{
  "id": 14890,
  "url": "https://arxiv.org/abs/2607.27652",
  "title": "Harness-G: A Graph-Structured Harness for Search Agents",
  "summary": "Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query st",
  "authors": "Yanning Hou, Haoyuan Chen, Sihang Zhou, Xiaoshu Chen, Xirui Liu, Duanyang Yuan",
  "category": "research",
  "topics": "regulation,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-29T20:00:00.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/14890",
  "original_url": "https://arxiv.org/abs/2607.27652",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}