes such as answering questions, following instructions, producing text, and assisting users. This is why design appreciation is a natural candidate: one might ask how well the system’s architecture, training, and interface realize the function of a conversational assistant. - *Paragraph 2: token-level generation* - Then introduce tokenization and autoregression, but without the long cat example. - Explain that the system converts text into tokens, represents them computationally, estimates probabilities over possible next tokens, samples a token, adds it to the context, and repeats the process. - Mention temperature only if needed, and probably in one sentence or a footnote. It is not central to the aesthetic argument unless later we discuss loosened constraints or more unusual outputs. - Avoid “the model is rolling weighted dice, not making choices” in §2. That is too argumentative against person appreciation too early. We can say something more neutral: “The process is computational rather than deliberative.” - Argumentative function: this gives the person-appreciation section its constraint. Whatever one says about agency, the basic operation is token-by-token generation, not deliberative speech production. - Suggested content: > At the level of generation, an LLM works by converting input text into tokens and estimating, at each step, which tokens are likely to follow the current context. The selected token is then added to the context, and the calculation is repeated. The system therefore produces text sequentially, by repeatedly updating a context and selecting continuations from learned probability distributions. This does not by itself decide whether intentional descriptions are ever legitimate, but it does mean that the immediate process of generation is computational rather than deliberative. - This replaces both the “cat sat on the” example and the “capital of France” example. The current draft explains autoregressive decoding twice: once with “The cat sat on the” and again with “What is the capital of France?” We only need one short explanation. - *Paragraph 3: pre-training and learned regularities* - Explain that the model is not given hand-coded rules for grammar, meaning, politeness, or reasoning. It is trained to predict tokens across large corpora, and its parameters are adjusted across many examples. - Use “regularities” rather than “forces” or “laws.” This is crucial. “Regularities” is neutral; “forces” already belongs to semiotic physics. - Say that what is learned includes patterns of co-occurrence, style, genre, syntax, discourse structure, explanation, dialogue, refusal, and so on. This matters because later the paper will argue that many aesthetically salient features of outputs arise from such learned regularities. - But do not yet say that those regularities are the proper object of order appreciation. - Suggested content: > During pre-training, the model is exposed to very large bodies of text and adjusted so as to improve next-token prediction. The point is not that the system stores all possible continuations. Rather, training adjusts its parameters so that it becomes sensitive to regularities in the training distribution: which words and phrases tend to occur together, how syntactic dependencies are marked, how explanations are typically structured, how different registers unfold, and how conversational turns normally proceed. These regularities are learned from text rather than written into the system as explicit rules. - This preserves the core of the current draft’s discussion of pre-training but removes the “doctor/patient” micro-example unless we decide we need it. The current draft’s “doctor” example is clear, but in an 8,000-word paper it is probably expendable. - *Paragraph 4: embeddings as contextual organization* - Keep embeddings, but explain them in terms of contextual organization rather than as “meaning emerges.” - The current draft says: “This is how meaning emerges in the model: not from understanding concepts but from tracking which words appear in similar contexts.” That is useful but too compressed and possibly too controversial. It invites a semantics dispute we do not need. - Better: embeddings help explain why certain words, topics, registers, and associations cluster in generation. - This is needed because §5 later discusses vocabulary clustering and semantic attraction. But §2 should not yet call it “semantic attraction.” - Suggested content: > One result of training is that tokens are represented in ways that reflect their distributional roles. In embedding spaces, tokens that occur in similar contexts tend to be represented as closer to one another than tokens that occur in dissimilar contexts. This is not yet understanding in the ordinary human sense, but it is one way in which the system becomes sensitive to patterns of use. It also helps explain why generated text often develops clusters of related vocabulary and topic. - This is better than the current “cat sits near dog” explanation if we need to save words. But a short example could remain: > For example, “cat” and “dog” come to occupy nearby regions because they appear in many overlapping contexts. - I would include the example only if the paragraph otherwise feels too abstract. - *Paragraph 5: attention, context, and dependency* - Keep attention, but again do not over-teach it. - We need attention because later §6.2 concerns chats, context accumulation, coherence maintenance, and register stability across turns. - Explain that transformer models use attention mechanisms to weigh relations among tokens in the current context, allowing material from earlier in the prompt or conversation to shape later generation. - We do not need the full “The cat that chased the mouse sat on the mat” explanation, nor the discussion of attention heads specializing in pronouns, verbs, etc. - Suggested content: > Transformer models also use attention mechanisms that allow information from one part of the context to bear on what is generated elsewhere. This is why earlier material in a prompt or conversation can shape later continuation. The model does not simply respond to the last word or sentence; it operates over a structured context in which some prior tokens are more relevant than others for the next step of generation. This helps explain why extended exchanges can develop local coherence, recurrent vocabulary, and persistent register, but also why coherence can break down when earlier material becomes weakly represented or falls outside the effective context. - This paragraph is doing a lot. It sets up §6.2 directly but neutrally. It does not yet say chats are “environments” shaped by semiotic forces. It just says that extended exchanges have context-dependent structure. - *Paragraph 6: post-training and assistant-like profiles* - This paragraph is central. It should absorb the useful part of current §3.3, but in a neutral form. - Explain that most chat systems are not base models. They are post-trained through instruction tuning, preference learning, RLHF or related procedures, and then placed in product environments. - Say that this produces stable patterns: hedging, refusal, politeness, explanation, step-by-stepness, safety disclaimers, helpfulness, deference, directness, and so on. - But do not yet conclude that these are “not persons.” Instead say: this is why person-like language is tempting, and also why the relevant “profile” can be studied without assuming a subject behind it. - Suggested content: > Most systems encountered by users are not base models. They are further shaped by instruction tuning, preference learning, RLHF or related post-training procedures, and then embedded in products with system prompts, safety policies, and interfaces. These layers affect which responses are easy to elicit in ordinary use. They make some patterns more likely: helpful answers, explicit uncertainty, refusals, summaries, numbered explanations, conciliatory tone, and other features of assistant-like behavior. This is one reason users describe models as having a “personality” or “vibe.” Such language tracks something real: stable patterns in interaction. But §2 should leave open whether these patterns amount to character, design profile, or generated textual regularity. - This improves the current draft. At the moment, §2 says that “personality” or “vibe” is “not a separate mechanism or inner subject added on top of the predictive core.” That is true, but it already sounds like the conclusion of the anti-person section. We should make the point less verdict-like here. - *Paragraph 7: emergence and the designed/trained distinction* - Keep the “grown, not directly specified” idea, but make it less rhetorically heavy than in the current draft. - The Olah quote is useful, but the current block quote is far too long. We only need a short quotation, perhaps: “we don’t program them... we kind of grow them.” That is enough. The rest can be paraphrased. - Explain that designers specify architectures, objectives, datasets, procedures, and interfaces, but they do not directly specify the full organization that results. - This paragraph is the hinge between design and order appreciation. It makes §4 necessary, because it shows why design appreciation has a foothold but may not be sufficient. - Suggested content: > A final feature matters for the later argument. Designers specify architectures, objectives, datasets, training procedures, and deployment conditions, but they do not directly specify every regularity the trained system exhibits. Olah captures this by saying that “we don’t program them... we kind of grow them” (Olah 2024). The point is not that LLMs are biological organisms. It is that a trained model’s internal organization and output tendencies arise through optimization under constraints, rather than by direct rule-writing. This makes LLMs artifacts, but artifacts whose behavior is partly structured by learned organization that no designer has explicitly laid out. - This is neutral enough. It does not yet say that LLMs require order appreciation. But it shows why the design/order question arises. - *Paragraph 8: the three objects of later analysis* - End §2 by introducing the three levels: output, chat, model. - Use neutral vocabulary here. Do not yet say “outputs as specimens,” “chats as environments,” or “models as grounds of order.” Those are §6 formulations. - In §2, say only: - individual outputs are bounded generated texts; - chats are extended exchanges in which prior turns shape later turns; - models are trained systems whose response patterns can be compared across many outputs and chats. - Suggested final paragraph: > This description also distinguishes three possible objects of appreciation. We may attend to an individual output: a bounded generated text produced under particular prompt and system conditions. We may attend to an extended chat: a temporally structured exchange in which earlier turns shape later ones. Or we may attend to a model: the trained system whose characteristic patterns become visible across many outputs and chats. The following sections ask how these objects should be appreciated. Their artifact status supports design-based approaches; their assistant-like profiles explain the pull of person-based approaches; and their learned regularities will later motivate an order-based alternative. - This is probably the most important paragraph in the revised §2. It solves the later §6 problem without preempting semiotic physics. ## What should be cut from current §2 - Cut the long token-ID example. - We do not need invented IDs like 464, 3857, 4521, etc. - We do not need exact invented probabilities like 38%, 22%, 15%. - We do not need both “cat sat on the” and “capital of France.” - Cut or relocate “randomness, not creativity.” - It may be true, but it is too evaluative for §2. - It belongs, if anywhere, in the anti-person or anti-agency discussion. - Cut the detailed “doctor/patient” parameter-adjustment example unless the final section feels too abstract. - Cut most of the attention-head detail. - We only need attention as context-sensitive dependency tracking. - Cut the long Olah block quote. - Keep one short phrase and paraphrase the rest. - Rephrase the “personality/vibe” claim. - In §2, do not say too strongly that vibe is “not” an inner subject. - Say instead that user descriptions of “personality” and “vibe” track stable response patterns, and that the later person-appreciation section asks whether this is enough for person-based aesthetics. ## What