MemTrapBench tests cognitive traps in LLM memory use
The benchmark finds memory can degrade current-task reasoning even when retrieved records are relevant.
Why it matters
The work highlights a gap in memory evaluation: correct storage and retrieval do not guarantee better reasoning. It suggests memory systems need safeguards against relevant but misleading context before being deployed in long-term AI assistants.
The key points
- 1.MemTrapBench targets reasoning failures caused by retrieved memories.
- 2.All tested memory strategies lagged a no-memory setting.
- 3.AdaptiveMem mitigated traps at inference time.
Researchers introduced MemTrapBench, a benchmark for evaluating how retrieved memories can distort large language model reasoning on current tasks. The benchmark covers two failure modes, Reasoning Fixation and Belief Distortion. Experiments across two model families and five memory frameworks found that all evaluated memory strategies underperformed a no-memory setting, with the strongest methods dropping by more than 10%.
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Test memory-enabled agents against no-memory baselines and add instructions or filters to prevent fixation and belief distortion.
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