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QREF, a plug-and-play Query Refiner and Evidence Filter that rewrites search queries according to the current reasoning intent while ensuring complementary coverage across each search batch, improves the performance of DeepSearch agents across four challenging benchmarks.
Deep research (DR) agents address open-ended information needs through multi-step reasoning, long-horizon web search, and evidence synthesis. However, existing benchmarks primarily focus on academic research or synthetic tasks, leaving DR agents'capabilities in real-world life and professional productivity settings ins...
A gradient-level characterization of reverse-KL OPD trained with the K2 estimator, with Surprise-aware Reweighting (SuRe), a detached, bounded weighting rule that further amplifies this existing allocation.
Sci-MMR is introduced, a benchmark for multi-step evidence-grounded scientific reasoning built on structured argument graphs linking scientific claims, citation-grounded knowledge, visual evidence, and supporting regions, and it is found that current answer-centric benchmarks substantially overestimate the evidence-gro...
A novel textual representation of fault trees is proposed, and a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments is constructed, evaluating a model's ability to assist in malfunction localization.
CAFE (Coupled Agent--Feedback Evolution), a framework in which a shared-parameter model alternates between search-agent and critic roles, is introduced, suggesting that a self-improving search agent needs feedback that co-evolves with the policy it guides.
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