quotations are needed - The only quotation I would keep in §2 is the short Olah phrase: > “we don’t program them... we kind of grow them” (Olah 2024) That quote gives vivid support to the designed/trained distinction. It also helps the later design section. The full current quote is too long and too metaphor-heavy for an 8,000-word version. - I would not quote Carlson again in §2. - Carlson has already been quoted in §1. - §2 can simply refer back to the recommendation that appreciation should fit what the object is. - I would not quote Wolfram, Janus, Picca, or the “semantic laws of motion” material in §2. - Those belong in §5. - Bringing them into §2 would make the section sound as though semiotic physics has already won. ## How this improves §5 The revised §2 would make §5 much cleaner. - §5 would no longer need to re-explain embeddings, attention, post-training, or generated outputs. - §2 will already have introduced: - token-by-token generation; - learned regularities; - distributional clustering; - context-sensitive dependency; - post-training as shaping assistant-like response profiles; - outputs, chats, and models as different objects of analysis. - §5 can therefore begin directly with the question: given the technical facts introduced in §2, what kind of knowledge makes the relevant patterns appreciable? - The mechanistic interpretability paragraph can be shorter. - The current §5 spends a lot of time explaining why mechanistic interpretability is not the right aesthetic level. It says that mechanistic interpretability studies “weight matrices, activation patterns, circuit-level features,” whereas appreciation of generated text needs concepts connected to perceivable features. - With the new §2, this contrast can be made in one paragraph because the reader already understands the difference between internal mechanisms and perceivable textual regularities. - The Janus/Picca/Wolfram literature review can be compressed. - At present, §5 introduces Janus, Picca, Kirchner/metasemi, Wolfram, “linguistic feature space,” “trajectories,” and “semantic laws of motion.” - Once §2 has already explained generation, learned regularities, embedding, context, and post-training, §5 can say: existing work gives us the idea of text continuation according to learned regularities; our contribution is to use this as the right kind of knowledge for Carlsonian appreciation. - That prevents §5 from becoming another technical primer. - §5 can introduce semiotic physics as a *redescription* of §2’s neutral facts. - This is the key payoff. - §2 says: LLMs learn regularities from text. - §5 says: when those regularities guide aspection, they function as semiotic physics. - §2 says: embeddings help explain clustering. - §5 says: this appears aesthetically as semantic attraction. - §2 says: attention/context allows earlier material to shape later generation. - §5 says: this appears aesthetically as contextual threading. - §2 says: post-training makes assistant-like patterns likely. - §5 says: this appears aesthetically as alignment pressure. That structure avoids preemption. The same facts are first introduced neutrally and later redescribed under the positive aesthetic theory. - §5.2 can be shortened or folded forward. - The current practical-acquaintance section repeats ideas that will also appear in §6.2: prompting, interaction, extended exchange, and the farmer/geologist analogy. - If §2 already introduces chats as extended context-sensitive exchanges, then §5.2 can be reduced to one paragraph at the end of §5: - theoretical semiotic physics and practical acquaintance are two ways of grasping the same regularities; - experienced users may know the patterns without being able to theorize them; - this practical knowledge will become salient in the discussion of chats. ## How this improves §6 The revised §2 also makes §6 more efficient and more coherent. - The general introduction to §6 can be much shorter. - The current §6 introduces outputs, chats, and models by analogy with tree, forest, and biosphere. - If §2 already introduces individual outputs, extended chats, and models as possible objects of appreciation, §6 no longer needs a full analogy to establish the levels. - §6 can begin: > Section 2 distinguished three objects of analysis: individual outputs, extended chats, and models. We can now show how semiotic physics guides appreciation at each level. - §6.1 becomes more focused. - The reasoning-output example can be compressed because §2 already explained post-training, assistant-like response profiles, and generated outputs. - We no longer need to explain from scratch why step-by-step structure, hedging, and summaries arise. We can simply say that these are post-training-visible features of ordinary assistant output. - This lets the bee-text case do the vivid work without making §6.1 enormous. - §6.2 becomes structurally stronger. - §2 will already have said that chats are extended exchanges in which earlier turns shape later ones. - §6.2 can then argue that this temporal and contextual structure makes chats especially apt for environmental/order appreciation. - The current §6.2 says that chats involve coherence maintenance, context accumulation, register dynamics, and mode stability. Those categories become much more natural if §2 has already introduced attention/context and post-training in the right way. - §6.2 can absorb the practical-acquaintance material. - This is probably the biggest structural improvement. - Instead of having §5.2 discuss practical acquaintance and §6.2 discuss chats, we can make §6.2 the place where practical acquaintance is developed. - Prompting is intervention; extended exchange is where the user learns the model’s tendencies; chats are where practical knowledge of the system accumulates. - This avoids duplication and makes the farmer/geologist analogy land in the place where it does the most work. - §6.3 becomes less defensive. - The current §6.3 has to explain what it means to appreciate the model rather than the outputs, then introduces “vibe,” then gives the video-game physics analogy, then distinguishes appreciation from benchmarking, capability evaluation, and safety testing, then flags the “mirror of culture” idea. - If §2 has already defined the model as the trained system whose characteristic patterns are visible across many outputs and chats, §6.3 can start directly with model-level appreciation. - It can say: model appreciation is appreciation of stable tendencies across possible and actual outputs. What users call “vibe” is the informal recognition of those tendencies. - The video-game analogy becomes unnecessary. - It is clever, but in an 8,000-word paper it is expendable. - The revised §2 already gives us the needed conceptual contrast: output, chat, model. - §6.3 can use that distinction instead of bringing in a new analogy. - The “mirror of culture” paragraph should be cut. - It is interesting, but it opens a new paper. - The revised §2/§5/§6 architecture will already be dense enough. ## The main benefit The main benefit is that §2 becomes a neutral hinge rather than a tutorial. It would no longer say, in effect, “Here is how LLMs work.” It would say: “Here are the features of LLMs that any aesthetic account must accommodate.” That gives the paper a cleaner dialectic: - §1: Carlson says appropriate appreciation depends on what the thing is and what kind of knowledge fits it. - §2: LLMs are designed, trained, text-generating, chat-embedded systems. - §3: person appreciation and design appreciation each capture part of this object, but neither captures the whole. - §4/§5: semiotic physics explains the learned regularities that become appreciable in generated text and interaction. - §6: those regularities can be appreciated at the level of outputs, chats, and models. That is much stronger than simply cutting the old §2 down. It also solves your preemption worry: §2 does not say that semiotic physics is right. It quietly gives us the facts that later make semiotic physics look like the best account. it seems to me the plan you just gave me doesn't factor in enough what's already been introduced in section one. Okay, so first of all the obvious another obvious reason to keep the title the way that I want it is it's echoing the Carlson phrase introduced in section one. So it's yeah, it's not very encouraging that you missed that also your second bullet point of the plan. Yeah, that's fine, at least the first sentence is, I guess the second sentence is as well. But again, the easiest way to do this is yeah to to draw the analogy with the the comparison with the the environment, right? Or with what Carlson says about this layer of the world and the environment. Okay, something that sort of different types of science can be put over the top of. So there's a lot of words to say you should be following Carlson more closely here in this part, rather than pretending it hasn't been described yet. Should the key point really be that? Because again we're trying to stay close to the Carlson here. Yeah, I'm not gonna say anymore. That's your that's your in that's my comments and you'll please push back if you think you that you should. But yeah that's my comment is you don't seem to be taking what what's been said about Carlson already properly into account when you're giving me this plan.Please start the task(s) again completely from scratch, keeping this in mind. You’re right to press this. I’ll restart from Carlson rather than from the LLM mechanics: §2 should echo the §1 principle that we first identify what the object is, while keeping open which body of knowledge will later guide appreciation. You are right. The previous plan treated §2 too much as an independent technical setup, whereas it should be written as the direct continuation of §1. The title *What LLMs Are* is not just descriptive. It echoes Carlson’s first recommendation: appropriate appreciation begins by taking the object as what it is. §2 should therefore answer the Carlsonian question, not merely introduce LLM mechanics. The relevant point from §1 is this: Carlson does not say that we first give a neutral physical description and then later add aesthetic interpretation. He says that appreciation is guided by taking the object under the right description and by bringing to bear the right kind of knowledge. In the natural case, the environment is not appreciated simply as “what is visually before us,” but as something whose order can be disclosed by geology, biology, ecology, and so on. The same object can be approached through different bodies of knowledge, but not all of those bodies of knowledge are equally apt for all appreciative purposes. Your draft already says this: “the same landscape might be illuminated by geology, botany, and ecology together,” and that such knowledge makes natural structures visible as “effects of identifiable processes.” So §2 should not pretend that LLMs have not yet been philosophically framed. It should say: given Carlson’s framework, what sort of object are we dealing with, and what are the levels at which different forms of knowledge might illuminate it? Here is the restarted plan. ## Plan for §2: What LLMs Are - *Opening move: §2 as a Carlsonian identification of the object* - Begin by explicitly tying the section title to Carlson’s phrase from §1. - The point is not: “Here is a technical primer on LLMs.” - The point is: “If appreciation depends on what the object is, then we need to say what LLMs are at the level relevant to appreciation.” - The opening should also say that §2 gives a *layered description*, not a final aesthetic verdict. That is the Carlsonian move: different kinds of knowledge can be brought to bear on the same thing, but the thing must first be identified under the right categories. - Suggested opening: > Carlson’s first recommendation is that we appreciate things as what they are. This section applies that recommendation to LLMs. The aim is not to provide a full technical account of their operation, but to identify the features of LLMs that any mode of aesthetic appreciation must respect. As with natural environments, where geology, biology, and ecology disclose different aspects of the same world, different bodies of knowledge can be brought to bear on LLMs. They can be described as designed artifacts, trained systems, text-generators, chat products, or apparent conversational partners. The question for the rest of the paper is which of these descriptions, and which associated body of knowledge, best guides aesthetic appreciation. - This