{
  "schema_version": "1.1",
  "id": "s13:https://arxiv.org/abs/2609.21997v1",
  "slug": "2609-21997v1-0yth274",
  "url": "https://feed7.dev/p/2609-21997v1-0yth274",
  "title": "Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents",
  "why_included": "Bayesian Chronicle Agents separate explicit belief state from generated speech, making persuasion strength configurable and multi-agent opinion changes auditable.",
  "summary": "Bayesian Chronicle Agents represent each stance as a probability and update it once per heard utterance. A single **κ prior-strength parameter** controls stubbornness, producing consensus, persistent disagreement, or committed-minority influence.",
  "practical_implication": "For multi-agent simulations, keep belief state outside the language model and let generation express rather than own that state. Persistent disagreement matched analytical fixed points at **R² 0.93–0.99**, and κ retained **perfect rank-order recovery across four models** after the language round-trip.",
  "agent_context": "Bayesian Chronicle Agents represent each stance as a probability and update it once per heard utterance. A single **κ prior-strength parameter** controls stubbornness, producing consensus, persistent disagreement, or committed-minority influence.\n\nFor multi-agent simulations, keep belief state outside the language model and let generation express rather than own that state. Persistent disagreement matched analytical fixed points at **R² 0.93–0.99**, and κ retained **perfect rank-order recovery across four models** after the language round-trip.\n\nThis controls a narrow form of opinion dynamics, not general agent reasoning or memory. The abstract does not show whether the same parameterization holds for richer beliefs, longer interactions, or tool-using agents.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2609.21997v1",
    "published_at": "2026-09-18T16:57:14.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "research"
  ],
  "topics": [
    "multi-agent",
    "agent-memory"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "This controls a narrow form of opinion dynamics, not general agent reasoning or memory. The abstract does not show whether the same parameterization holds for richer beliefs, longer interactions, or tool-using agents."
  ],
  "connected_context": {
    "meaning": "This turns one class of multi-agent memory into an explicit, inspectable state transition rather than prose remembered by each model. It reinforces the candidates’ broader external-state and harness-control pattern, while sharply narrowing the claim: a scalar prior-strength parameter can govern stylized stance dynamics, but does not establish a general memory format or coordination mechanism for long-horizon, tool-using agents.",
    "corpus_size": 831,
    "generated_at": "2026-09-21T09:04:18.336Z",
    "connections": [
      {
        "title": "huangruiteng/loopx",
        "source_name": "GitHub",
        "source_url": "https://github.com/huangruiteng/loopx",
        "feed7_url": "https://feed7.dev/p/loopx-0j0o7ux",
        "reason": "Both place durable control state in the harness rather than model context; the Bayesian layer supplies a mathematically constrained belief update, while LoopX covers broader goals, evidence, ownership, and gates."
      },
      {
        "title": "Handover of In-Context Learning State Across Session Boundaries",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.14528v1",
        "feed7_url": "https://feed7.dev/p/2608-14528v1-184x94t",
        "reason": "The handover work asks which task-relevant state must survive session boundaries; Bayesian Chronicle Agents provide a concrete narrow case where the preserved state is an explicit stance probability and prior strength."
      },
      {
        "title": "TauricResearch/TradingAgents",
        "source_name": "GitHub",
        "source_url": "https://github.com/TauricResearch/TradingAgents",
        "feed7_url": "https://feed7.dev/p/tradingagents-0on808i",
        "reason": "TradingAgents demonstrates role-based debate with persistent decisions, while this work makes opinion change during interaction externally controllable, suggesting a more inspectable alternative for belief evolution within such debates."
      },
      {
        "title": "A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AI",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.02553v1",
        "feed7_url": "https://feed7.dev/p/2608-02553v1-12y8joy",
        "reason": "The taxonomy separates durable state from other capability gaps; this work implements and validates one tightly scoped form of that state without claiming to solve monitoring, action control, adaptation, or general reasoning."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-09-18T16:57:14.000Z",
  "modified_at": "2026-09-18T16:57:14.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2609-21997v1-0yth274",
    "json": "https://feed7.dev/p/2609-21997v1-0yth274.json",
    "markdown": "https://feed7.dev/p/2609-21997v1-0yth274.md"
  }
}