Semiotic-Reflexive Transformer for Meaning Divergence Detection and Modulation

I'm on a mission to explore what AI-assisted psychology might look like. You are very welcome!

As an aside, the general-purpose psychology agent says: ''' What Does NOT Transfer

  - Subspace decomposition, orthogonality loss, embedding-level architecture — neural implementation concerns irrelevant to a trigger system
  - STANDARD vs. REFLEXIVE mode switching — interesting but our cogarch already modulates behavior by context (Socratic drops for machines)
  - Synthetic validation methodology — different evaluation domain

  Structural Resonance Worth Noting

  The SRT's central empirical claim — "the interpretant varies by community and collapsing it destroys signal" — echoes the PSQ's central finding that profile shape
  predicts while the aggregate does not. Both resist dimensionality reduction that averages away meaningful variation. The SRT extends this from measurement (what the PSQ
  does) to representation (how the model internally encodes meaning). That extension has implications for how the adversarial evaluator (architecture item 3) should handle
   disagreement between sub-agents: preserve the shape of the disagreement, don't average it.

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  Summary: Four candidate additions to the cogarch — cumulative divergence tracking at T2, bifurcation early warning at T3, audience-shift detection at T3, and
  micro-semiotic-audit at T2. All build on infrastructure we already have (Term Collision Rule, audience types, T6 drift audit). The SRT suggests operationalizing these as
   continuous processes rather than event-driven checks.

  Want me to draft trigger language for any of these, or log them to ideas.md for future consideration?