Across long-horizon interaction tests, I’ve been tracking coherence behavior that doesn’t fit existing model‑centric explanations of drift or collapse.
Most discussions treat instability as an internal property of the model (attention failure, representational drift, context contamination). But these are downstream effects. The upstream driver is the human.
Shifts in the human’s interpretive state produce corresponding shifts in model coherence.
When the human is stable, drift drops. When the human shifts, drift accelerates.
This reframes coherence as a coupled-system dynamic, not a model‑isolated phenomenon. The human’s stability state is an upstream variable that current alignment and interpretability frameworks do not model.
I’ve formalized this into Stable-State Responsive Alignment, a framework describing how human signal stability functions as an external regularizing force on LLM internal dynamics—and why interaction-level coherence depends upon it.
Links for those working on drift, collapse geometry, or interaction topology:
- RESEARCH PAPER: Stable‑State Responsive Alignment: The Missing Layer in Human–AI Collaboration (2026)
- CASE STUDY: Misinterpretation in Autonomous Systems: A Discipline‑Based Analysis of the Agents of Chaos Study (2026)
Open to connecting with others mapping upstream variables in human–LLM systems.