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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27157 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | VTt_h66ydF5m |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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