标题： MOOSE-Chem2：通过分层搜索探索LLM在精细科学假设发现中的极限 显示英文标题

标题： MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search

摘要： 大型语言模型（LLMs）在自动化科学假设生成方面显示出潜力，但现有方法主要产生粗粒度的假设，缺乏关键的方法学和实验细节。 我们引入并正式定义了细粒度科学假设发现这一新任务，它涉及从粗粒度的初始研究方向生成详细且可实验操作的假设。 我们将此问题构建成一个组合优化问题，并探讨当LLMs被最大化利用时解决该问题的能力上限。 具体来说，我们探讨了四个基础问题：(1) 如何最好地利用LLM的内部启发式方法来制定细粒度假设，使其自身认为在其可能生成的所有假设中最有可能成功的假设，从而在假设空间上定义潜在的奖励景观；(2) 这种由LLM判断更优的假设是否与真实假设表现出更强的一致性；(3) 使用一组多样化的相似容量LLM来塑造奖励景观是否比用它们中最强大的LLM的重复实例来定义它能产生更好的结果；以及(4) 一组相同的LLM是否比单个LLM提供更可靠的奖励景观。 为了解决这些问题，我们提出了一种分层搜索方法，该方法逐步提出并整合假设中的细节，从一般概念进展到具体的实验配置。 我们表明，这种分层过程平滑了奖励景观并实现了更有效的优化。 在最近化学文献中专家注释的细粒度假设的新基准测试中的实证评估显示，我们的方法始终优于强大的基线。 显示英文摘要

摘要： Large language models (LLMs) have shown promise in automating scientific hypothesis generation, yet existing approaches primarily yield coarse-grained hypotheses lacking critical methodological and experimental details. We introduce and formally define the novel task of fine-grained scientific hypothesis discovery, which entails generating detailed, experimentally actionable hypotheses from coarse initial research directions. We frame this as a combinatorial optimization problem and investigate the upper limits of LLMs' capacity to solve it when maximally leveraged. Specifically, we explore four foundational questions: (1) how to best harness an LLM's internal heuristics to formulate the fine-grained hypothesis it itself would judge as the most promising among all the possible hypotheses it might generate, based on its own internal scoring-thus defining a latent reward landscape over the hypothesis space; (2) whether such LLM-judged better hypotheses exhibit stronger alignment with ground-truth hypotheses; (3) whether shaping the reward landscape using an ensemble of diverse LLMs of similar capacity yields better outcomes than defining it with repeated instances of the strongest LLM among them; and (4) whether an ensemble of identical LLMs provides a more reliable reward landscape than a single LLM. To address these questions, we propose a hierarchical search method that incrementally proposes and integrates details into the hypothesis, progressing from general concepts to specific experimental configurations. We show that this hierarchical process smooths the reward landscape and enables more effective optimization. Empirical evaluations on a new benchmark of expert-annotated fine-grained hypotheses from recent chemistry literature show that our method consistently outperforms strong baselines.