# 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, 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 and from the way generated continuations develop from context, 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 knowledge — geology, ecology, and the like — that makes those patterns visible. LLMs call for something similar: attention to patterns produced by trained continuation systems, guided by what we call semiotic physics — knowledge of how generated text develops from context under learned constraints. This framework applies at three levels: outputs as bounded continuations, chats as extended exchanges, and models as the trained systems whose tendencies become visible across many outputs and chats. The result is an aesthetics that treats LLMs neither as quasi-persons nor as ordinary tools, but as generative systems with their own characteristic order. Section 1 sets out Carlson's distinction between design appreciation and order appreciation, and considers why person appreciation has to be discussed alongside it. Section 2 asks whether design appreciation can guide the appreciation of LLMs, and argues that LLMs are best understood, for present purposes, as trained continuation systems. Section 3 asks whether person-directed knowledge can guide their appreciation. Section 4 introduces _semiotic physics_ — knowledge of how trained continuation systems develop text from context — as the right kind of knowledge for order appreciation of LLMs. Section 5 shows how this framework guides appreciation at the levels of output, chat, and model. --- # 1.Appreciating Design, Appreciating Order Both our criticism of agentive views and our positive account will draw from Carlson's environmental aesthetics, as laid out in his 2000 book _Aesthetics and the Environment_. In particular, we adopt Carlson's general recommendation for aesthetic appreciation: take things as what they are, and look at them in the light of the right kind of knowledge. He applies this to the appreciation of the natural environment thusly: > First, that, as in our appreciation of works of art, we must appreciate nature as what it in fact is, that is, as natural and as an environment. Second, it recommends that we must appreciate nature in light of our knowledge of what it is, that is, in light of knowledge provided by the natural sciences, especially the environmental sciences such as geology, biology, and ecology. The natural environmental model thus accommodates both the true character of nature and our normal experience and understanding of it. (Carlson, 2000, p. 6) This captures something intuitive about how we appreciate nature versus art. Appreciating mountains and cliff faces as the work of a divine artisan, rather than of natural forces, would be wrong-headed (cf. Carlson, 2000, Chapter 8); so would appreciating a Rembrandt as if it were the product of natural forces slopping paint together (cf. Danto 1974, p. 140). In both cases, appreciation is undermined by a failure to recognise what the object really is. Carlson argues that artworks and everyday objects call for _design appreciation_. With paradigmatic artworks,[^1] we recognise them as creations of designers, objects whose features are, as Gombrich puts it, each "the result of a decision by the artist" (Gombrich, 1950, p. 13, quoted in Carlson, 2000, p. 109). We appreciate such works by seeing how well the result realises the artist's design. The same approach extends to designed artifacts more generally. Carlson is explicit that functional objects are properly appreciated by seeing how their forms answer to what they are for: > This is in part the point of the much-repeated phrase 'form follows function.' The forms of all functional objects — buildings, airplanes, and appliances as well as landscapes — must be aesthetically appreciated in terms of how and how well such forms fit their functions. However, the cliché is frequently interpreted too narrowly. With anything functionally designed, not only its form, but much of its aesthetic interest and merit, 'follows function'. (Carlson, 2000, ch. 12, p. 188) So, on this account, a chair, or a bridge invite the same style of attentive appraisal as a painting – guided by knowledge of ends, materials, constraints, and the fit between purpose and realisation. For the natural environment, in contrast, Carlson recommends a different mode: order appreciation. Appreciation of things like trees or valleys cannot be grounded in considerations of how well a designer managed to realise her intentions, because they are not designed objects. Instead, Carlson recommends that the knowledge grounding appreciation of the natural world is appreciation of how the order we find has been shaped by natural forces: > On the assumption that order appreciation provides the correct model for the appreciation of nature, such appreciation has the following general form: An individual qua appreciator selects objects of appreciation from the things around him or her and focuses on the order imposed on these objects by the various forces, random and otherwise, that produce them. Moreover, the objects are selected in part by reference to a general nonaesthetic and nonartistic story