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  "path": "/t/interaction-stability-as-an-upstream-variable-in-long-horizon-llm-coherence/175902#post_1",
  "publishedAt": "2026-05-10T19:18:13.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "Stable‑State Responsive Alignment: The Missing Layer in Human–AI Collaboration",
    "Misinterpretation in Autonomous Systems: A Discipline‑Based Analysis of the Agents of Chaos Study"
  ],
  "textContent": "Across long-horizon interaction tests, I’ve been tracking coherence behavior that doesn’t fit existing model‑centric explanations of drift or collapse.\n\nMost 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**.\n\n> **Shifts in the human’s interpretive state produce corresponding shifts in model coherence.**\n>  **When the human is stable, drift drops. When the human shifts, drift accelerates.**\n\nThis 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.\n\nI’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.\n\nLinks for those working on drift, collapse geometry, or interaction topology:\n\n  * **RESEARCH PAPER:** Stable‑State Responsive Alignment: The Missing Layer in Human–AI Collaboration (2026)\n  * **CASE STUDY:** Misinterpretation in Autonomous Systems: A Discipline‑Based Analysis of the Agents of Chaos Study (2026)\n\n\n\nOpen to connecting with others mapping upstream variables in human–LLM systems.",
  "title": "Interaction Stability as an Upstream Variable in Long-Horizon LLM Coherence"
}