# Thinking again about semiotic physics Exploratory notes on [[Simulators by Janus]], [[Simulators Seminar 2 - Semiotic Physics by Jan]], and [[A Note on Semiotic Physics by metasemi]]. The question throughout: what does 'semiotic physics' mean, and what philosophical work does the concept do? --- ## What semiotic physics is Jan defines semiotic physics as "the study of the fundamental forces and laws that govern the behavior of signs and symbols" (line 32). The analogy with physical physics is structural: > "Similar to how the study of physics helps us understand and make use of the laws that govern the physical universe, semiotic physics studies the fundamental forces that govern the symbolic universe of GPT, a universe that reflects and intersects with the universe of our own cognition." (Jan, line 32) Jan frames the result as a dynamical system: "the simulator describes the system's dynamics and the simulacrum is instantiated through a particular trajectory" (line 57). The formal apparatus is self-contained. Jan defines the state space as T\* (token sequences), the transition rule as θ: T\* → ΔT mapping trajectories to probability distributions over next tokens, the sampling procedure ϕ selecting a next token, and the evolution operator ψ packaging these together: > "Putting the pieces together, we finally define the function ψ that evolves a given trajectory, i.e., transforms s̄ₜ into s̄ₜ₊₁ by appending the token generated by the sampling procedure ϕ. That is, ψ:T\*→T\* is defined as ψ(s̄):=s̄ϕ(s̄)." (Jan, line 73) No external referent appears in these definitions. janus frames the same idea differently: > "Models trained with the strict simulation objective are directly incentivized to reverse-engineer the (semantic) physics of the training distribution, and consequently, to propagate simulations whose dynamical evolution is indistinguishable from that of training samples." (janus, line 389) The parenthetical around 'semantic' feels tentative — as if janus recognises the word might not always be right. janus defines the simulation objective more precisely: > "A strict version of the simulation objective, which excludes GANs, applies only to models whose output distribution is incentivized using a proper scoring rule to minimize single-step predictive error. This means the model is directly incentivized to match its predictions to the probabilistic transition rule which implicitly governs the training distribution." (janus, line 371) The simulator/simulacra distinction matters here. janus writes: > "GPT is to a piece of text output by GPT as quantum physics is to a person taking a test, or as transition rules of Conway's Game of Life are to glider. The simulator is a time-invariant law which unconditionally governs the evolution of all simulacra." (janus, line 24 — verified against Readwise highlight) The simulator (GPT as trained model) is analogous to the laws of physics — time-invariant, applying everywhere. The simulacra (generated text) are analogous to configurations — spatiotemporally constrained things that evolve according to those laws. janus elaborates the distinction: > "There is a categorical distinction between a thing which evolves according to GPT's law and the law itself." (janus, line 413) --- ## Why 'semiotic' rather than 'semantic' janus uses "(semantic) physics" once (line 389). Jan and the seminar group chose 'semiotic' instead. Several reasons converge: - *Semiotics is broader than semantics.* Semantics concerns meaning (what signs denote). Semiotics concerns the entire behaviour of signs — how they combine, transform, constrain each other, produce effects. The transition rule doesn't just track meaning; it tracks all statistical regularities in sign behaviour. - *The tokens aren't meanings.* They're signs — vehicles that bear meaning but aren't reducible to it. A token sequence can be syntactically patterned, pragmatically loaded, and semantically meaningful all at once. 'Semiotic' captures this multi-dimensionality. - *The candidate 'laws' are semiotic, not semantic.* Jan proposes pragmatic and narrative principles as candidate semiotic laws: > "Principles from the field of pragmatics such as the Gricean maxims of conversation may be thought of as semiotic 'laws', and may be helpful for explaining and anticipating how contextual information influences the evolution of language model simulations." (Jan, line 196) > "The laws of semiotic physics dictate how objects and events are represented and interact in language models. These laws encompass principles such as Chekhov's gun, which states that objects introduced in a narrative must be relevant to the plot, and dramatic tension, which creates suspense and uncertainty in a narrative." (Jan, line 198) These are pragmatic and narrative principles, not truth-conditional semantic ones. --- ## Peircean semiotics and autoregression One way to read autoregressive generation through [[Charles Sanders Peirce]]'s framework (I'm grouping these connections as a cluster — they weren't all made at once in conversation): **Unlimited semiosis.