arXiv 论文提出统一评测框架，检验 LLM 智能体科学探究能力
Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
七个模型在模拟科学探究任务里集体翻车，成功率低得离谱，但加个简单引导结构就明显变强，搞 agent 评测的可以看看。
一篇 arXiv 论文将 Countdown 任务扩展为数学探究环境，让智能体通过操纵变量、收集观测来推断变量间关系。实验覆盖七个 LLM 和多种提示策略，结果显示模型发现底层表达式的成功率很低。受教学脚手架启发的结构化引导能显著提升探究表现，说明为人类学习者设计的方法对 LLM 同样有效。
Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
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.