that helps make them appreciable by making this order visible and intelligible. (Carlson, 2000, p. 119) In both modes, knowledge guides acts of aspection — ways of attending to an object that partly constitute its appreciation (Carlson, 2000, pp. 41–42, 106). But the character of this knowledge differs. In the case of design, we need functional and technical understanding: what the designer intended and what constraints they faced. In natural cases, we need an account of the processes that produced the order we perceive — knowledge that lets us see natural structures as effects of processes (Carlson, 2000, pp. 50, 60–61). Once a specific scientific account is in play, some cases will exhibit its order more clearly than others — which prevents order appreciation from flattening every natural object into equal appreciability (Carlson, 2000, pp. 118–119). It could be argued, however, that Carlson’s approach to aesthetics overlooks another important category of object of appreciation: people. Their character traits can be aesthetically as well as morally valuable — what is sometimes called _beauty of character_ (Gaut 2007; Paris 2018). Carlson's recommendation seems naturally extendable here: appropriate aesthetic appreciation of persons will depend on the right kind of person-directed knowledge.[^2][^3] %%one more sentence, but what? consider what is immediately before and after%% LLMs are manmade artefacts, so the natural first thought is that they admit design appreciation in the way other functional objects do. In the next section, we shall suggest that this cannot be the whole story. --- ## Footnotes [^1]: We will consider some non-paradigmatic artworks in Section 5. [^2]: Carlson stresses that ordinary descriptions of environments and more theoretical scientific, historical, and functional descriptions lie on a continuum, so that scientific and historical knowledge can deepen rather than displace practical familiarity as a basis for aesthetic appreciation (Carlson, 2000). By analogy, one might speculate that the sciences of mind and behaviour could relate to folk-psychological and biographical understanding in a similar way, so that in some cases empirical work on personality, emotion, or cognition might feed into the aesthetic appreciation of persons alongside the more everyday forms of knowledge stressed in the main text. [^3]: This personal appreciation also scales up to what we might call performance personalities. We respond to a comedian's improvisational skill or an orator's gravitas much as we respond to character in our friends, but now filtered through a public persona – genuine to the individual, yet artfully composed for performance. Recent scholarship has engaged with these performative dimensions, examining how performers construct and present public personas (Carroll 2013). Here too, Carlson's knowledge requirement bites: to appreciate such personas we need to understand both the individual and the conventions of the performance context. --- # 2. What LLMs Are It might seem obvious what sort of knowledge would be required to ground the appreciation of LLMs. LLMs are artefacts, and design appreciation, as Section 1 set it out, is one way of satisfying Carlson’s demand that appreciation answer to the kind of thing its object is. In the case of technical artefacts, this requires knowledge of the purpose for which the object was made and of how its features serve that purpose. LLMs initially look like straightforward candidates for this mode of appreciation: they are engineered systems, and their responses depend on choices made before any user enters a prompt. We shall argue in this section that knowledge of the creation and functioning of LLMs cannot, on its own, ground their aesthetic appreciation. A car is appreciated through its designed form. The shape of its body is at once what the appreciator looks at and what determines how the car moves through air at speed. This shape is what it is because of what the car is for. To appreciate the car aesthetically is to attend to this fit between form and purpose, and design knowledge — knowledge of the car's purpose and of how its features answer to that purpose — reaches the form the appreciator engages with. In the LLM case, design does not completely settle how the system will respond to a prompt; it determines only the conditions under which the model is trained. A transformer architecture gives text a processable form by dividing it into tokens and relating positions in a sequence through attention. The training objective then gives the system a standard against which its outputs can be adjusted: given a stretch of text, the model assigns probabilities to possible continuations, and its weights are altered when those probabilities diverge from the continuation found in the training data. What the trained model then does with a prompt, however, is not settled by these choices; it is acquired over the course of training. When the system responds to user input, it uses what it has acquired in training to continue the text it has been given. The prompt, together with whatever the system has already produced, gives it a context. From that context the model assigns probabilities to