{
  "id": 3238,
  "url": "https://arxiv.org/abs/2606.03135v1",
  "title": "Uncertainty-Aware Clarification in LLM Agents with Information Gain",
  "summary": "Large Language Model (LLM) agents often operate under underspecified user instructions, where latent uncertainty over user intent leads to erroneous tool actions. To address this challenge, we propose a goal-oriented clarification framework that aligns clarification behavior with ambiguity resolution. Central to our approach is the Information Gain Reward, a metric that quantifies the utility of clarification questions by measuring the Bayesian belief update towards the ground-truth goal induced",
  "authors": "Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Ying Zhao, Zhijiang Guo et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-02T04:23:59.000Z",
  "fetched_at": "2026-07-14T16:30:05.531Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3238",
  "original_url": "https://arxiv.org/abs/2606.03135v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}