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论文arXiv 2610.2160413 天前

评估基于 LLM 的智能体中的探究学习：假设、推理与探索 ​

提出统一交互框架评估 LLM 探究能力，在扩展 Countdown 数学环境中验证探索、因果发现与假设改进。

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原文链接：Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration（arXiv 2610.21604 · 发布日期：2026-10-15）

Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration

提出统一交互框架评估 LLM 探究能力，在扩展 Countdown 数学环境中验证探索、因果发现与假设改进。

核心要点 ​

  • 以往研究多孤立评估归纳推理、假设生成和数据分析，尚不清楚模型如何整合完整探究循环。
  • 将 Countdown 扩展为数学环境，使智能体操纵变量、收集观察并推断变量关系。
  • 实验覆盖七个 LLM 和多种提示策略，发现其内在探究能力有限，发现底层表达式成功率较低。
  • 受教育支架启发的结构化指导显著提升表现，支持更有效的探究行为。

验证范围与局限：验证范围限于扩展的 Countdown 数学交互环境、七个 LLM 和多种提示策略。

英文摘要 (Abstract) ​

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 isolation, leaving open how models integrate them across a full inquiry cycle. In this work, we introduce a framework for evaluating LLMs' inquiry capabilities in a unified, interactive setting. We extend the Countdown task to a mathematical environment where an agent probes the system by manipulating variables, collecting observations, and inferring relationships between them. This setup enables analysis of exploration strategies, causal discovery, and hypothesis refinement during inquiry. Across seven LLMs and multiple prompting strategies, our experiments reveal limited inherent inquiry capabilities and low success rates in discovering the underlying expressions. However, structured guidance inspired by educational scaffolding significantly improves performance. Our findings highlight current limitations of LLMs in strategic exploration and iterative hypothesis refinement, and show how simple guidance strategies developed for human learners in inquiry learning can support more effective inquiry behavior.

作者：Marta Knežević, Seyed Parsa Neshaei, Jibril Frej, Vinitra Swamy, Tanja Käser

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  1. arXiv 2610.21604：Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration访问 2026-10-21