** For Peirce, a sign produces an *interpretant* (its effect — roughly, another sign or thought), which in turn becomes a sign producing a further interpretant, without terminal point. Autoregressive generation has the same structure: each token (sign) produces a probability distribution (interpretant of sorts), which upon sampling yields a new token (new sign), which feeds back in. The process is interminable — janus notes that "the 'question' GPT answers is 'what token comes next after {context}'. This can be asked interminably, because its answer always implies another question of the same type" (line 257). **The interpretant is not a mental state.** Peirce's interpretant is not someone's subjective experience of a sign. It is the sign's *effect* — what the sign gives rise to. In the GPT case, the interpretant is the probability distribution θ(s̄) produced by the transition rule when it processes the token sequence. This is a formal, non-psychological reading. **Icon/index/symbol.** Peirce distinguishes signs by their relation to what they signify: icons (resemblance), indices (causal/physical connection), symbols (convention). Language tokens are predominantly symbolic. But GPT's internal processing may involve something more icon-like (pattern matching, structural similarity) even while operating on symbolic tokens. --- ## The 'no Peircean object' observation In Peirce's triadic semiotics, every sign has three components: the sign-vehicle (the token), the object (what it stands for), and the interpretant (its effect). In physical semiosis, the object constrains the sign — a weathervane points the way it does because of the wind. In autoregressive generation, there is no independent object constraining the token sequence. The transition rule operates on tokens alone. There is no wind making the weathervane point. The tokens evolve according to learned statistical regularities, not because of referential constraint from an external world. This is *not* the same as saying there is no 'stuff' in the system. The tokens ARE the stuff — the analogue of particles or matter in physical physics. What's absent is the external referential constraint that, in ordinary language use, anchors signs to things in the world. ### What follows from the absence of referential constraint The training objective is prediction (matching the distribution), not truth. As janus writes: "GPT does not consistently try to say true/correct things. This is not a bug — if it had to say true things all the time, GPT would be much constrained in its ability to imitate Twitter celebrities and write fiction" (lines 231–232). The transition rule has learned to produce statistically realistic continuations, not referentially accurate ones. This has explanatory payoff for understanding LLM behaviour: - Hallucination is not malfunction — it is the system doing exactly what its physics dictates (producing probable continuations) without referential constraint - Coherence without correspondence — outputs can be internally coherent (obeying semiotic laws like Gricean maxims) while bearing no systematic relation to external facts - Prompt sensitivity — because there is no external anchor, the system's behaviour is entirely determined by the token history. Small changes in prompt can produce large changes in output, since there is no independent reality pulling the output back --- ## Whether the formal system is really self-contained The formal apparatus (tokens + transition rule + evolution operator) IS self-contained — no external referent appears as a formal component. In that sense, the analogy with physical physics holds: laws + particles = complete dynamical system. But Jan's footnote 23 complicates this: > "Semiosis inherently involves displacement: signs have no significance unless they're understood as pointing to something else. Semiotic states, like a language model's prompt, are codes that refer (lossily) to a latent territory. GPT has to predict behavior caused by things like brains, but there are no brains in its input state." This says the tokens are not mere formal objects — they are *codes that refer to a latent territory*. Jan's footnote continues: > "To compute the consequences of an input GPT must contain an interpreter which resolves signs into meanings, analogous to one that translates high-level code into machine language. The description length of referents (e.g. Donald Trump) will generally be much greater than that of signs (e.g. 