Flow states in AI correspond to centers in internal state that 'work' well.

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

• The flow of internal and active states can be described as a gradient descent on variational free energy.claim0.773
  A formal result from the proof.
• Internal states significantly predicted motion of external subsystems; best prediction for the farthest subsystem (magenta circle, Fig 4d).finding0.762
  Result of canonical variates analysis showing statistical dependency between internal states and external motion.
• Agents and their interactions are intrinsic to studying information flow in computation, not peripheral to classical information theory.claim0.755
  Central thesis: traditional static information theories fail to capture dynamic interaction necessary for understanding modern computing.
• AI should be understood as a companion and constituent participant in collective stress-care loops, not a mere tool.claim0.754
• Model attention patterns can map to and reveal something about contemplative and flow states.claim0.750
• We hypothesize that emotion states are more persistent because they correspond to genuinely stateful internal representations, not merely local surface contenthypothesis0.748
  Proposed explanation for why emotion probes are more persistent than variance-matched random probes
• In the case of AI, inferences from behavior to features of internal processes are often unreliable, as demonstrated by recent LLMs.claim0.747
  Justifies using internal indicators rather than behavioral tests for AI consciousness
• The structure which is created by a feeling for centers and by a conscious and deliberate aim towards the feeling of the whole, will often turn out to be an efficient structure.claim0.746
  Reiterated empirical observation from twenty years of practice.