is the right correction to my earlier proposal. It keeps neutrality, but not by avoiding Carlson. It keeps neutrality by making §2 a Carlsonian inventory of possible descriptions. - *Paragraph 1: LLMs as artifacts* - Start with design because §1 has already introduced design appreciation. - LLMs are built by companies and labs; their architectures, training objectives, datasets, post-training procedures, deployment contexts, interfaces, system prompts, safety filters, and update cycles are all products of design. - This matters because the design view should not be made artificially weak. It has an obvious basis in what LLMs are. - But the paragraph should not yet conclude that design appreciation is inadequate. It should only say that artifact status makes design appreciation a live candidate. - Suggested content: > The first description is straightforward. LLMs are artifacts. They are built by research groups and companies, trained under selected objectives, adjusted through post-training, and embedded in products with system prompts, safety filters, memory policies, and user interfaces. Under this description, they invite design appreciation: one might ask how well the architecture, training regime, and product interface realize the function of a conversational assistant or general-purpose text generator. - This directly picks up §1’s account of design appreciation: knowledge of ends, constraints, materials, and realization. - *Paragraph 2: LLMs as trained systems* - Move from artifact status to trained organization. - This is the key paragraph. It should say that LLMs are artifacts, but not artifacts whose relevant structure is specified in the way a chair, kettle, or bridge is specified. - Designers set up the architecture and training regime, but much of the organization arises through training. - This should be stated neutrally: not yet “therefore order appreciation,” but “therefore design knowledge alone may not exhaust what there is to understand.” - Suggested content: > But LLMs are not artifacts whose operative structure is simply specified in advance. Designers set architectures, objectives, datasets, and training procedures, but the trained system’s detailed organization arises through optimization across many examples. In this respect, LLMs occupy an intermediate position: they are designed, but what is produced by the design process is a system whose internal dispositions and output patterns are learned rather than directly written as rules. - This is where the Olah quotation belongs, but only briefly. - Keep: > “we don’t program them... we kind of grow them” (Olah 2024) - Cut the long block quotation. It is rhetorically useful, but too expensive. - The point should be: > Olah’s metaphor is useful only if kept under control: LLMs are not organisms, but their organization is produced through a process set in motion rather than directly assembled feature by feature. - *Paragraph 3: token-level generation, but only as much as needed* - Now explain tokenization and autoregression. - The old “cat sat on the” passage should be cut. It is too tutorial-like and pulls the reader away from Carlson. - We only need the fact that text is broken into tokens, possible continuations are assigned probabilities, one continuation is selected, and the context is updated. - This matters because it constrains person-based appreciation: the immediate process is not deliberative utterance. - But do not make the anti-person conclusion here. - Suggested content: > At the level of generation, an LLM converts text into tokens and produces further text by repeatedly estimating likely continuations. Each new token is added to the context, and the model recalculates what is likely to follow. This is why LLM outputs can have the form of coherent assertions, explanations, jokes, apologies, or refusals without being produced in the way human utterances are produced. The process is sequential and computational; whether it can also be described in intentional terms is a further question. - This is better because it gives Frankish/Mallory room later. It does not prematurely say “there is no agency here.” It says only what the process is. - *Paragraph 4: learned regularities as the analogue of environmental structure* - This is where the Carlson analogy should be explicit. - In the natural environment, science does not merely add information to a visual scene; it discloses the processes by which the scene has come to have its order. - For LLMs, the relevant analogue at this stage is not yet “semiotic physics” but *learned regularities*. - Use the phrase “learned regularities” rather than “forces,” “laws,” or “trajectories.” - Suggested content: > Because the model is trained on large bodies of text, its outputs reflect learned regularities in those bodies of text: patterns of co-occurrence, syntactic dependency, genre, register, argumentative form, conversational turn-taking, and explanation. This is the point at which the analogy with Carlson’s environmental case begins to matter. Just as a landscape can be understood through the processes that produce strata, erosion channels, and ecological patterns, an LLM can be understood through the processes that make some textual continuations, registers, and forms of response more likely than others. At this stage, however, this is only a description of the object. It does not yet settle which mode of appreciation is appropriate. - This is the paragraph my earlier plan failed to give you. It follows Carlson much more closely. - *Paragraph 5: embeddings and attention as two relevant forms of organization* - Keep embeddings and attention, but present them as two examples of learned organization. - Do not present them as separate tutorial topics. - The function of this paragraph is to show why LLM outputs are not random strings, and why later sections can talk about vocabulary clustering, coherence, register, and context. - Suggested content: > Two aspects of this learned organization are especially relevant later. First, tokens are represented in ways that reflect their distributional roles: words and word-parts that occur in similar contexts tend to be represented as related. This helps explain why generated text often develops clusters of vocabulary, topic, and register. Second, transformer models use attention mechanisms that allow parts of the current context to bear on later generation. This helps explain why an output can maintain a topic over several sentences, and why an extended chat can preserve, transform, or lose material introduced earlier. - This is enough. We do not need “cat near dog,” pronouns, attention heads, or “The cat that chased the mouse sat on the mat.” - *Paragraph 6: post-training and the invitation to person-like appreciation* - This should be written as part of the Carlsonian inventory of what LLMs are. - LLMs are not merely base models. They are encountered as chat-optimized systems inside interfaces. - This creates stable assistant-like profiles, which is why users talk about “personality” and “vibe.” - But again, §2 should not yet decide whether person appreciation is appropriate. - Suggested content: > The systems users ordinarily encounter are also post-trained and product-shaped. Instruction tuning, RLHF or related methods, system prompts, safety policies, and interface design make some forms of response easier to elicit than others. They favor helpful answers, refusals, hedging, summaries, apologies, numbered explanations, and a broadly assistant-like stance. This explains why users often describe models as having a “personality” or “vibe.” Such descriptions are not arbitrary: they register stable patterns in interaction. Whether those patterns amount to character, persona, thin agency, design style, or something else remains to be argued. - This is much better than simply saying “vibe is not an inner subject.” It lets the person-based view enter fairly. - *Paragraph 7: LLMs as multi-level objects of appreciation* - Here we should introduce outputs, chats, and models. - But again, do it in a Carlsonian way: these are possible *objects selected for appreciation*, not merely technical levels. - Carlson says the appreciator selects objects from the environment under a body of knowledge. Your §1 already emphasizes that order appreciation involves selecting objects and bringing the right story to bear. - So §2 should say that LLMs can be selected or framed at different levels. - Suggested content: > This also means that the object of appreciation is not fixed in advance. One may attend to an individual output, understood as a bounded generated text. One may attend to an extended chat, understood as a temporally developing interaction in which earlier turns shape later ones. Or one may attend to the model itself, understood as the trained system whose tendencies become visible across many outputs and chats. These are not three unrelated objects, but three levels at which the same kind of system can be approached. - This prepares §6 without smuggling in §6’s conclusions. - *Final paragraph: leave all three appreciative routes open* - End by explicitly connecting §2 to the following sections. - The final paragraph should say that the layered description gives support to three possible approaches: - artifact/design appreciation; - person/persona appreciation; - order/environmental appreciation. - The aim is to make the transition feel fair. - Suggested final paragraph: > We therefore have a layered object. LLMs are artifacts, and so design appreciation has an initial claim on them. They are encountered through assistant-like profiles, and so person-like appreciation is psychologically and philosophically tempting. They are also trained systems whose outputs and chats display learned regularities not directly specified by designers, and so they may call for a form of appreciation closer to Carlson’s order appreciation. The rest of the paper asks which of these approaches best fits what LLMs are. ## What this version fixes - It keeps the title *What LLMs Are* because that title is doing real Carlsonian work. - It does not treat §2 as a free-standing technical primer. - It does not pretend that the philosophical frame has not already been introduced. - It follows the structure of §1 more closely: identify the object, identify the possible forms of relevant knowledge, then ask which knowledge best guides appreciation. - It does not preempt semiotic physics because it stops at “learned regularities.” It does not yet use the later vocabulary of “forces,” “laws,” “trajectories,” “semantic attraction,” or “semiotic physics.” - It also makes the rivals stronger. The design theorist, the person-appreciation theorist, and the order-appreciation theorist each get a legitimate basis in the object as described. ## What this does to §5 This revised §2 would make §5 much more direct. At present, §5 has to do too many things: it reintroduces the technical facts, contrasts semiotic physics with mechanistic interpretability, introduces Janus/Picca/Wolfram, explains “forces,” explains perceivable textual regularities, and then gives the aspection-shift argument. The reason it feels heavy is that §2 has not yet provided a Carlsonian layered description of the object. With the revised §2 in place, §5 can begin like this: > Section 2 described LLMs as trained systems whose outputs reflect learned regularities in text. We can now ask what body of knowledge makes those regularities aesthetically appreciable. Our answer is semiotic physics. That is much cleaner. The revised §2 would also allow §5 to distinguish three levels of description more sharply: - mechanistic interpretability describes internal computational structure; - design knowledge describes artifact function, architecture, interface, and deployment; - semiotic physics describes learned regularities as they become manifest in generated text and interaction. This follows the Carlson model much more closely. In §1, different sciences can illuminate the same environment differently. In §5, different bodies of knowledge can illuminate the LLM differently. The argument for semiotic physics is not that it is the only true description, but that it is the description best suited to order appreciation of generated text and chat interaction. That also means §5 no longer needs to over-defend the “level” point. The level point has already been prepared in §2. Semiotic physics becomes the body of knowledge that discloses the relevant order, just as geology discloses certain orders in a landscape. ## What