arXiv AI July 8, 2026 By Minho Kim

When Should LLMs Search? Counterfactual Supervision for Search Routing

arXiv:2607. 05752v1 Announce Type: cross Abstract: Search-augmented language models can use external evidence to compensate for limitations in parametric knowledge, but search is not uniformly beneficial: models may call search for questions they can already answer, or rely on noisy evidence when correction, clarification, or abstention would be more appropriate.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605. 09038v3 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries.

By Jinchao Hu, Meizhi Zhong, Kehai Chen, Min Zhang

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arXiv AI

Jul 29

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

arXiv:2607. 25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation.

By Chandan Kumar Sah, Xiaoli Lian, Li Zhang

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Jul 28

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from black-box language models can serve as an actionable signal for retrieval routing.

llms rag nlp benchmarks

arXiv AI

Aug 11

Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards

arXiv:2608. 07531v1 Announce Type: cross Abstract: Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence.

By Cheng Ruoxi, Ma Haoxuan, Zhang Hongyi, Zhang Junming, Duan Ranjie, Xia Qiaolin, Wang Hao, Lu Yu, Shi Haibo, Ma Xingjun

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arXiv AI

Jul 7

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

arXiv:2510. 08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion.

By Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, Haihong Tang

llms reinforcement-learning fine-tuning

arXiv AI

Jun 2

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses

arXiv:2606. 02373v1 Announce Type: new Abstract: Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked.

By Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, Jiashuo Sun, Jimeng Sun, Hammad Bashir, Jiawei Han

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SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs

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