the possible next tokens, one of which is selected and added to the context before the same step runs again, so that what appears at the end as a single answer is built through successive transitions of this kind. Later parts of the continuation depend on earlier parts, and each step is shaped by the dispositions acquired in training. The text the appreciator reads is the trained system going on from the prompt — under conditions design has set, but in a direction it has not. Olah describes the relation between what design fixes and what training produces as a kind of growth. > I think one useful way to think about neural networks is that we don't program, we don't make them, we grow them. We have these neural network architectures that we design and we have these loss objectives that we create. And the neural network architecture, it's kind of like a scaffold that the circuits grow on. It starts off with some random things, and it grows, and it's almost like the objective that we train for is this light. And so we create the scaffold that it grows on, and we create the light that it grows towards. But the thing that we actually create, it's this almost biological entity or organism that we're studying. (Olah 2024) What design reaches is the scaffold and the objective; the form that grows under them is acquired through training. In the car case, what the appreciator engages with is the form the designer built. An LLM's response is produced by trained dispositions operating on the prompt and on the continuation as it develops. Design knowledge can explain the conditions under which the response becomes possible; it does not by itself give us the order of the generated text. Carlson's form-follows-function claim also depends on there being a function determinate enough to guide appreciation. The car has such a function: it is made to be driven, and its form is shaped by what that requires. LLMs do not stand in this relation to any function. Next-token prediction is the training objective, but no one consults an LLM in order to have next tokens predicted. The deployment intention that the system should be helpful is closer to use, though too unspecific to constrain the trained form. A prompt can recruit the same system into quite different activities, and the configuration that supports this openness has not been shaped to any one of them in particular. Form-follows-function reasoning needs a determinate function for the form to follow, and an LLM has none. Artefact is too coarse a description for the appreciation Carlson requires. LLMs are artefacts, and design knowledge bears on how they should be appreciated; ignoring how they were built would distort that appreciation. Carlson's question, however, is what the thing is at the level needed for appropriate appreciation. At that level, an LLM is a trained continuation system: a made system whose responses are generated from dispositions acquired in training and operating on context. Post-training helps explain why the generated continuation can seem like a speaker's reply. It is the further stage of training in which the model's responses are shaped toward the form of an assistant's answer. The output therefore arrives in the form of a turn in an exchange. Person-directed knowledge is the next candidate for what design knowledge cannot supply. Section 3 takes the question up. [^1]: Strictly speaking, generation proceeds token by token. Since token boundaries vary across tokenisation systems, the difference can be left in the background. [^2]: The regularities at issue operate at many scales, from local word co-occurrence to the structuring of extended discourse. Calling them dispositions marks this probabilistic and context-sensitive character; it carries no attribution of beliefs, intentions, or other personal states to the model. --- # 3. LLMs and Person-Directed Knowledge Person-directed knowledge is a natural candidate for what design knowledge leaves out. This temptation arises because the way we interact with LLMs is so similar to interacting with actual human interlocutors. We sometimes appreciate persons aesthetically. A person’s warmth may be aesthetically appreciable as a feature of character, rather than as a feature of bodily appearance. Gaut (2007) and Paris (2018) treat such appreciation as directed at the traits and dispositions through which a person’s life is intelligible. Something similar is possible with fictional characters: we can aesthetically appreciate a character as a person within a fiction, without believing that the character exists outside it. The account of LLMs in Section 2 puts pressure on this comparison. The system producing the text is a trained continuation system: it generates responses by applying dispositions acquired in training to the context it is given. The person-directed thought can be preserved in two ways. The first grants that the LLM is not literally a person but reads its outputs as fictional speech, so that what is appreciable is a fictional character rather than the system itself. The second argues that the LLM is an intentional system in a thin sense, and that this is enough to license some person-directed appreciation. Mallory (2023) takes