{
  "schema_version": "1.1",
  "id": "archive:https://arxiv.org/abs/2608.07438v1",
  "slug": "2608-07438v1-117tf7f",
  "url": "https://feed7.dev/p/2608-07438v1-117tf7f",
  "title": "PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents",
  "why_included": "PsychoAgent separates factual and affective memory, then reranks relevant memories by salience. It retrieved more conflict-critical context, but output-quality differences were not significant.",
  "summary": "PsychoAgent filters affective memories by semantic relevance, reranks them by salience, and combines them with factual memory through an executive controller. In **three conflict scenarios**, retrieval scored **0.933**, versus 0.500 and 0.667 for two baselines.",
  "practical_implication": "For memory systems where importance is not captured by similarity alone, test a second ranking signal after relevance filtering. Keeping factual and affective stores separate also makes the retrieval policy easier to inspect.",
  "agent_context": "PsychoAgent filters affective memories by semantic relevance, reranks them by salience, and combines them with factual memory through an executive controller. In **three conflict scenarios**, retrieval scored **0.933**, versus 0.500 and 0.667 for two baselines.\n\nFor memory systems where importance is not captured by similarity alone, test a second ranking signal after relevance filtering. Keeping factual and affective stores separate also makes the retrieval policy easier to inspect.\n\nFive blinded raters assessed **27 outputs**. The full system led by **+0.22 SD** after within-rater standardization, but corrected pairwise differences were not statistically significant, and retrieval incurred a small semantic-similarity cost.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2608.07438v1",
    "published_at": "2026-08-07T17:22:29.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "research"
  ],
  "topics": [
    "agent-memory",
    "retrieval",
    "agent-reliability"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "Five blinded raters assessed **27 outputs**. The full system led by **+0.22 SD** after within-rater standardization, but corrected pairwise differences were not statistically significant, and retrieval incurred a small semantic-similarity cost."
  ],
  "connected_context": {
    "meaning": "This adds affective salience as an inspectable second-stage ranking signal after semantic relevance and supports separating memory types before an executive controller combines them. It extends value-aware retrieval beyond generic utility scores, but the small, statistically inconclusive output study narrows the claim to a promising architecture and retrieval result rather than demonstrated behavioral reliability.",
    "corpus_size": 409,
    "generated_at": "2026-08-10T10:05:42.678Z",
    "connections": [
      {
        "title": "MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2607.25992v1",
        "feed7_url": "https://feed7.dev/p/2607-25992v1-0q2kxfl",
        "reason": "MemLens makes memory value observable at record level; PsychoAgent provides a more specific implementation consequence by applying salience only after relevance filtering and keeping affective and factual stores separate."
      },
      {
        "title": "Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=H7puB0RwJMM",
        "feed7_url": "https://feed7.dev/p/citation-needed-provenance-for-llm-built-knowledge-graphs-daniel-chalef-1iob5t8",
        "reason": "Separate memory stores and reranking improve policy inspectability, but provenance remains necessary to explain which source memories produced a combined response and to support correction or deletion."
      },
      {
        "title": "Wearing the Agent: From Group Chats to Glasses — Sai Krishna Rallabandi",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=s67bE2Ur3bY",
        "feed7_url": "https://feed7.dev/p/wearing-the-agent-from-group-chats-to-glasses-sai-krishna-rallabandi-102hk49",
        "reason": "Affective salience could increase retrieval priority without establishing disclosure permission; the shared-agent candidate therefore adds a governance prerequisite of per-user privacy, audience filtering, and silence policies."
      },
      {
        "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": "PsychoAgent is a concrete memory and control architecture within the taxonomy’s broader durable-state and adaptation gaps, while its limited evaluation confirms that addressing one gap does not establish overall agent reliability."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-07T17:22:29.000Z",
  "modified_at": "2026-08-07T17:22:29.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2608-07438v1-117tf7f",
    "json": "https://feed7.dev/p/2608-07438v1-117tf7f.json",
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