新闻
MeClear: Cooperative Game-Theoretic Attribution and Risk-Aware Memory Clearance for Long-Horizon LLM Agents
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- MeClear framework identifies and removes harmful memories from LLM agent context using game-theoretic attribution, improving task recovery by 25.5 percentage points.
- 为何重要
- Engineers building long-horizon LLM agents with persistent memory systems need better ways to prevent outdated or conflicting information from degrading performance.
- 注意
- Results shown on ten dialogue memory pools; unclear how well this generalizes to other agent types, domains, or whether computational cost of attribution is practical.
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