{
  "schema_version": "1.1",
  "id": "s13:https://arxiv.org/abs/2609.09115v1",
  "slug": "2609-09115v1-1xq0kqg",
  "url": "https://feed7.dev/p/2609-09115v1-1xq0kqg",
  "title": "MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents",
  "why_included": "MeClear suppresses memories that hurt the current task, using cooperative attribution to expose conflicts that single-memory removal can miss, while leaving the persistent memory bank unchanged.",
  "summary": "MeClear scores memories by downstream utility using Leave One Out screening plus sampled Shapley attribution, then tests progressively filtered contexts. Across **10 long-dialogue memory pools**, it reached **85.9% target recall** and **82.3% task recovery**.",
  "practical_implication": "Builders of long-running agents should treat retrieval relevance as insufficient: retrieved memories can be stale, misleading, or mutually conflicting. Query-scoped suppression offers a pattern for recovering a task without deleting durable memory.",
  "agent_context": "MeClear scores memories by downstream utility using Leave One Out screening plus sampled Shapley attribution, then tests progressively filtered contexts. Across **10 long-dialogue memory pools**, it reached **85.9% target recall** and **82.3% task recovery**.\n\nBuilders of long-running agents should treat retrieval relevance as insufficient: retrieved memories can be stale, misleading, or mutually conflicting. Query-scoped suppression offers a pattern for recovering a task without deleting durable memory.\n\nThe reported recovery rate is a **25.5 percentage-point improvement over LOO baselines**, but the supplied material covers only the authors’ experiments. Runtime cost, sensitivity to the utility test, and performance on production memory stores remain open.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2609.09115v1",
    "published_at": "2026-09-08T17:46:00.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "coding"
  ],
  "topics": [
    "agent-memory",
    "retrieval",
    "agent-reliability"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The reported recovery rate is a **25.5 percentage-point improvement over LOO baselines**, but the supplied material covers only the authors’ experiments. Runtime cost, sensitivity to the utility test, and performance on production memory stores remain open."
  ],
  "connected_context": {
    "meaning": "MeClear shifts memory reliability from deciding what to retrieve or retain to testing whether retrieved combinations actually help the current task. Its query-scoped suppression provides a reversible response to stale, misleading, or conflicting context, strengthening prior warnings that relevant or reusable memories can still degrade performance. The reported gains support utility-aware filtering in controlled long dialogues, while production cost and portability remain unresolved.",
    "corpus_size": 713,
    "generated_at": "2026-09-09T10:12:26.227Z",
    "connections": [
      {
        "title": "Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.20274v1",
        "feed7_url": "https://feed7.dev/p/2608-20274v1-08ujug8",
        "reason": "Both show that retrieved memory can perform worse than having no memory; MeClear adds a query-time attribution and suppression mechanism for identifying and withholding harmful memories."
      },
      {
        "title": "PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.07438v1",
        "feed7_url": "https://feed7.dev/p/2608-07438v1-117tf7f",
        "reason": "PsychoAgent reranks memories for conflict-sensitive salience, whereas MeClear evaluates their downstream task utility and progressively removes harmful context, offering a complementary control after relevance ranking."
      },
      {
        "title": "Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2609.05339v1",
        "feed7_url": "https://feed7.dev/p/2609-05339v1-06dws1c",
        "reason": "Memory portability identifies model upgrades as a source of retrieval failure; MeClear could mitigate harmful migrated context at query time, but does not replace embedding rebuilds or migration testing."
      },
      {
        "title": "thedotmack/claude-mem",
        "source_name": "GitHub",
        "source_url": "https://github.com/thedotmack/claude-mem",
        "feed7_url": "https://feed7.dev/p/claude-mem-1uba088",
        "reason": "Claude-Mem supplies an inspectable persistent retrieval workflow but leaves contradiction handling unresolved; MeClear provides a compatible pattern for temporarily suppressing damaging retrieved observations without deleting durable history."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-09-08T17:46:00.000Z",
  "modified_at": "2026-09-08T17:46:00.000Z",
  "supersedes": [],
  "expires_at": null,
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    "html": "https://feed7.dev/p/2609-09115v1-1xq0kqg",
    "json": "https://feed7.dev/p/2609-09115v1-1xq0kqg.json",
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}