ConMem：用反事实多场景探测解决 LLM 记忆错位问题
ConMem: Counterfactual Multi-Scenario Probing for Resolving LLM Memory Misalignment
AI 导读
研究者提出 ConMem 算法，将 LLM 智能体长期记忆的保留决策重构为前瞻性反事实因果推断，以解决现有记忆系统存储过度泛化、错误归因或隐私敏感信息的“记忆错位”问题。
正文 · 原文
Abstract:Long-term memory is critical for autonomous Large Language Model (LLM) agents executing multi-session, long-horizon tasks. However, existing memories often inappropriately store, retain, or retrieve over-generalized, misattributed, or privacy-sensitive information, a problem we term memory misalignment. To study this problem, we construct \textbf{MemAlign}, a benchmark comprising 1,079 curated memory cases grounded in synthetic, validated multi-session conversational contexts covering diverse misalignment taxonomies. We then propose ConMem, an algorithm that reframes retention decisions as prospective counterfactual causal inference. \textbf{ConMem} synthesizes counterfactual interaction scenarios to prospectively audit downstream intervention effects across three aspects: relevant task execution, irrelevant invariance checks, and risk-sensitive boundary shifts. Experiments show that ConMem outperforms other methods, achieving 75.16\% accuracy, 64.15\% balanced accuracy, and a Macro-F1 of 65.31\%, effectively mitigating over-retention biases without sacrificing personalization utility.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33432 [cs.AI] |
| (or arXiv:2610.33432v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33432 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tenghao Ji [view email]
[v1]
Sat, 24 Oct 2026 04:16:37 UTC (634 KB)
来源：arXiv cs.AI · arxiv.org