MOOSE-Chem2：通过层次化搜索探索大语言模型在细粒度科学假设发现中的极限
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search
AI总结：
本文提出层次化搜索方法，将细粒度科学假设发现形式化为组合优化问题，探索LLM在假设空间中的奖励景观极限，并在新基准上优于强基线。
摘要：
大型语言模型（LLMs）在自动化科学假设生成方面展现出潜力，然而现有方法主要产生缺乏关键方法和实验细节的粗粒度假设。我们引入并正式定义了细粒度科学假设发现这一新任务，该任务要求从粗略的初始研究方向生成详细、可实验操作的假设。我们将其构建为一个组合优化问题，并研究在最大化利用LLM能力时，其解决该问题的上限。具体而言，我们探讨四个基础问题：（1）如何最好地利用LLM的内部启发式方法，根据其自身内部评分，在所有可能生成的假设中，制定出它自己认为最有前景的细粒度假设——从而在假设空间上定义一个潜在奖励景观；（2）这种由LLM评判的更优假设是否与真实假设表现出更强的一致性；（3）使用由能力相近的多样化LLM组成的集成来塑造奖励景观，是否比使用其中最强LLM的重复实例来定义奖励景观产生更好的结果；（4）由相同LLM组成的集成是否比单个LLM提供更可靠的奖励景观。为解决这些问题，我们提出一种层次化搜索方法，该方法逐步提出细节并将其整合到假设中，从一般概念推进到具体实验配置。我们表明，这种层次化过程平滑了奖励景观，并实现了更有效的优化。在来自近期文献的专家标注细粒度假设的新基准上的实证评估表明，我们的方法始终优于强基线。
英文摘要：
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 new 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 literature show that our method consistently outperforms strong baselines.
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