## 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 regularities governing text propagation in LLMs. 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 articulation of these patterns and understanding of their source in the model's training. This understanding makes different features salient and organises aspectual attention differently than reading-as-human-expression would. ## 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.