MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
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 Beli...
| Authors | Mengru Wang, Haozhe Luo, Zhenqian Xu et al. |
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| Primary category | cs.AI |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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