MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use

A new research paper introduces 'MemTrapBench,' a benchmark designed to evaluate how large language models (LLMs) are susceptible to 'memory-induced cognitive traps.' These traps occur when faithfully recorded and relevant memories distort an LLM's reasoning or beliefs, leading to degraded performance on current tasks. The research focuses on two specific forms of these traps: Reasoning Fixation and Belief Distortion, highlighting a critical area beyond simple information retrieval in LLM development.

Why it matters

This research highlights a fundamental challenge in the reliability and trustworthiness of advanced AI systems, particularly large language models. Understanding and mitigating these cognitive traps are crucial for deploying LLMs in critical applications where accurate, unbiased reasoning is paramount, impacting overall strategic decision-making and operational effectiveness.

Key insights

• Existing LLM memory benchmarks primarily assess correct information extraction, storage, and retrieval.
• A significant gap exists in evaluating how retrieved memories influence model reasoning and task performance.
• The concept of 'memory-induced cognitive traps' is introduced, where accurate memories can still negatively impact model output.
• Two specific cognitive traps are identified: Reasoning Fixation (memories distorting reasoning) and Belief Distortion (memories altering beliefs).
• MemTrapBench is proposed as a systematic evaluation tool for these cognitive traps across different LLM families.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.20202