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arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...
SearchHorizonBench is a new benchmark that evaluates how AI agents decide whether the cost of an additional search is justified by its expected benefit. It provides 45,632 executions on 3,168 questions, using paired continuations and repeated runs to separate true search gain from generation noise. The dataset shows that historical calibration can reduce Brier error by 16.2% and that free‑selection observations offer a 2.49‑point utility advantage over a constant action, highlighting the importance of accurate search‑value estimation.
arXiv:2610.08452v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring...
The paper introduces RuVerBench, a benchmark with 2,458 instances for evaluating the reliability of Large Language Models acting as judges (LaaJ) in verifying rubric compliance within agentic scenarios such as deep research and agentic coding. It systematically meta‑evaluates frontier LLMs, revealing that even the most advanced models perform well yet still produce substantial noise. The study also examines how prompt design, batching, and majority voting affect verification accuracy, noting that weaker models are more prompt‑sensitive, batched verification trades accuracy for efficiency, and majority voting offers diminishing returns.
arXiv:2604.18235v3 Announce Type: replace-cross Abstract: Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy O...