this does to §6 The revised §2 would also improve §6 because the three levels would already have been introduced as possible objects of appreciation. At present, §6 has to announce outputs, chats, and models and then explain them using the tree/forest/biosphere analogy. That analogy is doing work that §2 should already have done. Once §2 says that LLMs can be approached at the level of individual outputs, extended chats, and the trained model, §6 can simply return to those levels under the positive theory. The revised §6 would then proceed like this: - outputs are individual generated texts in which learned regularities appear under particular prompt and system conditions; - chats are temporally extended interactions in which earlier turns shape later ones, making practical acquaintance especially salient; - models are trained systems whose characteristic tendencies become visible across many outputs and chats. This means that §6 no longer has to feel like a new conceptual apparatus. It becomes the payoff of §2 plus §5. The biggest improvement concerns chats. If §2 already presents chats as temporally developing interactions, §6.2 can absorb much of the current §5.2 material on practical acquaintance. That is where the farmer/geologist analogy should probably do its work. Practical knowledge of an LLM is built through repeated interaction, just as practical knowledge of land is built through working it. That point belongs most naturally with chats, not in a separate section that risks repeating the later discussion. So the new structure would be: - §2: LLMs can be approached at the levels of outputs, chats, and models. - §5: semiotic physics is the body of knowledge that makes learned regularities appreciable. - §6: semiotic physics guides appreciation differently at each level. That is the clean version. And it is more Carlsonian than the previous plan, because the whole sequence turns on the relation between *what the thing is*, *which knowledge discloses its order*, and *which object of appreciation is selected*. "They can be described as designed artifacts, trained systems, text-generators, chat products, or apparent conversational partners. The question for the rest of the paper is which of these descriptions, and which associated body of knowledge, best guides aesthetic appreciation." this is not good. You're just adding a load of complications to what should be a fairly straightforward section. Okay, you've completely turned it upside down. I don't understand why you're doing this. Note again, Carlson doesn't do this. Okay, this is not the way Carlson's argument would proceed, is it? Okay. By sort of having to sort of set up things by saying it could be characterized like this and it could be characterized like that. No, this is a really bad way to go. Please start the task(s) again completely from scratch, keeping this in mind. You’re right. The section should not begin by multiplying possible descriptions. It should proceed much more simply: Carlson says appreciate things as what they are; §2 answers that question for LLMs, in a way that leaves the later appreciative argument to do its work. You are right. I was making §2 do the wrong kind of dialectical work. Carlson does not begin by saying “nature can be described as this, or this, or this, and now we must decide which description wins.” He begins from a straightforward identification of the object: nature is to be appreciated *as nature*, and then the relevant knowledge is the knowledge that discloses its order. Your §2 should do the same. It should not present a menu of possible descriptions. It should say, directly: *LLMs are trained computational artifacts that generate text from learned statistical regularities*. That is what they are. The later sections then draw out what follows for person appreciation, design appreciation, and order appreciation. So the section should be much simpler. ## Revised plan for §2: What LLMs Are - Start from the Carlsonian question. - The title should stay *What LLMs Are* because it is the direct application of Carlson’s recommendation: appreciate things as what they are. - The opening should not say, “LLMs can be described in several ways.” That is the wrong structure. - It should say: > Carlson’s recommendation requires us to ask what LLMs are. For present purposes, the answer can be schematic. LLMs are trained computational artifacts that generate text by producing probable continuations from learned patterns in large bodies of text. This section explains that description. - That is the spine of §2. Everything else fills it out. - First explain that LLMs are *artifacts*. - This should come early because it connects directly with §1’s design appreciation. - But do not turn it into an argument about “one possible description.” Just state the fact. - The paragraph should say that LLMs are made by people, embedded in engineered systems, and shaped by design choices. - Suggested content: > LLMs are artifacts. Their architectures are designed, their training data are selected or filtered, their training objectives are specified, and their deployment is shaped by system prompts, safety filters, interfaces, and product decisions. In this respect they differ from Carlson’s central cases of natural environments. They are not unmade items in the world; they are engineered systems. - This gives design appreciation its place without making the section into a debate. - Then immediately qualify the artifact point: they are artifacts whose organization is *trained*, not simply specified. - This is the key transition. - We do not say: “Here is another way to describe them.” - We say: “This is the kind of artifact they are.” - Suggested content: > But LLMs are not artifacts whose relevant behavior is simply written into them as a set of rules. Designers construct the architecture and set the conditions of training, but the detailed organization of the trained model arises through exposure to text and repeated adjustment of parameters. What results is not a program in which every significant behavior has been specified in advance, but a trained system with learned dispositions to continue text in some ways rather than others. - This follows Carlson more closely because it identifies the kind of object before asking what knowledge fits it. - Keep the Olah quote, but only in miniature. - The current block quote is far too long. - Keep only: > “we don’t program them... we kind of grow them” (Olah 2024) - Then immediately control the metaphor: > The point is not that LLMs are organisms, but that their operative organization is produced through training rather than directly assembled feature by feature. - That is enough. We do not need the “scaffold,” “light,” and “organism” material in §2. - Explain token generation, but remove the tutorial feel. - Cut the “The cat sat on the” example with invented token IDs and probabilities. - Cut the second example about “What is the capital of France?” - One compact paragraph is enough. - Suggested content: > At the level of generation, an LLM converts text into tokens and produces further text one token at a time. Given a context, the model assigns probabilities to possible continuations, selects a token, adds it to the context, and repeats the process. The result can look like an answer, an explanation, a refusal, a joke, or a confession, but the immediate generative process is sequential continuation from a learned probability distribution. - This gives later anti-person arguments what they need, but does not yet make the whole anti-person argument. - Explain training as the source of regularity. - This is the most Carlsonian part of the section. - The analogy is not “LLMs may be like environments.” The analogy is methodological: in Carlson, once we know what nature is, we understand its visible features in light of the processes that produced them. In §2, once we know what LLMs are, we understand their outputs in light of training. - Suggested content: > The central fact for the rest of the paper is that the model’s continuations reflect regularities learned from text. During pre-training, the system is exposed to large corpora and adjusted to improve next-token prediction. It thereby becomes sensitive to patterns of co-occurrence, syntax, genre, register, explanation, dialogue, and argumentative form. These patterns are not stored as explicit instructions about what to say. They are acquired as statistical regularities that affect what continuations become likely in a given context. - This is where §2 should begin to resemble Carlson’s treatment of environments. Not because we already call this “order appreciation,” but because we are identifying the processes that make the object the kind of object it is. - Explain embeddings and attention only as needed. - Do not give a technical mini-lecture. - Use them to explain why generated text has structure. - Suggested content: > Some of this organization can be described in terms of embeddings and attention. Embeddings place tokens in relation to other tokens on the basis of their use in similar contexts, which helps explain why generated text tends to cluster around topics, vocabularies, and registers. Attention mechanisms allow earlier parts of a context to bear on later generation, which helps explain how outputs can sustain themes, dependencies, and local coherence across stretches of text. - This is enough. The details about “cat” near “dog,” pronouns, attention heads, and long sentences should go. - Explain post-training as shaping the generated profile. - Again, do not present this as “another description.” It is just part of what contemporary LLMs are. - Suggested content: > Most LLMs encountered by users are not merely base models. They have been post-trained for instruction-following and conversational use. Through instruction tuning, RLHF or related procedures, and product-level constraints, the system is made more likely to produce helpful answers, hedged claims, refusals, summaries, apologies, step-by-step explanations, and other familiar features of assistant-style output. These patterns explain why users experience models as having a tone, style, or “vibe.” - I would avoid saying here that this “is not an inner subject.” That conclusion belongs in the person section. But we can say “assistant-style output” rather than “assistant personality.” - End by identifying the objects the rest of the paper will examine. - This should be short and direct. - No tree/forest/biosphere analogy here. - Suggested content: > These facts also fix the main objects of later analysis. A single output is a generated text produced from a prompt and context. A chat is an extended sequence in which earlier turns condition later ones. A model is the trained system whose tendencies become visible across outputs and chats. In each case, what we encounter is text shaped by design, training, post-training, and context. The following sections ask what kind of appreciation this permits. - This sets up §6 without preempting it. ## The argumentative structure of §2 The whole section should have one simple movement: 1. Carlson says we must appreciate things as what they are. 2. LLMs are trained computational artifacts. 3. They are artifacts because they are engineered. 4. They are trained because their detailed behavior arises from optimization over text, not direct rule-writing. 5. They generate text token by token from learned probabilities. 6. Their outputs display learned regularities of vocabulary, syntax, register, dialogue, and explanation. 7. Post-training shapes those regularities into assistant-style output. 