wuyang
67 lines
1.8 KiB
Markdown
67 lines
1.8 KiB
Markdown
# Paper: TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory
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---
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type: paper
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title: "TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory"
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authors: Tianyu Yang, Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni, Srinivas Chappidi
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year: 2026
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venue: arXiv
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url: https://arxiv.org/abs/2606.25161
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code_url:
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source: arxiv
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collected_at: 2026-07-08
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published_at: 2026-06-23
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updated_at: 2026-06-23
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status: skimmed
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relevance: high
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topics:
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- agent-evaluation
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- computer-use
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- memory
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- rag
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- reasoning
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- tool-use
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methods:
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- memory-transition-verification
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- transition-ranked-grpo
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- write-revise-prune
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benchmarks:
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- MemoryAgentBench
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- HaluMem
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- Mem-alpha
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models:
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-
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datasets:
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- cs.AI
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related_concepts:
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- memory-consolidation
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- transition-verification
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related_jobs:
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-
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related_experiments:
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- KC-001-agent-memory-pilot
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related_projects:
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- learning/agent-memory
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collection_score: 19
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collection_queries: agent-memory
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---
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## One-line Takeaway
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持久 memory 需要在每次 WRITE、REVISE、PRUNE 后检查 coverage、preservation 和 faithfulness，不能只看最终回答是否正确。
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## Pilot Skim
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- problem: 终局 reward 无法定位 memory 更新中的遗漏、破坏和幻觉，而错误会成为持续系统状态。
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- method: Memory Transition Verifier 为局部更新打分，并用 Transition-Ranked GRPO 优化更新策略。
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- evidence: 相比各错误类型最强 baseline，遗漏、破坏、幻觉分别减少 40.1%、79.1%、50.0%。
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- boundary: verifier 本身仍由冻结 LLM 实现；内容可靠不等于来源可信或具备行动权限。
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## Used In
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- [Agent Memory evidence matrix](../../learning/agent-memory/evidence-matrix.md)
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## Links
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- arXiv: https://arxiv.org/abs/2606.25161
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