> [!NOTE] > I haven't had a chance to work on this section yet. at all. LLMs can be appreciated at three levels: individual outputs, extended chats, and models themselves. Discussion of generative AI aesthetics has so far focused on outputs, such as images from Midjourney and texts from ChatGPT. But chats and models are also objects of appreciation, and the framework developed in Section 5 applies at each level. The relations among these levels can be clarified by analogy. An individual output is like an individual natural object, a tree, say: it is a sample of how semiotic forces have shaped a particular text under particular conditions. A chat is like an environment, a forest: semiotic forces shape the exchange over many turns, producing a configuration with its own coherence and dynamics. A model is like a natural system, the planet's biosphere, or the planet itself: it is the ground of order that manifests in outputs and chats, the system whose regularities produce those manifestations. Appreciation at each level calls for its own acts of aspection, though all are guided by knowledge of semiotic physics. ## 6.1 Appreciating Outputs A single output is one realisation of the model's semiotic physics under particular conditions. The prompt, the system configuration, and the preceding context specify initial conditions from which the model propagates text in line with its learned regularities. Different prompts activate different regularities; different contexts produce different trajectories. No single output exhausts the model's characteristic order. But each output shows how the forces operate in a specific case, and each can be appreciated as such. To show how semiotic physics guides aspection of outputs, we consider two cases that occupy different regions of a model's behavioural space. The first is the reasoning-style output familiar from everyday use: step-by-step structure, numbered stages, explicit hedging, restatement of the question, and a concluding summary. As human prose, such passages resemble competent but unremarkable textbook writing. They are useful for teaching and troubleshooting, but they do not obviously invite aesthetic attention. From the perspective of semiotic physics, however, the same outputs look different. The model has been trained on reasoning-related texts: worked proofs, textbook explanations, exam solutions, and online Q\&A threads. It has learned that certain kinds of questions are typically followed by sequences with a characteristic structure. Post-training procedures, including instruction tuning and reinforcement learning that rewards explicit intermediate steps, further bias the model towards this pattern. Reasoning-style outputs are a stable attractor in the model's behavioural space: once entered, the model tends to stay in this mode, yielding modal inertia. The hedging, the step-by-step structure, and the summary are marks of alignment pressure: response shapes reinforced because they correlate with high human ratings. Given this, we attend differently. We attend to the characteristic rhythm of the reasoning mode: how steps are sized, how transitions are signalled, and whether the pacing is tight or padded. We attend to where alignment pressure shows: hedging patterns ('it seems', 'one might argue', 'I think'), politeness markers, and pre-emptive qualifications. We attend to how semantic attraction operates under tight constraints: vocabulary stays on topic, related terms cluster, and the model is pulled towards the semantic field established by the question. We also attend to whether the mode remains stable or shows signs of strain, and to whether the model sustains the reasoning register or begins to drift. What seemed merely useful becomes appreciable as a specimen of how semiotic forces produce reasoning-like text under tight constraints. ![][image1] The second case is different. The text discussed here was produced by a Claude-like model in a modified configuration with safety constraints relaxed. It begins with neologisms and proceeds in short blocks separated by headings in capitals. The vocabulary is dense with coinages, many of which recombine recognisable roots from entomology, anatomy, theology, and internet slang. The registers are mixed: fragments of cod-French, pseudo-scientific talk, mystical declarations, and obscene slang. Despite the surface disorder, a stable theme runs throughout: bees and honey, tongues and throats, sweetness, bodily contact. Under semiotic physics, this text shows the forces operating under loose constraints. Semantic attraction is at work: the bee and honey theme creates an attractor, and related vocabulary – tongues, throats, sweetness, pollen, flowers, stings – is pulled towards it. But unlike the reasoning case, the attraction spreads freely across registers rather than being channelled narrowly. The model has been trained on texts that invent words – experimental poetry, surrealism, internet wordplay – and it has learned patterns of neologism: how to recombine roots, suffixes, and sound-shapes. The neologisms follow learnable patterns of word-formation rather than being random noise. The register collision reflects training diversity: the model has absorbed texts in many registers (scientific, mystical, erotic, internet-surreal), and under loose constraints these do not get filtered to a single appropriate register. They collide and mix. Despite the apparent chaos, there is order: recurring rhetorical templates, alternation between narrative stretches and reflective sentences, and consistent sound-play in the neologisms. This order is the product of semiotic forces operating with fewer constraints than in the reasoning case. Attending to this text with knowledge of semiotic physics, we notice how semantic attraction shapes the vocabulary: the gravitational pull towards bee-related terms operates across registers. We also notice patterns in neologism (learnable word-formation rules that produce coinages with a family resemblance) and the rhythm of alternation between modes (narrative stretches, reflective sentences, and exclamatory outbursts). Finally, we notice internal consistency