8. Therefore, when later sections ask whether to appreciate LLMs as persons, designed artifacts, or objects of order appreciation, they must answer to this basic account of what LLMs are. That is much cleaner than my previous plan. It does not multiply descriptions. It gives one description, then later sections test what follows from it. ## What this changes in §§5–6 For §5, this means semiotic physics should not be introduced as an entirely new technical account. It should be introduced as the *right kind of appreciative knowledge* for the regularities already identified in §2. The current §5 begins by saying that §2 has described LLMs as token-based predictors trained on corpora and shaped by RLHF, and then asks what knowledge makes their patterns visible. That basic move is right. But with the revised §2, §5 can be shorter because it no longer needs to re-explain embeddings, attention, post-training, or regularities in output. It can simply say: §2 told us that LLMs generate text from learned regularities; §5 now argues that semiotic physics is the body of knowledge that makes those regularities aesthetically appreciable. For §6, this means the levels of appreciation can be introduced with less effort. §2 will already have said what outputs, chats, and models are. §6 can then say: now that semiotic physics has been introduced, we can see how it applies at each level. This removes the need for the tree/forest/biosphere analogy, or at least reduces it to a sentence. It also lets the chat section absorb the practical-acquaintance material, since chats are where repeated interaction reveals the trained system’s tendencies. The main gain is that the paper becomes more Carlsonian, not less. §1 gives the principle: appreciate things as what they are, in light of the knowledge appropriate to them. §2 says what LLMs are. §3/§4 ask whether person appreciation or design appreciation fits that object. §5 argues that semiotic physics is the relevant knowledge for appreciating its learned order. §6 shows how that appreciation works for outputs, chats, and models. "I need to respond, but I’m hitting a bit of a snag with citations. The results from msearch give citation markers, but without line numbers. Hmm, file\_search includes those, but it's unclear whether citations without line numbers are acceptable. I’ll try using mclick for some line-numbered context instead." please stop wasting tokens thinking about fucking citations and such other trivia. does this really seem like something I want us to focus on right now? "First explain that LLMs are artifacts." Nope! not without you justifyying why this is better than alrternatives. Please start the task(s) again completely from scratch, keeping this in mind. Yes, fair. The previous version still moved too quickly from “What LLMs are” to “LLMs are artifacts,” without justifying why that should be the first explanatory emphasis. That is not harmless, because the ordering itself can bias the later argument. If §2 begins with artifacts, the reader may think the design-aesthetic route is the default and that order appreciation is an exception. If §2 begins with text generation, the reader may think semiotic physics is already being smuggled in. If §2 begins with chat personae, the paper gives too much ground to the person-based view. So the section should not begin with any one later option. It should begin with the *minimal object Carlson requires us to identify*: LLMs as systems whose relevant aesthetic presentation is mediated by generated linguistic behavior. Then, only after that, we distinguish the features that make them designed, trained, and chat-facing. Here is the better structure. ## Section 2: What LLMs Are ### 1\. Begin from Carlson’s demand, not from artifacts The opening should say that §1 gives us the question: if appropriate appreciation depends on what something is, then we need to know what LLMs are. But the answer must be pitched at the right level. We do not need a full computer-science account. We need the level at which LLMs become candidates for aesthetic appreciation. The opening claim should be: > LLMs are computational systems that generate linguistic outputs from learned regularities in text. That is better than starting with “LLMs are artifacts,” because it identifies the object at the level at which it enters the paper: not as a corporate product, not as a quasi-person, not yet as an environment, but as a system whose outputs and interactions are linguistically presented. Then we can say: they are *also* artifacts, but that is a further fact about how such systems come to exist. ### 2\. Explain generation first, but not as a tutorial This should be short. We need only the core point: LLMs generate text sequentially, token by token, by estimating continuations from a learned distribution. No invented token IDs. No “cat sat on the” worked example. No “capital of France” example. No long explanation of temperature. The paragraph should say: > At the point of use, an LLM receives a context and generates text by producing one token after another. Each new token is added to the context, and the system recalculates what is likely to follow. The output may look like an answer, an explanation, a refusal, a joke, or a confession, but the immediate operation is sequential text generation from learned probabilities. This gives us the basic object without yet deciding whether the output should be treated as speech, artifact behavior, or semiotic order. ### 3\. Then explain training as the source of regularity This is the Carlsonian bridge. In Carlson, what we see in the natural environment becomes intelligible when we understand the processes that produced it. Here, what we read in LLM output becomes intelligible when we understand training as the process that makes certain continuations likely. So the next paragraph should say: > These probabilities are not assigned by hand. During training, the system is exposed to large bodies of text and adjusted so as to improve next-token prediction. It thereby becomes sensitive to regularities in text: patterns of vocabulary, syntax, genre, register, explanation, dialogue, argument, and conversational turn-taking. These regularities are not explicit rules written by programmers. They are acquired through training. This is neutral in the right way. It does not yet say “semiotic physics.” But it gives the later account its ground. ### 4\. Only now introduce artifact status This is where “LLMs are artifacts” belongs. Not first. After generation and training. The justification is this: we first identify the kind of operation through which LLMs become aesthetically salient — generated linguistic behavior. Then we explain the conditions under which that operation exists: design, architecture, training, post-training, interface. The paragraph should say: > LLMs are therefore artifacts, but artifacts of a particular kind. Designers specify architectures, training objectives, datasets, filtering procedures, post-training methods, and interfaces. Yet they do not specify in advance every regularity the trained system will display. What is designed is a system capable of acquiring dispositions through training. This avoids treating artifact status as the whole answer. It gives design appreciation a legitimate foothold while already showing why design may not exhaust the case. ### 5\. Use Olah only to mark the designed/trained distinction The Olah quote should be very short. The current long quotation costs too much and makes the metaphor do too much work. Use only: > “we don’t program them... we kind of grow them” (Olah 2024) Then immediately clarify: > The point is not that LLMs are organisms. It is that their detailed organization is produced through training rather than directly specified feature by feature. That is enough. ### 6\. Explain embeddings and attention as forms of learned organization This should not become a technical primer. It should explain why generated text has patterns readers can notice. The paragraph should say: > Two features of this learned organization will matter later. Embeddings represent tokens in relation to other tokens on the basis of patterns of use, helping to explain why generated text clusters around topics, vocabularies, and registers. Attention mechanisms allow earlier parts of a context to bear on later generation, helping to explain how outputs sustain dependencies, themes, and local coherence across stretches of text. This prepares §5 and §6 without naming semiotic physics. ### 7\. Explain post-training as shaping ordinary chat behavior This should come after the base model story. It explains why users experience models as having tone, style, and “vibe.” The paragraph should say: > Most systems encountered by users are not base models. They have been post-trained for instruction-following and conversational use, through instruction tuning, RLHF or related methods, and product-level constraints. These processes make some patterns easier to elicit: helpful answers, refusals, hedging, summaries, apologies, numbered explanations, and assistant-like forms of address. This helps explain why users describe models as having a tone, style, or “vibe.” Do not add here: “but this is not a person.” That belongs in the next section. ### 8\. End with outputs, chats, and models This should be brief and non-metaphorical. > The relevant objects of later analysis can now be distinguished. A single output is a generated text produced from a particular context. A chat is an extended sequence in which earlier turns condition later ones. A model is the trained system whose tendencies become visible across many such outputs and chats. In all three cases, what we encounter is linguistic behavior shaped by training, design, post-training, and context. This sets up §6 without preempting it. ## Why this order is better The order should be: 1. Carlson requires us to ask what LLMs are. 2. LLMs are systems that generate linguistic outputs. 3. Their outputs arise from learned regularities in text. 4. They are artifacts because the systems that learn these regularities are designed. 5. Their detailed behavior is trained rather than simply specified. 6. Embeddings and attention explain some of the relevant structure. 7. Post-training explains assistant-style behavior. 8. Outputs, chats, and models are the later objects of appreciation. That order is better than starting with artifacts because it does not let design appreciation set the terms. It is also better than starting with “text-propagation” or “semiotic physics” language because it does not make the positive view look pre-decided. And it is better than starting with “vibe” or “assistant personae” because it does not let person appreciation dominate the setup. The section’s basic claim should be: > LLMs are designed computational systems trained to generate linguistic outputs from learned regularities in text. That is the clean answer to *What LLMs Are*. Everything else in §2 unpacks that claim. I gave your plan to claude, he definkyy gets somethings wrong, like seeming to think that the artifactuality should be previewed very early on when actually it shouldn't be mentioned yet at all, being one. give a thorough analysis of the rest of his many points. I've now extracted the paper to \[\_paper\_formatted.txt\](Attachments/\_paper\_formatted.txt) and read it in full. Here is the plan + analysis grounded in the actual prose. ## The final ChatGPT plan, in detail Title kept: \*What LLMs Are\*. Spine sentence: > "LLMs are computational systems that generate linguistic outputs from learned regularities in text." Eight paragraphs, in this order: - §2.1 — Begin from Carlson's demand - State the spine sentence; flag that "they are also artifacts" is a further fact, not the lead. - §2.2 — Generation, briefly - Tokenisation + sequential continuation only. - Cuts: cat-sat-on-the, capital-of-France, temperature material. - Suggested prose: > "At the point of use, an LLM receives a context and generates text by producing one token after another… the immediate operation is sequential text generation from learned probabilities." - §2.3 — Training as the source of regularity (the "Carlsonian bridge") - Use "regularities" — never "forces," "laws," or "trajectories." - §2.4 — Artifact status, \*after\* generation and training - Designers specify architecture/objectives/datasets/post-training/interface; not every regularity. - §2.5 — Olah, in miniature - Keep only "we don't program them... we kind of grow them" (Olah 2024); discipline the metaphor. - §2.6 — Embeddings and attention as forms of learned organisation - Embeddings → vocabulary/topic/register clustering; attention → coherence and dependency. - §2.7 — Post-training and assistant-style behaviour - Stable patterns users register as "tone, style, or 'vibe'"; no verdict on persons. - §2.8 — End on outputs, chats, models - Direct, no tree/forest/biosphere. Justifications the plan gives for the ordering: artifact-first would cue design appreciation as default; "text-propagation" / "trajectory" would smuggle semiotic physics; persona-first would over-cede to person view. Cascade into §§5–6 (one short paragraph each in the plan): - §5 opens directly: "§2 told us LLMs generate text from learned regularities; semiotic physics is the body of knowledge that makes those regularities aesthetically appreciable." - §6 inherits outputs/chats/models from §2.8; tree/forest/biosphere goes (or shrinks); §6.2 absorbs §5.2's