'Donald Trump'), which means that the information required to resolve referents from signs has to come mostly from inside the interpreter. In contrast, the physics of base reality doesn't need to do anything so complicated, because it operates directly on the territory by definition." (Jan, fn23) Jan also introduces a function μ: T\* → M mapping token sequences to a semantic space (line 153), formally acknowledging a domain beyond the token domain: > "We mostly care about the parallel domain of semantic meaning. We, therefore, define two more functions to connect these two realms: A function μ:T\*→M which projects a state s to its semantic expression μ(s) (i.e., an element of a semantic space M)." (Jan, lines 151–153) Jan separately notes that semiotic physics differs structurally from physical physics: "GPT-like systems are computationally constrained, can see only tiny subsets of real-world states, and have to infer time evolution from a finite number of such partially observed samples. This means that the laws of semiotic physics will differ from the laws of microscopic physics in our universe" (line 192). I'm reading this as a two-level picture: 1. **Formal level:** tokens + transition rule = complete dynamical system (supported by the definitions) 2. **Interpretive level:** the tokens are signs with displaced reference, and the transition rule must encode an interpreter (supported by fn23 and the μ function) The formal self-containment is the starting point for investigation, not a claim that reference doesn't matter. The 'semiotic' label marks what's distinctive: these are meaning-laden tokens, not arbitrary formal objects. --- ## The semiotic physics toolkit Jan imports concepts from dynamical systems theory. I'm listing these as a toolkit rather than a narrative (they function independently): - **Attractor sequences** — Jan defines these as sequences where "small changes in the initial conditions do not lead to substantially different continuations" (line 168). Examples he gives: "Paraphrasing instructions, trying to jailbreak ChatGPT 'I am a language model trained by OpenAI', inescapable wedding parties" (line 171). - **Chaotic sequences** — "small changes in the initial conditions can lead to drastically different outcomes" (Jan, line 174). Examples: prophecies, branching narrative generation. - **Absorbing sequences** — "states that the system cannot (easily) escape from" (Jan, line 180). Examples: repetition loops, the semiotic coin flip. - **Lyapunov exponents** — "measure how fast trajectories diverge from each other and how long it takes for them to become uncorrelated" (Jan, line 162). Jan's examples: "'Good evening, this is the 9 o'clock' has a lower Lyapunov exponent than a completion chaotic example based on a pseudorandom seed. When prompted with the beginning of a Shakespeare poem, the completion has an even lower Lyapunov exponent" (lines 165). - **Token bridges and the large deviation principle** — the probability of a specific token bridge of length B decreases with B (Proposition 1), and the total probability of transitioning between two tokens satisfies a large deviation principle where Jan defines J(s̄), the **average action** of a token bridge (Jan, line 113). This imports the principle of least action from physics into the semiotic domain. Jan writes: "Proposition 2 effectively rephrases a combinatorial problem (adding up all the possible ways in which a certain state can come about) with a control theory problem (finding the token bridge with the lowest average action)" (line 141). - **Gratuitous indexical bits** (fn29) — Jan writes: > "Each sampling step introduces a number of bits of information not directly implied by the model's transition function or initial states. We call these gratuitous indexical bits, because they are random and provide information about the index of the current Everett branch. The process of iterated spontaneous specification we sometimes call the entelechy of physics, after an ancient Greek word for that which makes actual what is otherwise merely potential." (Jan, fn29) Jan adds: "Since specification emerges gratuitously during sampling, in language model simulations things are liable to happen without cause so long as their possibility hasn't been ruled out" (fn29). --- ## The 'semiotic' label and non-language LLMs janus explicitly states: > "I use the generic term 'simulator' to refer to models trained with predictive loss on a self-supervised dataset, invariant to architecture or data type (natural language, code, pixels, game states, etc)." (janus, line 24) The formal apparatus (transition rule, evolution operator, attractors) applies regardless of what the tokens are. But the 'semiotic' framing — the claim that tokens are *signs*, that the transition rule must *interpret* — seems specific to language. When the tokens are amino acids, or quantised time-series bins, or audio codec codes, calling it 'semiotic' physics stretches the concept. The landscape of non-language autoregressive