Internal states significantly predicted motion of external subsystems; best prediction for the farthest subsystem (magenta circle, Fig 4d).

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Internal subsystem dynamics significantly predict external subsystem motion via canonical variates analysis (χ²-distributed, p=0.00052).finding0.844
  Empirical validation from primordial soup that internal states encode information about hidden environmental states.
• Internal states appear to encode Bayesian beliefs about hidden external states.claim0.780
  The inferential interpretation of internal dynamics.
• Slow subsystems were distributed among internal and external states, not segregated.finding0.772
  Observation about heterogeneous rate constants in the simulation.
• Functionally closed subsystems were rusticated to the periphery of the ensemble; no simulation produced a functionally closed internal state.finding0.764
  Emergent spatial segregation of closed subsystems.
• LLMs can compute meaningful functions over perturbations to their internal states, establishing introspection as a real but layer-dependent phenomenonclaim0.764
  Primary positive claim of the paper, grounded in strength comparison and localization results
• "Our findings demonstrate that LLMs can compute meaningful functions over perturbations to their internal states, establishing introspection as a real but layer-dependent phenomenon that merits further investigation."quote0.763
  Central thesis statement of the paper
• Flow states in AI correspond to centers in internal state that 'work' well.claim0.762
• Can external observers infer the anatomical goal state of a developing system by observing stress patterns?question0.761