practical-acquaintance material. ## What the plan gets right - The diagnosis matches the actual §2. Current §2 walks the reader through tokens (with placeholder IDs 464, 3857, 4521), then probabilities (38%, 22%, 15%), then a temperature aside, then "The cat sat on the mat" being built up token-by-token, then pre-training with the doctor/patient mini-example, then embeddings ("'Cat' sits near 'dog' because both appear after 'the'"), then attention with "The cat that chased the mouse sat on the mat" plus attention heads tracking pronouns, then the capital-of-France worked example for autoregressive decoding, then RLHF, then chat product wrapping, then a long Olah block quote. That is a tutorial, in exactly the order a CS primer would use. - Three claims currently inside §2 are verdicts that belong elsewhere, and the plan is right to want them cut from §2: - "But it is randomness, not creativity. The model is rolling weighted dice, not making choices." — anti-person claim, belongs in §3. - "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." — semantic verdict, anticipates §3. - "What users describe as a model's 'personality' or 'vibe' is a stable pattern in its responses under this post-training and product regime, not a separate mechanism or inner subject added on top of the predictive core." — this is the §3.3 thesis, repeated in §2. - Holding back the lexical signals of semiotic physics is the right answer to your pre-emption worry. Current §2 already says "as we shall see, it has consequences for what kind of appreciation is appropriate" — i.e., it pre-empts. The plan's "regularities" / no "forces" rule directly addresses this. - Cutting the Olah block quote (~80 words) to one short phrase is right: the long quote is doing rhetorical, not argumentative, work, and §1 has already established the distinction between making-to-a-plan and not-making-to-a-plan. - Putting outputs/chats/models on the table at the end of §2 is the move that lets §6's intro paragraph (the tree/forest/biosphere one) shrink to a sentence. That intro currently spends 170 words doing what §2's last paragraph could do in 50. - Title preserved. The current §2 opens "Carlson recommends we appreciate things for what they are. So what are LLMs?" — the title is doing real work and the plan respects it. ## What the plan gets wrong, or misses This is what I think is most worth your attention. - §1 already does the Carlsonian methodological setup. The plan's §2.1 re-states it. - §1 says: "Different sorts of thing, Carlson says, require different modes of appreciation." It distinguishes design appreciation from order appreciation. It says: "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)." - Your push to "follow Carlson more closely" was pointing here. §2 should \*answer\* §1 directly — given §1's apparatus, what are LLMs? — rather than re-installing the Carlsonian frame. - The plan's §2.1 does the latter: "Carlson's first recommendation requires us to appreciate things as what they are. This section applies that recommendation to LLMs." That is a re-statement, not a continuation. - "Generation → training → artifact" is still a feature-list, not a Carlsonian movement. - Carlson's actual structure in §1 (which you wrote into the paper): identify the kind of object → identify the kind of knowledge that discloses its order → guide aspection. The plan rearranges §2's features but doesn't replicate this movement. It defines and unpacks. - There is a version that does replicate it: identify LLMs as one kind of object (trained text-generators sitting on a designed scaffold), and signal that — as in §1's geology/botany/ecology — different bodies of knowledge can be brought to bear on this object: design knowledge, mechanistic interpretability, semiotic physics, even person-talk. §2 doesn't decide which knowledge fits the aesthetic question; later sections do. This honours Carlson's logic without the menu-of-descriptions move you killed. - The plan absorbs §3.3's \*content\* into §2.7 and silently drops §3.3's \*argumentative work\*. - §3.3 currently does load-bearing work: it says vibe and persona are "recurrent patterns in how the model tends to respond when prompted and constrained in certain ways" — "not a unified character with a life and projects." That claim is what §6.3 returns to: "What they are responding to, we suggest, is not a character in the person-aesthetic sense but a characteristic semiotic order." - If §2.7 stops at "users register stable patterns as 'tone, style, or vibe'" (no verdict on persons) and the merged negative section doesn't pick up the "not a subject" argument, then §6.3's appeal to vibe-as-semiotic-order has no set-up. The plan needs to say where the §3.3 argument lands. - The plan does not address §4's Pollock/raku material. - Current §4 makes the move that Pollock and raku show "the maker creates conditions and then yields" — Carlson himself uses Pollock as an order-appreciation example in art. This is a key bridge to semiotic physics, not a stylistic flourish. Earlier ChatGPT iterations correctly proposed Pollock travels to §5.1 (where it does its real work on the "forces" claim) and raku gets cut or footnoted. The final plan loses this entirely. - Without a decision on Pollock, the merged negative section either leaves the bridge unbuilt or has to rebuild it. - The plan has no word budget. - Current §2 is ~1,470 words. Earlier targets: Opus 700; ChatGPT-1 800–900. Eight paragraphs × ~100 words = 800; × ~150 = 1,200. Without a target you can't tell whether the eight-paragraph structure is the right shape. - The original 8K cut programme requires §2 around 700–900 to hit the budget given the merged negative section absorbs ~2,000 and §§5–6 trim to ~3,000–3,400. - The §§5–6 cascade is one paragraph each, far thinner than earlier passes. - Earlier passes had concrete decisions: §5.1's Janus/Picca/Wolfram material to one paragraph; mechanistic-interpretability detour shortened (the chemistry/geology paragraph); §5.2 absorbed into §6.2; the video-game / \*GTA V\* / \*Dark Souls\* / \*Breath of the Wild\* analogy in §6.3 cut; the "mirror of culture" paragraph cut; the reasoning-output case in §6.1 either kept-but-compressed (ChatGPT-1) or cut (Opus). The final plan lets all of these go. - If §2 is the lever, the cascade decisions are where the lever's effect is realised. Without them you don't yet have a 8K cut programme — only a §2 plan. - The plan ignores §5.1's existing opening. §5.1 currently begins "Section 2 described what LLMs are: token-based predictors trained on large text corpora and shaped by RLHF." That sentence has to be rewritten under any new §2; the plan doesn't say how. - One bias the plan admits, one it doesn't. - Admitted: opening with "artifact" would default the reader into design appreciation. - Not admitted: opening with "generation from learned regularities" defaults the reader into the trained-system view, which is the order-appreciation view's natural home. The plan's spine sentence inherits this bias. A truly neutral §2 might need to do less heavy lifting in its first move. ## The unresolved tension I'd want you to decide on before iterating - "Follow Carlson's logic more closely" vs. "don't multiply descriptions" — these are only in conflict if you read Carlson as saying "is this nature, art, or person?". He doesn't. He says: identify the kind of object (one identification), and acknowledge that different sciences disclose its order. The §2 analogue is one identification of LLMs + a brief acknowledgment that several knowledge frames can address this kind of object, with the question of which frame fits the aesthetic question deferred. That preserves Carlson's logic without re-opening the menu-of-objects move you rejected. The final plan resolved the tension by doing neither. ## Questions - Do you want me to fold the cascade into §§3+4 / §5 / §6 into the §2 plan before any drafting, so we have a complete 8K programme to react to? Or would you rather lock §2 first and handle the cascade after? - The final plan's §2.1 re-states §1's Carlsonian frame. If we instead have §2 \*answer\* §1 directly (presupposing the apparatus rather than re-installing it), do you want that answer to mention all three later candidate appreciations explicitly, or just identify the object and let §3+ raise the candidates? - Do you want §3.3's "vibe is recurrent pattern, not subject" claim to (a) live inside the merged negative section, or (b) be partly seeded in §2's post-training paragraph despite the verdict-in-§2 worry? \*La sezione due, qui, è il fulcro di tutto: cambiare lì significa cambiare ovunque.\* Claude’s analysis is useful, but it partly repeats the mistake we were trying to avoid. It sees that §2 should not be a CS tutorial, and it sees that the section has to connect to Carlson. But it keeps trying to make §2 manage the later dialectic: design, person, semiotic physics, mechanistic interpretability, and so on. That is the wrong job for §2. The job of §2 should be narrower: *Given Carlson’s demand that we appreciate things as what they are, what are LLMs, at the level relevant to the rest of the paper?* The answer should be direct: LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. That is all §2 needs as its spine. It should not yet say “artifact.” It should not yet say “design.” It should not yet say “semiotic physics.” It should not yet set up a list of possible descriptions. It should describe the thing whose appreciation is later at issue. ### 1\. Claude is right that the current §2 is a tutorial This is the strongest part of Claude’s analysis. The present §2 spends too much space on token IDs, invented probabilities, temperature, “The cat sat on the,” “What is the capital of France?,” embeddings, attention heads, RLHF, product wrapping, and the long Olah quote. That order makes sense for explaining LLMs to a novice. It does not make sense for this paper. The section currently has the rhythm of: here are tokens; here are probabilities; here is training; here are embeddings; here is attention; here is decoding; here is post-training. Claude is right that this is pedagogical rather than argumentative. For the paper, we only need the features that matter for later appreciation: sequential generation, training on text, learned regularities, contextual dependence, post-training, and the difference between output, chat, and model. So yes: most of the micro-examples should go. The cat example goes. The capital of France example goes. The temperature aside goes. The “doctor/patient” parameter story probably goes. Attention heads probably go. We need the architecture of the point, not the tutorial machinery. ### 2\. Claude is right that several verdicts now in §2 belong later This is also right. The current §2 sometimes stops describing and starts arguing. These claims should not be in §2: “The model is rolling weighted dice, not making choices.” “It does not learn that doctors treat patients or that cats are animals.” “What users describe as a model’s ‘personality’ or ‘vibe’ is a stable pattern... not a separate mechanism or inner subject.” All three may be defensible, but they are not equally placed. The first is anti-agency rhetoric. The second opens a semantics issue. The third is basically the §3.3 thesis. In §2, we should not yet settle the person-appreciation question. We should only give the account that later constrains it. The right §2 version should say something more restrained: the system generates text through learned continuations; post-training makes some response profiles more likely; users often experience those profiles as tone, style, or “vibe.” Full stop. The “not a subject” conclusion belongs in the merged negative section. ### 3\. Claude is right about holding back semiotic-physics vocabulary This is a major point. §2 should use “regularities,” not “forces,” “laws,” “trajectories,” “semantic attraction,” “modal inertia,” or “semiotic physics.” The reason is not just stylistic. It is dialectical. If §2 already describes outputs as trajectories through a learned landscape, then §5 looks preloaded. The positive account begins to sound as though it has been assumed rather than earned. “Regularities” is neutral enough. It tells us what training produces without yet telling us how to appreciate it. So the §2 vocabulary should be something like: “learned regularities” “patterns in text” “probable continuations” “context” “generated output” “extended exchange” “trained model” Then §5 can redescribe these regularities as the subject matter of semiotic physics. ### 