despite surface chaos. The text becomes appreciable as a specimen of semiotic forces operating in a different region of behavioural space from the reasoning output. Carlson (2000) notes that once a specific scientific account is in play, some natural formations show the relevant order better than others: not every cliff face is equally instructive about sedimentation, not every valley equally revealing of glacial dynamics. This prevents order appreciation from collapsing into the view that everything is equally appreciable; the guiding knowledge discriminates among cases. The same holds for semiotic physics. Standard reasoning-style outputs show semiotic order, but the order they show is shallow and familiar: alignment pressure is everywhere visible, the reasoning template is stock, and the semantic channelling narrow enough that the regularities are unsurprising. The bee text is a more interesting object of appreciation not because it is more orderly but because it reveals order where none was expected. What looks like chaos – neologistic excess, register collision, surface incoherence – turns out, under semiotic physics, to be structured by identifiable forces: semantic attraction spreading freely across registers rather than channelled narrowly, learnable word-formation patterns producing coinages with family resemblance, rhythmic alternation and internal consistency maintained beneath apparent disorder. The bee text also shows forces operating in regions of behavioural space that normal product configurations occlude. It is, in this sense, analogous to a geological formation that exposes strata usually buried – not more ordered than the surrounding terrain, but more *revealing* of the order that is everywhere present. The contrast between these two cases helps to locate what semiotic physics brings into view. Reasoning outputs show semiotic forces operating under tight constraints: a stable mode, narrow semantic channelling, and alignment pressure shaping response structure. The bee text shows semiotic forces operating under loose constraints: unstable modes mixing, semantic attraction spreading across registers, and training diversity showing through. Both are products of the same semiotic physics, but they occupy different regions of the model's space. Appreciating both requires the same kind of knowledge – knowledge of semiotic forces – but different acts of aspection. We scan the reasoning output for rhythm and regularity; we scrutinise the bee text for pattern within apparent chaos. ## 6.2 Appreciating Chats Carlson's environments are not collections of discrete objects but systems in which forces operate and interact over space and time. A forest is not just many trees; it is a space where ecological forces – competition for light, nutrient cycling, succession dynamics – play out, producing emergent order that no single organism embodies. The appreciator navigates this environment, and their path determines what order becomes visible. Chat instances stand to single outputs as environments stand to individual natural objects. A chat accumulates context that shapes how semiotic forces manifest: early vocabulary choices establish attractors that persist, early register-setting constrains later exchanges, and the exchange develops path-dependent structure that neither party fully controls. The user's prompts are not just elicitations but navigational interventions, steering the system through different regions of its behavioural space and making different orders visible. To appreciate a chat is to appreciate an emergent configuration produced by semiotic forces operating over the chat's temporal extension – not just a sequence of isolated responses. A single output is one trajectory from one set of initial conditions. An extended exchange lets regularities show up across turns. The model carries forward elements of earlier responses, picks up threads, sometimes drops them, and shifts register in response to user prompts. Chats manifest features that a single output does not. First, coherence maintenance, that is, how the model sustains or loses threads across turns, how far back its effective 'memory' extends, and where coherence begins to fray. Second, context accumulation, which determines how earlier material shapes later responses, and how terms or framings established early persist or fade. Third, register dynamics, which concerns how the model responds to shifts in user tone, topic, or style, and whether it matches the user's register or maintains its own. Finally, mode stability over time, to wit, whether the model stays in a mode or drifts, what triggers transitions, and how gracefully it handles them. These are manifestations of semiotic forces operating over longer timescales than a single output can reveal. A chat instance is like a particular forest: the forces of semiotic physics have produced a specific configuration. Different prompting strategies, different topics, and different user styles produce different configurations. But the same underlying forces are at work. Appreciating a chat means attending to how the forces have shaped this particular extended exchange: how contextual threading has produced coherence or incoherence, how modal inertia has maintained or failed to maintain a register, and how alignment pressure has shaped the arc of the exchange. In a chat, prompting is intervention. Each turn is a probe that reveals something about the model's regularities. The experienced user's expectations are tested and refined across many turns. Interaction is itself a mode of aspection: it selects what to attend to, organises appreciative attention over time, and deepens practical acquaintance with the model's semiotic physics. The farmer comes to know the land through working it; the user comes to know the model through prompting it. Extended exchanges are where practical acquaintance develops, where the user builds up the kind of knowledge that guides appreciation even without deliberate theoretical articulation. ## 6.3 Appreciating Models Outputs and chats are where semiotic order manifests. The model itself is the ground of that order: the