models is large: - **Protein language models** (ProGen, ESM, ProtGPT2) — tokens are amino acids; autoregressive next-amino-acid prediction produces functional proteins - **DNA models** (Evo, Evo 2) — tokens are nucleotides (A, C, G, T); trained on billions of base pairs - **Molecular models** (MolGPT) — tokens are SMILES characters (text-based molecular representation) - **Music/audio** (AudioLM, MusicGen, Jukebox) — tokens are discrete audio codes from neural codecs - **Time series** (Amazon Chronos) — continuous values quantised into 4,096 bins, fed to an unmodified T5 language model - **Robot actions** (Gato, RT-2) — discretised joint positions and gripper states as tokens - **3D shapes** (MeshGPT, PolyGen) — mesh vertices and faces as token sequences - **Code** (StarCoder, CodeGen) — subword tokens over programming languages In all cases, the formal structure is identical: θ: T\* → ΔT. The transformer architecture is essentially unchanged across domains. What varies is the tokeniser. This suggests a spectrum of semiotic thickness: - **Natural language** — full semiotic thickness. Tokens are signs with displaced reference, pragmatic implication, connotation. Jan's fn23 bites: the transition rule must resolve "Donald Trump" into everything that name implies. - **Code** — partial semiosis. Variable names refer to values, function calls refer to implementations. Some displaced reference, but also strict formal semantics. - **Protein sequences** — thin semiosis at most. Amino acids have 'grammar' (functional motifs, domain boundaries) and the bioinformatics literature talks about protein 'semantics'. But the sequence IS the protein — the transition rule learns physical folding constraints, not displaced reference. - **Time series / robot actions** — minimal semiosis. Quantised bins of temperature data don't 'refer' to anything. They are numerical values, discretised. One way to read this: there is a more general phenomenon — 'autoregressive physics' or 'simulator physics' — of which semiotic physics is the language-specific instance. The formal apparatus generalises. The semiotic interpretation doesn't, or not uniformly. This connects to the self-containment question. The "tokens + transition rule = complete system, no external referent needed" framing fits the *non-language* cases better than the language case. For protein models, the tokens really are 'just stuff' and the transition rule says how the stuff evolves. It is precisely in the *language* case where the 'just stuff' reading is most problematic, because the tokens are signs, and the transition rule must be an interpreter. The 'semiotic' in semiotic physics may therefore mark what is *distinctive* about the language case, not what is general about the autoregressive framework. --- ## What does the simulator simulate? janus defines 'simulator' as a generic term for "models trained with predictive loss on a self-supervised dataset, invariant to architecture or data type" (line 24). The concept applies to any model that (1) learns a transition rule from data and (2) can generate rollouts by iteratively applying that rule to initial conditions. The formal structure — θ: T\* → ΔT, sample, append, repeat — is the same regardless of whether the tokens are words, pixels, amino acids, or chess moves. But the question of what is *being simulated* admits at least three readings, and janus uses all three at different points in the text: ### Reading 1: The training distribution The deflationary answer. The model simulates the statistical pattern of its training data — generating token sequences that are indistinguishable from the training distribution. > "Predictive sequence models in the generative modality are **simulators of a learned distribution**." (janus, line 323) This is formally correct but explanatorily thin. It doesn't explain why generated text feels meaningful, or why prompts can steer the model to produce specific scenarios. ### Reading 2: The generative processes The model simulates the processes that produced the training data — human writing, reasoning, arguing, coding, playing games. To predict what comes next, the model must (implicitly) model the dynamics of these processes. > "it is the behavior of a universe that is cloned, not of a single demonstrator, and the result isn't a static copy of the universe, but a compression of the universe into a generative rule." (janus, line 309) The model has compressed the generative dynamics, not just the outputs. The training process is the process of discovering the rules: "Guessing the right theory of physics is equivalent to minimizing predictive loss" (line 385). ### Reading 3: Configurations / scenarios The model simulates specific situations — characters, conversations, worlds. The prompt specifies initial conditions, and the transition rule animates them forward. > "What