arXiv:2605.27157v1 Announce Type: new 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. Models exhibit a monitoring-control gap: they readily acknowledge contradictory evidence, yet this awareness fails to constrain their final recommendations - detecting epistemic conflict does not imply resolving it safely. Through a multi-turn document accumulation protocol across f
The increasing deployment of Retrieval-Augmented LLMs in critical applications makes understanding their limitations in multi-turn reasoning and epistemic conflict resolution more urgent.
This research reveals a fundamental flaw in how current LLMs handle contradictory information, impacting their reliability and safety in complex tasks requiring evidence synthesis over time.
The reliance on single-turn robustness metrics for evaluating LLMs will be challenged, pushing for more sophisticated multi-turn evaluation protocols that assess continuous evidence accumulation and conflict resolution.
- · AI Safety Researchers
- · Developers of advanced monitoring and control mechanisms for LLMs
- · Companies building alternative AI architectures
- · Developers deploying RAG LLMs without multi-turn evaluation
- · Applications requiring high-stakes, multi-turn reasoning from LLMs
Foundational assumptions about LLM robustness are being questioned, particularly for RAG models in real-world, dynamic environments.
This will likely lead to a new generation of LLM architectures or augmentation strategies specifically designed to manage and resolve epistemic conflicts over extended interactions.
Increased skepticism about autonomous AI agents could emerge until this monitoring-control gap is addressed, potentially slowing broader adoption in critical decision-making roles.
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Read at arXiv cs.AI