MemTrapBench：大型語言模型記憶使用中的認知陷阱基準測試

摘要

記憶已成為大型語言模型的關鍵組成部分，使其能夠保留資訊並從長期互動中學習。然而，現有的記憶基準主要評估資訊是否被正確提取、儲存與檢索，卻在很大程度上忽略了被檢索的記憶如何重塑模型推理並影響當前任務的表現。我們辨識出記憶誘發的認知陷阱：即使是忠實記錄且語意相關的記憶，也可能扭曲模型的推理或信念，並降低當前任務的效能。為系統性評估這些失敗模式，我們提出 MemTrapBench，涵蓋兩種形式的認知陷阱：「推理固著」與「信念扭曲」。涵蓋兩個模型家族與五個具代表性記憶框架的實驗顯示，MemTrapBench 具有相當的挑戰性：所有被評估的記憶策略表現均不如無記憶設定，即便是最強的方法也出現超過 10% 的效能下降。為緩解這些認知陷阱，我們提出 AdaptiveMem，一種簡單而有效的推論時方法，透過指令引導大型語言模型避開記憶陷阱。AdaptiveMem 在 MemTrapBench 上緩解了認知陷阱，同時在各種記憶框架的標準記憶基準上維持或提升了表現。

English

Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing memory benchmarks mainly evaluate whether information is correctly extracted, stored, and retrieved, while largely overlooking how retrieved memories reshape model reasoning and affect performance on the current task. We identify memory-induced cognitive traps: even faithfully recorded and semantically relevant memories can distort model reasoning or beliefs and degrade current task performance. To systematically evaluate these failure modes, we introduce MemTrapBench, which covers two forms of cognitive traps: Reasoning Fixation and Belief Distortion. Experiments across two model families and five representative memory frameworks show that MemTrapBench is challenging: all evaluated memory strategies underperform the no-memory setting, with even the strongest methods suffering drops of more than 10%. To mitigate these cognitive traps, we propose AdaptiveMem, a simple yet effective inference-time method that instructs LLMs to avoid memory traps. AdaptiveMem mitigates cognitive traps on MemTrapBench while preserving or improving performance on standard memory benchmarks across diverse memory frameworks.