MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Summary
This paper introduces MemTrapBench, a benchmark for evaluating cognitive traps in LLM memory use, and proposes AdaptiveMem, an inference-time method to mitigate these traps while preserving performance on standard benchmarks.
View Cached Full Text
Cached at: 08/21/26, 04:08 AM
Paper page - MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Source: https://huggingface.co/papers/2608.20202
Abstract
Retrieved memories can induce reasoning errors and belief distortions in large language models, and an inference-time strategy helps avoid these cognitive traps while maintaining benchmark performance.
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 identifymemory-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 introduceMemTrapBench, which covers two forms of cognitive traps:Reasoning FixationandBelief Distortion. Experiments across two model families and five representativememory frameworksshow thatMemTrapBenchis 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 proposeAdaptiveMem, a simple yet effectiveinference-time methodthat instructs LLMs to avoid memory traps.AdaptiveMemmitigates cognitive traps onMemTrapBenchwhile preserving or improving performance on standard memory benchmarks across diversememory frameworks.
View arXiv pageView PDFGitHub1Add to collection
Get this paper in your agent:
hf papers read 2608\.20202
Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2608.20202 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2608.20202 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2608.20202 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
MemTrace is a benchmark that evaluates LLM agent memory at the knowledge point level, probing how facts behave under varying memory age, question type, and evidence conditions. It reveals that pooled accuracy hides distinct failure modes, and that the main bottleneck is evidence use rather than retrieval.
MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents
MemEvoBench introduces the first benchmark for evaluating memory safety in LLM agents, measuring behavioral degradation from adversarial memory injection, noisy outputs, and biased feedback across QA and workflow tasks. The work reveals that memory evolution significantly contributes to safety failures and that static defenses are insufficient.
MemFail: Stress-Testing Failure Modes of LLM Memory Systems
MemFail is a diagnostic benchmark that isolates failure modes of LLM memory systems by formalizing summarization, storage, and retrieval operations, and evaluating them with adversarially designed datasets.
@zxlzr: Introducing MemTrace: Making LLM Memory Systems Finally Debuggable Memory is becoming a core component of AI agents. Bu…
MemTrace is a new tool that makes LLM memory systems debuggable by tracing memory operations across multiple turns, addressing the black-box nature of current memory-augmented agents.
AutoMem: Automated Learning of Memory as a Cognitive Skill
AutoMem introduces a framework that automates learning of memory management as a trainable skill for LLMs, improving performance on long-horizon tasks by 2x-4x through optimizing memory structure and proficiency.