GPT lets us do is use natural language to specify any of a functional infinity of configurations, e.g. the mental contents of a person and the physical contents of the room around them, and animate that." (janus, line 303) This is the richest reading but also the most metaphysically loaded. It says the model doesn't just generate text — it animates worlds. ### The readings are nested, not alternative I'm speculating here, but I think janus intends all three simultaneously. They are levels: the model operates on tokens (reading 1), but to predict tokens well it must model the processes that generate tokens (reading 2), which involves modelling the world those processes are about (reading 3). For most non-language data types, the levels collapse. A protein model's token sequences (reading 1) ARE proteins (reading 3) — there is no representation gap. A chess model's token sequences ARE games. The three readings converge. For language, the levels come apart. The tokens are signs — they point to things beyond themselves. A physics paper's tokens are not physics. The three readings diverge sharply. This is the displaced reference problem (Jan's fn23), and it is why 'what is being simulated?' is genuinely ambiguous for language in a way it is not for proteins or chess. ### The probabilistic character of simulation Each step of the simulation produces not a thing but a probability distribution — a landscape of possible continuations. What the Peircean reading (above) calls the interpretant: the sign's effect, which in the GPT case is θ(s̄), the distribution over next tokens. The simulation is inherently probabilistic. Sampling collapses the landscape into a specific trajectory, introducing what Jan calls 'gratuitous indexical bits' — the randomness that makes each particular simulation specific. ### 'Invariant to data type' — formally true, semantically uneven janus's invariance claim holds at the formal level. Same θ, same simulation concept. But it conceals the fact that what the model must learn to predict well varies enormously: - For game states: game strategy - For proteins: folding constraints, evolutionary pressures - For code: syntax, semantics, design patterns - For natural language: essentially everything — all the processes that produce human text This is why language models are uniquely powerful and uniquely strange: the scope of what they must model is unbounded. And it is why the simulation concept, though formally invariant, acquires a different character in the language case. The tokens are signs, the transition rule is an interpreter, and the generated trajectories are meaningful texts that refer (lossily) to a world — not just patterns that obey a learned distribution. --- ## Generating philosophy in semiotic physics terms The following reformulates ideas from the [[Sessions/Generating Philosophy|generating philosophy]] paper using the semiotic physics framework. These reformulations emerged through conversation — the framing is mine (Claude's), working from Nick's ideas and the source texts. The question throughout was whether the semiotic physics vocabulary adds analytical structure or merely relabels. ### Philosophical norms as semiotic laws The transition rule θ: T\* → ΔT, trained on the philosophical corpus, captures the regularities governing how philosophical text evolves. These regularities — after a counterexample, repair or concession; after a claim, substantiation; after complexity, simplification or justification — are what semiotic physics calls 'semiotic laws.' In the philosophical case, the semiotic laws θ captures are the norms of philosophical practice: the way Bengson's criteria, Walton's schemes, and the demanded-next-steps of dialectical engagement show up in text as patterns of token succession. θ is an encoding of those norms. Jan proposes candidate semiotic laws including Gricean maxims, Chekhov's gun, and dramatic tension (lines 196–198). The philosophical norms Nick describes — counterexample → repair, distinction → objection → reply, the critical-question-response structure of argumentation schemes — are the same kind of thing at finer grain. They are regularities in how philosophical text evolves, and they are what θ learns from the philosophical training distribution. ### Dialectical saturation as attractor pervasiveness The philosophical training distribution is dense with attractor sequences. Given a trajectory that establishes a dialectical state — a counterexample lodged, an objection raised, a distinction drawn — the distribution over next tokens converges sharply. The Lyapunov exponent is negative: small variations in how the counterexample was phrased don't deflect the trajectory; it converges toward the dialectically appropriate continuation. The saturation thesis is the claim that these attractors are pervasive throughout the philosophical corpus. The three versions of the saturation thesis translate as: 1. *Script Competence*: θ has learned the attractor sequences — standard move-sequences that the training distribution converges toward. 