MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Score
76首次提出记忆认知陷阱评测框架，填补现有记忆基准仅关注存取准确性的盲区。
实验设计覆盖多模型多记忆框架，对比充分，结论清晰且具有可复现性。
提出的 AdaptiveMem 简单易用，可直接应用于现有记忆系统，提升可靠性。
当前正是长记忆与 Agent 应用爆发期，对记忆安全性的需求高度迫切。
来源为 arXiv 预印本，作者机构信息虽未披露，但方法表述严谨。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper addresses a critical blind spot in memory evaluation and proposes an effective mitigation method, with experiments across models and frameworks, showing strong novelty and practical relevance.
AI commentary
「这篇论文最值得看的是它揭示了“忠实记忆也能坑模型”的盲区，提醒做记忆系统的人别只盯着存取精度。对于正在构建长期 Agent 记忆的工程师和研究者来说，MemTrapBench 和 AdaptiveMem 都是绕不开的参考。」
Quick overview
Existing memory benchmarks for large language models mainly evaluate whether information is correctly extracted, stored, and retrieved, but largely overlook how retrieved memories reshape model reasoning and affect current-task performance. MemTrapBench focuses on two forms of memory-induced cognitive traps: Reasoning Fixation and Belief Distortion. Experiments show that all evaluated memory strategies underperform the no-memory setting on MemTrapBench, with even the strongest methods suffering drops of more than 10%. The authors propose AdaptiveMem, a simple yet effective inference-time method that mitigates cognitive traps while preserving or improving performance on standard memory benchmarks.
- MemTrapBench covers two types of cognitive traps: Reasoning Fixation and Belief Distortion, testing how memories negatively affect current reasoning.
- Experiments show that all existing memory frameworks underperform the no-memory baseline on MemTrapBench, with even the strongest dropping over 10%.
- AdaptiveMem uses inference-time instructions to steer models away from memory traps, mitigating harm without sacrificing standard memory benchmarks.
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