# Paper: TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory

---
type: paper
title: "TRUSTMEM: Learning Trustworthy Memory Consolidation for LLM Agents with Long-Term Memory"
authors: Tianyu Yang, Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni, Srinivas Chappidi
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2606.25161
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-06-23
updated_at: 2026-06-23
status: skimmed
relevance: high
topics:
  - agent-evaluation
  - computer-use
  - memory
  - rag
  - reasoning
  - tool-use
methods:
  - memory-transition-verification
  - transition-ranked-grpo
  - write-revise-prune
benchmarks:
  - MemoryAgentBench
  - HaluMem
  - Mem-alpha
models:
  -
datasets:
  - cs.AI
related_concepts:
  - memory-consolidation
  - transition-verification
related_jobs:
  -
related_experiments:
  - KC-001-agent-memory-pilot
related_projects:
  - learning/agent-memory
collection_score: 19
collection_queries: agent-memory
---

## One-line Takeaway

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。

## Pilot Skim

- 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。
- method: Memory Transition Verifier 为局部更新打分，并用 Transition-Ranked GRPO 优化更新策略。
- 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%。
- 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。

## Used In

- [Agent Memory evidence matrix](../../learning/agent-memory/evidence-matrix.md)

## Links

- arXiv: https://arxiv.org/abs/2606.25161