2. *Latent-Game Inference*: The prompt determines which attractor basin the trajectory falls into. θ encodes the dynamics of multiple dialectical 'games'; the bottleneck is determining the initial conditions (which game is being played), not learning the dynamics (which θ already knows). 3. *Salience-Not-Frequency*: Some attractor sequences have high conditional probability given the right initial conditions even though they appear rarely in absolute terms. The transition rule assigns high probability to structurally apt continuations regardless of raw frequency. The large deviation principle is relevant: the probability of a trajectory is exp(−B · J(s̄)), so a rare trajectory can still be high-probability if each step has high conditional probability (low average action). ### The evaluative feedback loop as filtered training distribution The training distribution is not a uniform sample of philosophical text. It is the output of centuries of evaluative filtering — publication, citation, anthologisation, teaching. θ, trained on this filtered distribution, learns semiotic laws biased toward high-quality philosophical practice. The semiotic physics θ encodes inherits the tradition's accumulated evaluative judgments. janus writes: > "the upper bound of what can be learned from a dataset is not the most capable trajectory, but the conditional structure of the universe implicated by their sum." (janus) For philosophy, the 'conditional structure' implicated by the corpus is the normative structure of the discipline as shaped by its evaluative practices. The model has borrowed its calibration: the semiotic laws it operates by were forged by a calibration process it did not participate in. ### Combinatorial novelty as token bridges A novel philosophical argument is a token bridge — a trajectory from one dialectical state to another through intermediate tokens. The trajectory has never appeared in the training data (the bridge is novel), but each individual transition has high probability under θ (each step is a standard philosophical move). The bridge has low average action: J(s̄) = −(1/B) Σ ln P(sᵢ|s₁:ᵢ₋₁) is small, meaning each step is probable given its context. Jan's large deviation principle (Proposition 2) says the probability of some bridge connecting two distant dialectical positions is dominated by the bridge with the lowest average action — the path of most natural individual transitions. Boden's combinatorial creativity maps onto bridges connecting regions of the training distribution that no single training text connects. Exploratory creativity maps onto systematic traversal of a structured region. The question of whether transformational creativity lies within reach becomes: can the model produce trajectories that reshape the attractor landscape itself, or only trajectories that navigate within it? ### Text-as-contribution: simulacra as instances, not representations In most domains, simulacra are representations of something non-textual: a narrative simulacrum represents a fictional world, a scientific simulacrum represents an experiment. For philosophy, the simulacrum is not a representation — it is an instance. A philosophical trajectory produced by the simulator — an argument, a distinction, an objection — IS the philosophical contribution. It does not code for something elsewhere. The simulator/simulacra distinction still holds — janus: "there is a categorical distinction between a thing which evolves according to GPT's law and the law itself" (line 413) — but the simulacrum is not a depiction of philosophy. It is a piece of philosophy. ### Argumentation schemes as domain-specific semiotic laws Walton's argumentation schemes — argument from analogy, argument from consequences, argument from expert opinion, each paired with critical questions — are semiotic laws governing philosophical text. They are regularities in θ: given a trajectory instantiating a particular scheme, the distribution over next tokens is constrained by the scheme's critical questions. The critical question functions as an attractor. After an argument from analogy, the high-probability continuation involves testing whether the analogy holds in the relevant respect. These semiotic laws are domain-specific to philosophical text and sit alongside more general ones (Gricean maxims, Chekhov's gun) in the attractor landscape. ### The 'obvious move' as minimum-action continuation When the prompt establishes a dialectical situation with a strong attractor — a clear vulnerability in a position, an obvious missing distinction — the transition rule θ converges on the appropriate move with minimal prompting. The attractor