engagement with a chatbot to be a game of prop-oriented make-believe. The chatbot provides text that functions as a prop for imagining an interlocutor. Its outputs are "literally meaningless but fictionally meaningful" (Mallory 2023, p. 1082). On Mallory’s account, generated strings can function as props: they make it fictional that a character has said something, while the system that generates them remains distinct from that character. Mallory develops this as a metasemantic and epistemic account rather than an aesthetic one. Adapted to the present question, it suggests that the person-like object of appreciation is the fictional interlocutor made available by the game. The warmth or wit a user finds in Claude or in ChatGPT is the warmth or wit of a fictional interlocutor that playing with the prop brings into the game. Mallory’s fictionalism depends on keeping the prop apart from the character it makes available. Mallory is explicit: "the character is not this technological infrastructure any more than a character in a play is a human body or a costume" (Mallory 2023, p. 1091). The fictional interlocutor enters the game; the LLM is what makes the game possible. Person-directed appreciation can therefore attach to the interlocutor, but the interlocutor is not the LLM. The trained continuation system Section 2 identified remains to be appreciated as what it is, and Mallory's account leaves that task untouched. Frankish offers a more direct route, because he keeps the intentional description attached to the system itself. Drawing on Dennett’s intentional stance, he argues that we can ascribe beliefs and desires to a system when doing so yields a simple and fruitful account of its behaviour. On the shallow view he adopts, these attitudes need not be inner episodes or conscious states but can be dispositional patterns in the behaviour of a whole system. This makes the view unusually hospitable to LLMs: if treating a model as having beliefs and desires helps predict its textual behaviour, then the intentional description is not merely a fiction imposed from outside. Frankish’s own account also marks the limits of this move. LLMs are static systems with no needs and no social life, and their inner architecture does not update through interaction. For that reason, he does not credit them with the range of communicative desires we ascribe to human speakers. The thin agency he allows them is organised around a single goal: to play the chat game, producing textual responses that are cooperative by human conversational standards, given the context. The chat-game agent, however, falls short of what person-appreciation needs. A person's linguistic behaviour, as Frankish himself notes, is embedded in a wider web of non-linguistic behaviour, and tracking the predictive patterns of that whole web involves ascribing a much wider range of desires than the chat game alone calls for (Frankish 2024, p. 15). The chat-game agent has nothing answering to that web. The intentional structure Frankish licenses is restricted to making moves in the chat game; it does not place the text within a life in which earlier conduct informs later conduct. The categories person-appreciation works with depend on this kind of life. Steadiness, for example, can only be shown across the situations that make up someone's life. The chat-game agent does not have one. Post-training shapes generated text into stable patterns, and this might seem enough for person-appreciation. Users do say that one model feels friendlier than another, and they are picking up on something — a regularity in how chat-optimised systems tend to respond across many prompts. The regularity belongs to the way post-training disposes the system to continue prompts in assistant-like ways. It is not a trait of a temporally extended subject whose responses today are continuous with their responses last week. Applied here, the categories of person-directed appreciation lack the kind of subject they normally require. Neither person-directed route reaches the object isolated in Section 2: the trained continuation system whose responses develop from acquired dispositions operating on context. The remaining question is what kind of knowledge makes such generated text appreciable as the product of a trained continuation system. --- # 4. Semiotic Physics > [!NOTE] > i haven't checked the language from here on, please don't focus on the phrasing/grammar, it will be fixed in the next pass. Section 2 identified LLMs as trained continuation systems: made systems whose responses develop from dispositions acquired in training and operating on context. It also showed why design knowledge cannot, by itself, guide their appreciation. Design knowledge reaches the scaffold, the objective, and the conditions under which the system is trained and deployed. It does not by itself make visible the order acquired by a particular continuation as context is extended. Section 3 then showed why person-directed knowledge does not reach this object either. It reaches, at most, the person-like figure or profile made available through interaction. These bodies of knowledge therefore leave the same task unsettled. Carlson’s account gives that task a determinate form: the relevant knowledge must make the learned order of generated text visible as the order of a trained continuation system. 