ConMem: Counterfactual Multi-Scenario Probing for Resolving LLM Memory Misalignment

Authors: Shuning Zhang , Tenghao Ji , Jingruo Chen , Eryue Xu , Bohao Chu , Xin Yi

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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.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.33432 [cs.AI]

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arXiv:2610.33432v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33432

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arXiv-issued DOI via DataCite (pending registration)

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From: Tenghao Ji [ view email ]

[v1]

Sat, 24 Oct 2026 04:16:37 UTC (634 KB)

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