system whose regularities produce particular manifestations. Appreciating a model means appreciating its characteristic order across many possible outputs and chats, not just the ones actually encountered. This is not appreciation of any single output but of stable patterns across outputs: which registers the model favours, how it handles uncertainty, where it excels, where it struggles, and what regions of semiotic space it can occupy. Users sometimes speak of a model's 'vibe', a term that captures the sense that different models have different characteristic feels even when performing similar tasks. This notion of vibe, or characteristic feel, warrants pause. In Section 3 we argued against appreciating LLMs as if they were persons, on the grounds that LLMs lack the temporally extended life, the projects and commitments, and the evaluative outlook that ground beauty-of-character predicates. But users do respond to something when they talk about a model's personality or vibe. What they are responding to, we suggest, is not a character in the person-aesthetic sense but a characteristic semiotic order: a stable pattern in how the model tends to propagate text. Appreciating this order is not appreciating a person; it is appreciating a system's characteristic dynamics. The vocabulary of 'vibe' is a colloquial marker of what semiotic physics articulates more precisely. An analogy clarifies what appreciation of a model involves. Different 3D video games have different physics engines. *Grand Theft Auto V* has physics tuned for spectacle: cars drift in satisfying ways, explosions have exaggerated force, and the rag-doll system produces emergent comedy. *Dark Souls* has physics tuned for weight: movement feels heavy, attacks have commitment, and everything has momentum. *Breath of the Wild* has physics tuned for playful engagement: objects afford interesting interactions, and the system invites experimentation. We appreciate these physics not primarily by asking how realistic they are but by attending to internal consistency, characteristic feel, and aesthetic fit. *Grand Theft Auto*'s physics serves an aesthetic of chaos and spectacle; it would not suit *Dark Souls*. Each game's physics is tuned to its aesthetic and ludic goals. We appreciate the physics for what it is, not for its fidelity to real-world physics. For LLMs, the analogy suggests a parallel mode of appreciation. Different models have different semiotic physics: different characteristic dynamics of text propagation. Claude's semiotic physics differs from GPT's, which differs from Gemini's. We can appreciate these differences not primarily by asking which is most human-like or most useful but by attending to internal consistency and characteristic feel. Order appreciation, as Carlson develops it, differs from design appreciation. We do not primarily ask how well the artifact serves its intended function. We attend to the order itself, the patterns produced by the forces, and appreciate them for their own character. The bee text discussed in Section 6.2 is relevant here in a further way. It was produced under relaxed constraints, revealing a region of Claude's behavioural space that is normally inaccessible under standard product configurations. Knowing that this region exists – and knowing what the model can do under different conditions – is part of appreciating the model. Model appreciation involves appreciating not just the outputs a model typically produces but the full space of outputs it could produce, and how different conditions activate different regions of that space. The bee text is a window into latent capacities, a sample from a region of semiotic space that standard use does not reach. Different models instantiate semiotic physics differently. Different training corpora, different architectures, and different post-training regimes produce different characteristic orders. Users report different feels when interacting with different models: Claude's hedging rhythms differ from GPT's briskness, and Gemini handles certain registers differently. A fuller account of model-level appreciation would map these differences systematically, developing a comparative aesthetics of LLMs. That task lies beyond the scope of this paper. For present purposes, the point is that model-level appreciation is possible and that it takes the form of appreciating distinctive semiotic order: the characteristic dynamics of text propagation that distinguish one model from another. We should distinguish the kind of appreciation we have been describing from other modes of engaging with LLMs. Capability evaluation tests whether models perform tasks correctly. Safety testing probes whether models can be induced to produce harmful outputs. Benchmarking measures performance against standardised criteria. The appreciation we describe differs from all of these. The goal is not to assess correctness, safety, or performance but to appreciate characteristic order, and to develop acquaintance with semiotic physics as it manifests in a particular model. A response that would count as a failure in capability evaluation might be aesthetically rewarding as a product of learned regularities. The appreciator is not grading but attending, not measuring but developing acquaintance. Finally, we note a thought that we flag here but do not develop. Each model, as an instantiation of semiotic physics, has learned regularities from human text. It reflects, in transformed form, the semiotic culture of its training data. There is a sense in which generative AI is a mirror of culture, not only morally, as Vallor (2024) has argued, but aesthetically. The model shows us our own semiotic patterns, filtered through statistical learning. Appreciating an LLM is, in part, appreciating culture seen through technology. This thought merits extended treatment, but such treatment lies beyond the scope of the present paper and we reserve it for future work. ## 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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[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/ [^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.