The paper "From Intent to Improvement: Agentic Dataset Search for Better Model Training" introduces AgenticDS, a contract‑governed agentic framework that transforms a training‑goal query into a typed search plan, executes deterministic operators over hybrid retrieval and graph expansion, and verifies provenance‑grounded evidence before ranking datasets. On the HuDS‑Bench benchmark, AgenticDS outperforms state‑of‑the‑art dense–sparse hybrids by 25.7% in NDCG@10 and improves downstream accuracy in mathematics, code, and medicine. The authors provide open‑source code and data at https://github.com/nju-websoft/AgenticDS.
By Qiaosheng Chen, Kaijia Huang, Fanqi Zhou, Gong Cheng
arXiv:2609.09410v1 Announce Type: new
Abstract: While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world p...
By Ruofan Wu, Peiran Xu, Xiaolong Li, Fan Shu, Soyoung Yoon, Yite Wang, Xiaodong Yu, Boyi Liu, Feng Yan, Debiao Li, Yuxiong He, Zhewei Yao
arXiv:2605. 28787v2 Announce Type: replace-cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows.
By Shiyu Chen, Tarfah Alrashed, Alon Halevy, Natasha Noy
arXiv:2605.05726v2 Announce Type: replace
Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...
By Hongcheol Cho, Ryangkyung Kang, Youngeun Kim
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
By Kyle Zheng, Han Zhang, Renliang Sun, Chenchen Ye, Wei Wang
arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.
By Aivin V. Solatorio, Olivier Dupriez, Rafael Macalaba