4\. Claude is wrong that artifactuality should be introduced in §2 This is the largest error. Claude notices one bias — that starting with artifacts cues design appreciation — but then still wants artifactuality introduced in §2, just later. I now think that is wrong. The word “artifact” should not appear in §2 at all, or at least not as a category. §2 should not say, “LLMs are artifacts, but...” That formulation already starts the design section too early. It makes §4 feel like a continuation of §2 rather than a later candidate mode of appreciation. The better move is to describe the training process without classifying it aesthetically. We can say that LLMs are trained systems. We can say that training, post-training, system prompts, and deployment conditions shape their outputs. But we should not yet put that under the heading of artifactuality. The design section can later say: given the facts just described, one natural thought is that LLMs are artifacts and should be appreciated through design. That is where artifactuality enters. This also means Olah probably should not be in §2. Earlier I suggested keeping a short Olah quote there, but if the section is to avoid artifactuality, Olah belongs later. The “we don’t program them... we kind of grow them” line is useful exactly for the design/order hinge. It is not needed for the neutral answer to “What LLMs Are.” It should move to the design-appreciation discussion, where it helps explain why the designed-artifact model does not exhaust the case. ### 5\. Claude is half right about §1 already doing the Carlsonian setup Claude says §2.1 should not reinstall §1’s frame. That is right. §1 has already done the work. It has explained Carlson’s distinction between design appreciation and order appreciation, and it has already stressed that the relevant body of knowledge makes the object’s order visible. But Claude’s proposed remedy is wrong. He suggests that §2 should acknowledge several possible knowledge frames — design knowledge, mechanistic interpretability, semiotic physics, person-talk. That returns us to the “menu of descriptions” problem. It makes §2 sound like: here are several descriptions; later we choose one. That is not what we want. The better remedy is simpler: §2 should presuppose §1 and answer its question. It needs only a light opening, not a methodological paragraph. Something like: > Carlson’s first recommendation requires us to ask what LLMs are. For present purposes, the answer can be schematic: LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. Then move straight into the account. No menu. No “they can be described as...” No “different bodies of knowledge can be brought to bear...” That belongs later. ### 6\. Claude is wrong that “generation → training → artifact” is the right movement Claude says this remains a feature-list rather than a Carlsonian movement. There is something right in the complaint, but the proposed correction is bad. The problem is not that the section needs more explicit Carlson. The problem is that the ordering has to follow the object as encountered in the paper. The order should be: 1. LLMs generate linguistic outputs. 2. They do so sequentially from a context. 3. Their dispositions to continue in some ways rather than others are acquired through training. 4. This training produces regularities in vocabulary, syntax, register, explanation, dialogue, and so on. 5. Embeddings and attention are two relevant forms of organization. 6. Post-training shapes ordinary chat behavior. 7. Outputs, chats, and models are the three levels later discussed. That is not just a feature-list. It is the minimal technical answer to the Carlsonian question. It gives us what the thing is, without yet deciding which appreciative framework follows. The artifact point should not be step 4. It should be absent from §2 and introduced in the design section. ### 7\. Claude is right that §3.3’s argumentative work must be preserved This is a very good point. The current §3.3 is too long for an 8,000-word version, but it is not dispensable. It performs a specific task: it explains why post-training and chat interfaces generate something that feels like a persona without generating a subject with a life, projects, and character. That argument should not be absorbed into §2, because then §2 becomes anti-person too early. But it also cannot vanish, because §6.3 later relies on it when it says that “vibe” should be understood as characteristic semiotic order rather than character. So the correct relocation is: §2 says: post-training produces stable response profiles that users experience as tone, style, or “vibe.” Merged negative section says: those profiles do not amount to personhood, character, or beauty-of-character; they are recurring patterns in response under constraints. §6.3 says: the same phenomenon can be reinterpreted positively as model-level semiotic order. That gives “vibe” a clean three-stage development: introduced neutrally, rejected as personhood, recovered as semiotic order. ### 8\. Claude is right that Pollock/raku cannot be ignored Claude is right that my final §2 plan did not say enough about the cascade into §4/§5, especially Pollock and raku. But the conclusion should not be that §2 needs Pollock. It should not. Pollock belongs at the hinge between design appreciation and semiotic physics. Raku is probably expendable. In an 8,000-word version, we do not need both. Raku is vivid, but it requires extra explanation and does not have Carlson’s authority behind it. Pollock is better because Carlson already uses Pollock-type cases to show how appreciation of made things can involve attention to forces and material processes. So the revised structure should be: In the merged negative section, after explaining why simple design appreciation is insufficient, use Pollock as the analogy. The point is not that LLMs are artworks like Pollocks. The point is that even within made things, appreciation can require attention to the processes that produce order beyond direct design specification. That prepares §5’s talk of semiotic physics. Raku can go, or become a footnote if we really want a non-art craft case. But I would cut it. ### 9\. Claude is right that the plan needed a word budget Yes. The revised §2 should not be eight substantial paragraphs. That is too much. Target: *700–800 words.* A realistic version: Paragraph 1, 70–90 words: answer the Carlsonian question. Paragraph 2, 100–120 words: sequential generation from context. Paragraph 3, 130–150 words: training and learned regularities. Paragraph 4, 120–140 words: embeddings and attention. Paragraph 5, 120–140 words: post-training and ordinary chat behavior. Paragraph 6, 100–120 words: outputs, chats, models. That is enough. No artifacts. No Olah. No long examples. No named interlocutors. No semiotic physics. ### 10\. Claude is right that the §§5–6 cascade needs to be explicit The cascade should be this. For §5, the opening must be rewritten. The current opening says: “Section 2 described what LLMs are: token-based predictors trained on large text corpora and shaped by RLHF.” That will no longer fit. The new opening should be: > Section 2 characterized LLMs as systems trained on large bodies of text to generate linguistic continuations from a context. We can now ask what kind of knowledge makes the regularities in those continuations aesthetically appreciable. Then introduce semiotic physics. The mechanistic interpretability discussion should shrink. The geology/chemical physics analogy is useful, but the point can be made once: mechanistic interpretability may explain internal mechanisms, but the appreciative question concerns perceivable regularities in generated text. The Janus/Picca/Wolfram material should become one compressed paragraph. Janus and Picca can be used to establish that others have already treated LLMs as systems of text continuation and sign-transition. Wolfram can either be paraphrased or quoted briefly. Do not let this become a mini-literature review. The “forces” objection should remain. This is where Pollock can do its work. We can say that calling these regularities “forces” is not personification; it is causal abstraction at the level of generated language. Pollock helps show why that is a legitimate move in an aesthetics that already attends to causal order. For §6, the output/chat/model distinction should now require almost no setup. The tree/forest/biosphere analogy should be cut or reduced to one sentence. The reasoning-output case should be compressed but not eliminated: it shows that the account applies to ordinary LLM prose, not only weird edge cases. The bee case should remain the vivid example. §5.2’s practical-acquaintance material should move into §6.2 on chats. The video-game analogy in §6.3 should go. The “mirror of culture” paragraph should go. ### 11\. Claude is right that the old §5.1 opening needs replacement Yes. This is small but consequential. If §2 no longer says “token-based predictors trained on large text corpora and shaped by RLHF” in that exact way, §5 cannot open by summarizing it that way. Better: > Section 2 described LLMs as trained systems that generate linguistic continuations from a context. It also identified the regularities relevant to such generation: patterns of vocabulary, register, coherence, explanation, dialogue, and post-trained assistant-style response. We can now ask what kind of knowledge makes those regularities available for aesthetic appreciation. That links §2 and §5 without using “semiotic physics” too early. ### 12\. Claude is wrong about the alleged bias of “generation from learned regularities” Claude says the spine sentence itself biases the paper toward the trained-system view. I think that is not a serious objection. The point of §2 is not to be neutral among all rhetorically possible descriptions. It is to state what LLMs are at the level relevant to the paper. “LLMs are systems trained on large bodies of text to generate linguistic continuations from a context” is not a semiotic-physics thesis. It is a basic technical description. If that description later supports order appreciation more strongly than person appreciation or simple design appreciation, that is the argument, not a bias. Neutrality does not require giving equal space to personhood, design, and order in §2. That would be artificial. It requires not building the conclusions of §§3–5 into §2. A description in terms of training and generated text does not itself say “therefore semiotic physics.” It only supplies the facts §5 later uses. ### 13\. Claude’s “unresolved tension” is real, but his solution is wrong The tension is real: we want to follow Carlson closely without multiplying descriptions. But Claude resolves it by proposing “one identification plus a brief acknowledgment that several knowledge frames can address this kind of object.” That still sounds too much like a menu. The better resolution is: §1 gives the method. §2 identifies the object. §3–§4 test two inadequate modes of appreciation. §5 identifies the body of knowledge that fits the object. §6 applies that knowledge at three levels. That is cleaner, and it is closer to Carlson. Carlson does not need to say, “a landscape can be described aesthetically, religiously, geologically, ecologically, agriculturally...” before proceeding. He identifies the kind of object and then argues for the knowledge that makes its appreciation appropriate. We should do the same. ### 14\. Answers to Claude’s three questions On whether to fold the cascade into §§3–6 before drafting: yes, at least at the level of structural decisions. We should not draft §2 in isolation, because the whole point of rewriting §2 is that it changes what §§5–6 need to do. But we do not need to fully rewrite §§5–6 before drafting §2. We need only lock the cascade: §3.3 into merged negative section; Pollock into the design/semiotic-physics hinge; §5.2 into §6.2; video-game and mirror-of-culture out. On whether §2 should mention all three later candidate appreciations explicitly: no. It should identify the object and let the next sections raise the candidates. At most, the final sentence can say that the following sections ask what kind of appreciation this kind of system permits. But it should not list person, design, and order appreciation. On where §3.3’s “vibe is recurrent pattern, not subject” claim should live: mainly in the merged negative section. §2 should seed the phenomenon but not the verdict. So §2 says that post-training yields stable tone/style/vibe patterns. The merged