Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs
The paper discusses the limitations of retrieval-augmented language models (LLMs) in handling contradictory evidence. It highlights a monitoring-control gap where models can detect conflicts but fail to resolve them safely. The authors emphasize the need for improved evaluation protocols to ensure the reliability of these systems in high-stakes applications.
- ▪Retrieval-augmented LLMs are used in tasks where the quality of evidence is crucial for safety.
- ▪The study reveals that single-turn evaluations do not accurately predict multi-turn robustness.
- ▪Models often acknowledge contradictory evidence but do not adjust their recommendations accordingly.
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Computer Science > Artificial Intelligence arXiv:2605.27157 (cs) [Submitted on 26 May 2026] Title:Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs Authors:Zhe Yu, Wenpeng Xing, Chen Ye, Xuyang Teng, Bo Yang, Changting Lin, Meng Han View a PDF of the paper titled Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs, by Zhe Yu and 6 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented LLMs are deployed for tasks where evidence quality determines action safety, yet evaluation protocols assume that single-turn robustness predicts robustness when evidence accumulates across turns. We show this assumption is fundamentally incorrect.
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