{
  "id": 14851227,
  "title": "Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration",
  "url": "https://urgent.news/2026/10/15/evaluating-inquiry-learning-in-llm-based-agents-hypothesizing",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-15T09:52:14.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.21604v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large language models (LLMs) have demonstrated strong capabilities in several components of this process, including inductive reasoning, hypothesis generation, and data analysis. However, prior work typically evaluates these abilities in…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}