{
  "id": 11341,
  "url": "https://arxiv.org/abs/2607.10463",
  "title": "GRASP: GRanularity-Aware Search Policy for Agentic RAG",
  "summary": "Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matching or semantic similarity, and how to control context granularity to prevent irrelevant tokens from interfering with agent reasoning. In this paper, we introduce GRASP, a reinforcement learning (RL) framework for trainin",
  "authors": "Varun Gandhi, Jaewook Lee, Shantanu Todmal, Franck Dernoncourt, Ryan Rossi, Zichao Wang",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T20:00:00.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/11341",
  "original_url": "https://arxiv.org/abs/2607.10463",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}