From Intent to Improvement: Agentic Dataset Search for Better Model Training

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents
• benchmarks