TEPA:撤销过期记忆,让语言智能体稳健应对冲突

TEPA: Revoking Stale Memories for Conflict-Robust Language Agents

精选理由

TEPA 这篇论文把记忆过期当成核心问题,用可撤销机制让智能体不被陈旧信息带偏。逆转测试里从 0.21 提到 0.95,效果很明显。

AI 摘要

论文提出 TEPA,一种可撤销证据记忆机制,将有效性设为记忆的显式状态。在受控隐藏机制漂移的 50 次实验中,完整逆转时 append-only 和 last-write-wins 得分均为 0.210,无记忆为 0.309,而 TEPA 达到 0.950。真实文件执行场景再现同一模式:append-only 为 0.203,无记忆为 0.298,TEPA 为 0.950。在干净 MemoryAgentBench SH-6k 上,TEPA 匹配强 last-write-wins 缓存。多跳和长上下文边界测试暴露出检索链与上下文选择瓶颈,证明生命周期撤销是智能体记忆的核心操作。

原文 · arXiv cs.AI

TEPA: Revoking Stale Memories for Conflict-Robust Language Agents

Long-term memory enables language agents to reuse past facts, preferences, and task experience. Persistence also creates a central falsifiability problem: when the world changes, stale memories can remain retrievable and pollute the prompt. We characterize this failure mode as memory pollution: degradation caused by active memories that newer conflicting evidence has superseded. We introduce TEPA, a revocable evidence-memory mechanism that makes validity an explicit state of memory. TEPA represents observations as keyed precedents and revokes active precedents when fresh evidence contradicts them under the same key, allowing retrieval to draw from current evidence while preserving revoked history for audit. Across controlled hidden-regime drift, real file-backed executable drift, and preference-update streams, revocation prevents stale active memory from remaining in the retrieval set after reversal. In controlled drift over 50 seeds, append-only and last-write-wins memory fell below no memory during full reversal (append-only and last-write-wins both 0.210, no memory 0.309, TEPA 0.950), and the same pattern reproduced under real file execution (append-only 0.203, no memory 0.298, TEPA 0.950). On clean MemoryAgentBench SH-6k, TEPA matches a strong last-write-wins cache, confirming that current-key replacement is the decisive operation for single-hop fact consolidation. Boundary tests on multi-hop and very long-context MemoryAgentBench settings expose retrieval-chain and context-selection bottlenecks beyond fact-level validity tracking. Together, these results establish lifecycle revocation as a core memory operation for agents that must falsify, audit, and later re-promote evolving knowledge.