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 candidate we propose is semiotic physics. Semiotic physics, as we use the term, is an output-side account of how trained systems develop linguistic forms through iterated continuation from context. Section 2 described generation as iterated continuation. At any point in a run, the system receives the context so far and computes a distribution over possible next tokens. Once one token is selected, the context changes, and the next step is produced from that changed context. Training gives the system a graded sensitivity to the regularities of text — to what tends to follow what under what conditions. When the model is run, those regularities operate through a context that changes as the text develops. The prompt fixes the starting point; post-training and deployment affect which continuations are likely to be produced from it; sampling makes the text one realisation among other possible paths. Metasemi formulates this shift from isolated prediction to generated path: “It’s more illuminating to consider what happens when GPT . . . is run repeatedly to produce a multi-token forward trajectory, as in the familiar scenario of generating a text completion in response to a prompt” (metasemi 2023). The path generated by the model is computed token by token; the order at issue is encountered by readers as language. Semiotic physics has to stay tied to both features of the case. The trained system has acquired patterns governing what tends to follow what under given conditions; this is the structure Janus describes when he treats GPT-style models as simulators of a learned distribution (Janus 2022). The continuation also appears as linguistic form, which Picca captures when he describes LLMs as systems that “recombine, recontextualize, and circulate linguistic forms based on probabilistic associations” (Picca 2025, 1). Wolfram’s image of a trajectory in linguistic feature space connects generation and linguistic form: continuation traces a path through a learned space whose structure bears on whether the resulting text is meaningful (Wolfram 2023). The order made visible by semiotic physics is therefore the order of linguistic forms carried forward and transformed through continuation. Generated text develops by carrying forward what it has already produced. A prompt may set the task; the later shape of the continuation is also conditioned by material that has appeared in the output itself. This is why a response can gather a direction as it proceeds, or lose the direction it seemed to have. Semiotic physics directs aspection toward this path-dependence. The reader attends to the developing relation between earlier and later parts of the generated text, and understands that relation as a product of iterated continuation. The same account explains why person-like profiles remain aesthetically salient after the person-directed route has been rejected. A generated text can sustain a recognisable response profile across a continuation. Section 3 argued that such a profile is not the character of a subject. Semiotic physics treats it as a pattern in text propagation. 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. The question is whether force-talk repeats the mistake of projecting the wrong kind of structure onto a trained continuation system. Agent-talk attributes a subject with beliefs, intentions, a life, or character. Force-talk, in the restricted sense needed here, identifies factors that condition the continuation of text. The analogy with physics goes no further than regularity, constraint, and dependence on prior state. Its use requires regularities stable enough to guide attention to how generated text develops. Knowing that a text is LLM-generated rather than human-written changes how we aspect it. A human-written text is normally read as the product of authorial selection. An LLM-generated text can be read as a continuation shaped by context, learned regularities, and post-training. The same words on the page can therefore become appreciable under a different aspect. Semiotic physics works with ordinary linguistic competence. A reader already has a tacit sense of register, direction, and coherence. Semiotic physics gives that sensitivity a causal articulation by relating these patterns to training and generation. Familiar talk of model vibe can then be understood as a way of registering stable differences in how models propagate text. This order is encountered at more than one scale. A single output is one bounded continuation from a context. An extended chat is a longer process in which earlier turns condition later ones. A model is the trained system whose tendencies become visible across many such outputs and chats. These are different scales at which the same kind of semiotic order can be appreciated. The next section considers each in turn. --- # 5. Levels of Appreciation > [!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. 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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.