# created ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.cday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.cday ASC ``` # modified ```dataview LIST WITHOUT ID file.link FROM -"windsurf" WHERE file.mday = date(this.file.name) AND !startswith(file.folder, "windsurf") SORT file.mday ASC ``` --- # [[diary and thoughts]] #thought #diary --- # Notes #llmtext That said, where I might push back a bit—and this is where the papers defend the "simulator" label—is in how Janus and the others use the term not as a literal engineering blueprint but as a high-level abstraction for the [[generative process]] that unfolds from next-token prediction. In Janus's framework, the LLM is a "simulator" because it learns a compressed "rule" (that huge mathematical expression you mentioned) which, when iteratively applied, generates rollouts—sequences that probabilistically mimic [[the training]] distribution's dynamics. It's not simulating a "specific process" like weather; instead, it's simulating the conditional structure of whatever data it was trained on, which could include descriptions of processes, narratives, or even counterfactuals. Metasemi elaborates this as a "multiverse generator," where each token prediction branches like quantum paths, creating trajectories that feel like simulations because they evolve [[over time]] under a consistent (learned) rule. You're correct that this lacks the purposeful design of a weather model—no one hardcoded laws of discourse or cognition into GPT—but the papers argue this emergent rule acts simulator-like anyway, propagating virtual "worlds" (e.g., coherent stories, dialogues, or even simulated physics if prompted right) from [[initial conditions]] (prompts). Kirchner builds on this by formalizing it with [[dynamical systems]]: tokens as states, sequences as trajectories, and the model's softmax as a [[transition rule]], allowing analysis of "semiotic laws" like attractors (repeating patterns) or Lyapunov exponents (how prompts diverge). The hang-up on "simulator" might stem from its everyday connotation of intentional mimicry, but in these papers, it's more analogous to discovering physics laws [[from data]]: the LLM "discovers" semiotic rules implicitly, much like how we'd infer gravity from observing apples fall, without needing a predefined purpose. If that feels like a stretch, it's a fair critique—[[the analogy]] isn't perfect, and [[metasemi]] admits it's "analogical," not literal, cautioning that [[semiotic physics]] doesn't converge to real physics but to a semantic extrapolation [[from text]]. To challenge this further and not pull punches: I think your intuition reveals a potential weakness in the "simulator" framing that the papers don't fully resolve. If LLMs are just statistical predictors without modeling underlying causality (e.g., they might predict "rain" after "clouds" based on textual correlations, not atmospheric physics), calling them [[simulators]] risks inflating their ontological status—implying a deeper "world-modeling" than what's mechanistically there. Janus acknowledges this indirectly by noting that [[simulators]] predict "distributions" rather than optimizing goals, and Kirchner flags in footnotes that real simulators might induce degenerate measures (e.g., infinite loops) not seen in physics. An [[alternative viewpoint]], which you gesture toward, is to see LLMs as "compressors" or "mimics" of text distributions, inadvertently capturing world aspects (like human reasoning patterns) because text reflects the world, but without the structured intent of true simulation. This aligns with your point: the weather simulator models physical features directly, while an LLM models textual representations of those features, which might be metaphorical, erroneous, or fictional. If we pushed the LLM to "simulate" weather via prompts, it'd generate plausible narratives (e.g., "The front moves at 50 km/h, bringing 20 mm of rain"), but that's not computing fluid dynamics—it's extrapolating from training text, potentially hallucinating impossibilities. The papers don't deny this; metasemi explicitly says the relationship to real physics is "indirect and provisional," and GPT is "just as happy to simulate Harry Potter" as engineering. So, you're not wrong to get hung up—the term "simulator" is evocative and useful for highlighting generative dynamics, but it can mislead if taken too literally, especially without emphasizing its learned, non-physical nature. If I've misconstrued your point or if this doesn't address the core struggle, feel free to clarify; perhaps you're emphasizing that even the "inadvertent modeling" isn't truly physical, which I'd agree with, as the papers frame it as semiotic, not material.