negative section says these patterns do not amount to character. §6.3 says they are better understood as model-level semiotic order. ### Revised position after reading Claude The section should now be planned like this: *What LLMs Are* should be a 700–800 word answer to Carlson’s first recommendation. It should say that LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. It should explain generation, training, learned regularities, embeddings/attention, post-training, and the output/chat/model distinction. It should not mention artifacts, design appreciation, semiotic physics, forces, trajectories, or personhood. It should not include Olah. It should not include worked examples. It should give the thing whose appreciation the rest of the paper will assess. Then the later restructuring becomes much clearer: the merged negative section introduces person and design appreciation; the design part brings in artifactuality and Olah; Pollock bridges design and order; §5 introduces semiotic physics as the knowledge that makes the learned regularities appreciable; §6 applies it to outputs, chats, and models. i agree with pretty much everything you say here.. "The answer should be direct: LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. That is all §2 needs as its spine. It should not yet say “artifact.” It should not yet say “design.” It should not yet say “semiotic physics.” It should not yet set up a list of possible descriptions. It should describe the thing whose appreciation is later at issue." good. "All three may be defensible, but they are not equally placed. The first is anti-agency rhetoric. The second opens a semantics issue. The third is basically the §3.3 thesis. In §2, we should not yet settle the person-appreciation question. We should only give the account that later constrains it. The right §2 version should say something more restrained: the system generates text through learned continuations; post-training makes some response profiles more likely; users often experience those profiles as tone, style, or “vibe.” Full stop. The “not a subject” conclusion belongs in the merged negative section." i agree with thiws, but make sure that a) you don't tie yourself in knots trying to explain the part of the training which is menat to make the llm more persona like with out mentioning personas etc. b) don't just cut, think about how the extra space could be used to properly articulate what llms are. "That is not just a feature-list. It is the minimal technical answer to the Carlsonian question. It gives us what the thing is, without yet deciding which appreciative framework follows." good "No Olah." when would ollah be used? But can we put sections 5 and six on the back burner for a few moments and focus on section 2. I am worried 700 800 is too few words (remember you, like many LLMs have a fetish for short, shallow texts, we need to fight this), but let's see how we go. Good. Then we should loosen the 700–800 target. That was a cutting target, not a philosophical target. For §2, I now think the right range is probably *900–1,100 words*. Below that, the section risks becoming exactly the kind of compressed LLM prose you rightly want to avoid: a sequence of correct but under-articulated claims. The point is not to make §2 short; it is to make it *non-tutorial* and *properly placed*. On Olah: he should be used in the later design/artifact discussion, not in §2. More exactly, Olah belongs at the point where we have already introduced the tempting thought that LLMs are designed artifacts and should be appreciated through design. Then we can say: yes, but even people working on these systems often describe neural networks as things we “grow” rather than simply program. That quote then helps mark the limit of design appreciation. In §2, by contrast, Olah would prematurely introduce the artifact/growth contrast before we have even raised design appreciation as a candidate. So: no Olah in §2; Olah in the merged negative section, probably in the design-appreciation part. For §2 itself, I would now aim for something like this: ## Section 2: What LLMs Are The section should have one central sentence: > LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. Everything else should unpack this sentence. ### 1\. Opening: answer Carlson’s question directly The first paragraph should be short but not perfunctory. It should not repeat §1’s account of Carlson. It should simply take §1 as established and answer the question it generates. Something like: > If appreciation should be guided by what the object is, we need a schematic account of LLMs at the level relevant to aesthetic appreciation. For present purposes, LLMs are systems trained on large bodies of text to generate linguistic continuations from a context. This description is deliberately limited. It does not settle whether such systems should be appreciated as persons, as designed artifacts, or in some other way. It gives the account of the object to which those later claims must answer. That already does the defensive work without overdoing it. ### 2\. Generation from context The next paragraph should explain generation, but in a more mature way than the current token tutorial. We need tokens, but we do not need fake token IDs or fake probabilities. We need sequential generation, but we do not need two worked examples. We should say that the system receives a context, generates a continuation, and incorporates each generated token into the next step of generation. The key thought is that an LLM output is not emitted whole. It is produced through an iterated process in which the preceding context conditions each next step. That matters later for outputs and chats. Possible prose: > At the point of use, an LLM receives a context: a prompt, prior turns in a conversation, system-level instructions, and whatever other material is available to the model. It then generates a continuation by producing one token after another. Each token is selected in relation to the context so far; once produced, it becomes part of the context for the next step. The output is therefore built sequentially. It may have the surface form of an answer, explanation, objection, apology, joke, or refusal, but its immediate generative form is continuation from context. This is not shallow, but it is not a tutorial. ### 3\. Training as the source of regularity This should be the longest and most carefully articulated part. This is where the current §2 can gain philosophical depth from the space we save. We should explain that training is not just “feeding it data.” It is the process by which the model acquires dispositions to continue text in some ways rather than others. This is the neutral basis for later claims about order. Possible prose: > The dispositions that guide such continuation are acquired in training. During pre-training, the system is exposed to large bodies of text and adjusted so that it becomes better at predicting continuations. What is acquired is not a set of explicit rules stating what to say in each circumstance. Nor is it a database of stored sentences from which the model retrieves an answer. Rather, training gives the system a highly structured sensitivity to patterns in text: which words tend to occur together, how syntactic dependencies are marked, how registers are sustained, how explanations unfold, how objections are introduced, how stories continue, how dialogues proceed, and how different kinds of discourse normally organize themselves. The generated output is shaped by these learned regularities. This is where we should spend words. This is the core of “what LLMs are.” ### 4\. Embeddings and attention, but only as forms of organization Here we give just enough technical detail to explain why outputs have local structure. We do not need the “cat/dog” example unless the prose feels too abstract. The aim is to explain clustering and context-sensitivity. Possible prose: > Two aspects of this organization matter for the later argument. First, tokens are represented in relation to other tokens on the basis of patterns of use. This is why generated text often gathers around associated vocabularies, topics, and registers: a prompt concerning law, medicine, poetry, or programming does not merely introduce isolated words, but activates a structured field of related continuations. Second, transformer models use attention mechanisms that allow different parts of the context to bear differently on what is generated next. This helps explain how a model can sustain a topic across several sentences, preserve a distinction introduced earlier, or continue a style established by the user. It also helps explain why such coherence can fray, shift, or collapse. That last sentence is useful because it prepares not only successful order but breakdowns, which are often aesthetically revealing. ### 5\. Post-training without tying ourselves in knots I agree entirely with your warning here. We should not contort ourselves to avoid words like “assistant,” “persona,” or “person-like.” We just need to avoid drawing the conclusion too early. So we can say openly that post-training makes models more assistant-like and sometimes more persona-like. What we should not say in §2 is whether that persona-like profile is a person, a fictional character, a merely designed interface effect, or a semiotic pattern. Possible prose: > The systems most users encounter are not base models. They have usually been further trained for instruction-following and chat. Instruction tuning, RLHF or related methods, system prompts, safety policies, and interface design make some continuations much easier to elicit than others. They favor helpful answers, explicit qualifications, refusals, apologies, summaries, step-by-step explanations, and forms of address appropriate to an assistant. They also help produce the relatively stable tone, style, or “vibe” that users notice when comparing models. In this limited sense, post-training can make a system more persona-like. Whether that persona-like profile supports person-based appreciation is a further question. That last sentence is clean. It names the phenomenon without settling the issue. ### 6\. Outputs, chats, models The final paragraph should introduce the three later objects, but only as levels of analysis. No tree/forest/biosphere. No “specimen/environment/ground of order” yet. Possible prose: > This description also distinguishes three levels at which LLMs can be considered. A single output is a bounded linguistic continuation generated from a particular context. A chat is an extended sequence in which earlier turns condition later ones, so that vocabulary, register, assumptions, and tasks can accumulate or shift over time. A model is the trained system whose tendencies become visible across many such outputs and chats. These levels are not independent objects in the strong sense: outputs and chats are ways in which the model is encountered. But they mark different scales at which aesthetic attention may later be directed. That is enough setup for §6, without making §6 redundant. ## Revised word target I would set the target at *950–1,100 words*. That gives enough room to articulate training properly. A possible distribution: | Part | Target | | --- | --- | | Opening | 90–120 | | Generation from context | 140–170 | | Training and learned regularities | 230–280 | | Embeddings and attention | 170–220 | | Post-training and persona-like profiles | 180–230 | | Outputs/chats/models | 130–170 | That gives a section of roughly 950–1,190 words, depending on how much texture we want. I would not force it below 900. The guiding principle should be: cut examples, not articulation. The original §2 spends a lot of words on examples that teach the mechanism. We can reuse that space to articulate the philosophical characterization more carefully. That means the revised section can be shorter than the current one without becoming shallow. ## The settled rule for §2 So, for now, I would lock these constraints: No “artifact.” No “design.” No Olah. No “semiotic physics.” No “forces,” “laws,” “trajectories,” or “learned landscape.” No anti-agency rhetoric. No semantic verdicts about whether the model “learns that” anything is the case. No conclusion that vibe is not a subject. But yes to: context, continuation, training, learned regularities, embeddings, attention, post-training, assistant-like and persona-like profiles, tone/style/vibe, outputs/chats/models. That gives us a §2 that is not a tutorial, not shallow, and not prematurely argumentative.