What is the functional distinction between simple language models and multicellular organisms, and can generative AI harness that property to achieve long-range order?

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• The difference between simple language models and multicellular organisms goes beyond the substrate of intelligence considered.claim0.873
  Claim that topologies, not material substrates, account for differing organisational abilities
• Multiscale systems like those prevalent in biology are capable of organizing into complex patterns, whereas rudimentary language models are challenged by long sequences of outputs.claim0.817
  Conclusion about why biology organizes complexity well and flat LLMs do not
• "The model is based on generative communication. If two processes need to communicate, they don't exchange messages or share a variable; instead, the data producing process generates a new data object (called a tuple) and sets it adrift in a region called tuple space."concept0.790
• The large-scale morphological goals of an organism override and harness the local competencies of individual cells to adaptively navigate morphospace.claim0.779
  Describes top-down control in morphogenesis.
• Generative models are entailed by adaptive behavior, not explicitly encoded in brain statesclaim0.776
  Distinction from Bayesian brain: generative model is consequence of dynamics, not neural representation
• In some sense, this is the simplest language model we profoundly don't understand. And so it makes a natural target for our paper.quote0.774
  Articulates why a one-layer transformer with MLP is the appropriate starting target for mechanistic interpretability
• It's tricky, because for a typical language model the entity is sort of tricameral: the base simulator, the simulated simulator, and the simulated awareness.quote0.774
  Antra's earlier definitive statement of the tricameral model.
• Large language models show that linguistic competence can be dissociated from more basal agentic capacities (goal-directedness, multiscale competency, valence)claim0.774
  Used to argue AI minds must be assessed empirically via behavior science rather than via language interface alone.