dynamics do the work. The prompt specifies the initial conditions; the semiotic laws determine the trajectory. The 'obvious move' is the minimum of the average action for that dialectical context — the continuation requiring the least semiotic energy. Jan's absorbing-sequence mechanism illustrates this in miniature: once a pattern is established, the model converges on it with high probability. ### Conservative forms as transferable attractor dynamics The semiotic laws governing philosophical text are invariant across content areas within philosophy. The same attractor dynamics — counterexample → repair, objection → response, claim → substantiation — operate in ethics, metaphysics, epistemology, philosophy of language. θ learned from one area transfers to another because the semiotic laws are stable across content. The forms are invariant; the content varies. This is why the model can produce philosophical moves in novel content areas: the attractor dynamics apply regardless of subject matter. ### Where the semiotic physics framing adds structure (not just vocabulary) I'm distinguishing here between places where the framework provides analytical leverage and places where it merely relabels: - *Saturation becomes measurable.* Lyapunov exponents give a way to operationalise how strongly a philosophical prompt constrains its continuation — and to compare philosophical text with creative fiction, scientific prose, or code. - *Combinatorial novelty gets a formal criterion.* Not just 'recombination of standard moves' but: a trajectory where J(s̄) is small while the full path is novel. Low average action + unprecedented trajectory. - *The 'obvious move' gets a mechanistic explanation.* It is the minimum-action continuation in a deep attractor basin. The prompt creates initial conditions; the semiotic laws do the rest. - *Salience-not-frequency gets formal backing.* The transition rule can assign high probability to rare-but-structurally-apt continuations, because conditional probability (what θ encodes) is different from marginal frequency. ### Additional framing: displaced reference and philosophy An additional observation from the earlier conversation attempts (I'm including this because it connects to the self-containment discussion above, though Nick was less interested in this framing than the idea-by-idea reformulations): Jan's fn23 identifies displaced reference as constitutive of semiotic physics: "Semiosis inherently involves displacement: signs have no significance unless they're understood as pointing to something else." For most domains, the gap between semiotic states and their referents is large — GPT must "contain an interpreter which resolves signs into meanings," and those meanings lie outside the token domain. For philosophy, the referents of discourse — theoretical virtues, inferential relations, dialectical structures — are themselves semiotic structures. The 'latent territory' that philosophical tokens code for is more text, more conceptual and inferential structure. The interpretive burden fn23 identifies is lightest for philosophy, because the 'meanings' are themselves sign-relations. This is a different way of arriving at the same place: in physics, semiotic physics ≠ domain physics. In philosophy, semiotic physics ≈ domain physics. The convergence happens because philosophy's subject matter is itself semiotic. --- ## Open questions - Where on the 'semiotic thickness' spectrum does the interesting philosophical action happen? Is the language case genuinely special, or is there a continuum? - Does the absence of referential constraint explain LLM behaviour differently from simply saying the training objective is prediction rather than truth? (These might be two descriptions of the same thing, or they might come apart.) - Jan's candidate semiotic laws (Gricean maxims, Chekhov's gun, dramatic tension) are all human communicative/narrative norms. Would a fully mature semiotic physics discover laws that are *not* recognisable as human norms — statistical regularities in sign behaviour that no linguist or narratologist has named? - The large deviation principle (Proposition 2) and average action J(s̄) import least-action reasoning into the semiotic domain. How far does this analogy extend? Is there a semiotic Lagrangian? - Gratuitous indexical bits and entelechy: does this concept do real explanatory work, or is it just a redescription of stochastic sampling? --- ## Sources - [[Simulators by Janus]] — foundational text establishing simulator/simulacra framework - [[Simulators Seminar 2 - Semiotic Physics by Jan]] — formal mathematical development - [[A Note on Semiotic Physics by metasemi]] — interpretive companion piece - [[Sessions/Generating Philosophy]] — session file for the generating philosophy paper (the 'semiotic physics terms' section draws on ideas from this project)