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

arXiv cs.AIOctober 27, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.33432v1 Announce Type: new 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{Co

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