Can an LLM lose conceptual continuity while remaining coherent?

Hi Daniel, Your hypothesis on “Conceptual Drift” preceding visible incoherence is spot on. You are describing what I call the decoupling of Semantic Coherence (the text looks fine) from Dynamic Stability (the internal trajectory is fracturing). You mentioned that Stage 2 (Attention/Representation validation) is blocked by compute resources. I’ve been working on exactly this gap using hidden state dynamics rather than full attention maps, which is lighter but highly informative. In my recent audits (see Four Dynamical Regimes), I observed that models often enter a “Committed Non-Bifurcation” regime. Linguistically, they remain coherent and fluent, but internally, their inter-layer synchronization (kappa_sync) drops, and their trajectory becomes rigid. They are no longer “thinking” or exploring; they are just completing a pattern. This is the geometric signature of the drift you’re seeing in logit space. Why this matters for your RBRD framework: Your “Structural Observation Layer” is likely detecting the symptoms of this internal rigidity. If you could access even a sparse sample of hidden states (not full attention), you might see that these “abandoned topics” reappear not because of memory, but because the model’s dynamic regime shifts from Adaptive (exploring context) to Chaotic (grasping at distant logits) or Underactive (relying on strong priors). I’m currently mapping these internal regimes across 17 models. If you’re interested, I’d be curious to see if the “drift points” identified by your RBRD correlate with the instability spikes (ct_t) or synchronization drops I measure internally. It would be a powerful cross-validation between external trace analysis and internal dynamic monitoring. Great work on formalizing this. The distinction between “linguistic quality” and “structural stability” is the key to robust agents. Best,