# DeepStress: Stress-Testing Deep Search Agents

**Authors:** Ismael Rousseau, Geraldine Damnati, Frederic Bechet  
**Lab:** Orange · **Published:** 11 Nov 2026 · **Upvotes:** 6  
**Links:** [arXiv](https://arxiv.org/abs/2607.13920) · [PDF](https://arxiv.org/pdf/2607.13920) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2607.13920.html)

## Key numbers

- **12**: Evaluating 12 systems on questions from HotpotQA and BrowseComp-Plus

## Abstract

While search agents demonstrate impressive capabilities in multi-step question answering, their robustness to poor-quality evidence remains under-explored. Such evidence is rare in benchmark corpora, and resulting failures are hard to reproduce. We propose DeepStress, a dynamic stress-testing framework that subjects agents to degraded evidence at controlled rates. It replaces the retrieval module with a synthetic environment where documents are generated dynamically for each query, under independent control of their trustworthiness, relevance, and factuality. To characterize behavior under stress, we go beyond binary accuracy with a five-way classification of final-answer outcomes and two new reliability-aware metrics, RAS and CoPRAS, which credit cautious behavior and account for computational cost. Evaluating 12 systems on questions from HotpotQA and BrowseComp-Plus, we find that several open-weight agents answer confidently when sources contain obvious credibility red flags, and that many HotpotQA questions are answered from parametric knowledge even when all returned documents are factually wrong.

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