title: MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis
    Discovery via Hierarchical Search
summary: 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.
paper_authors._id: 683529e8ddbf19d1df9038fb
paper_authors.name: Zonglin Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:50:55.453Z
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paper_authors.fullname: Zonglin Yang
paper_authors.type: user
paper_authors.user: ZonglinY
paper_authors._id: 683529e8ddbf19d1df9038fc
paper_authors.name: Wanhao Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:33:14.160Z
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paper_authors.fullname: liuwanhao
paper_authors.type: user
paper_authors.user: wanhaoliu
paper_authors._id: 683529e8ddbf19d1df9038fd
paper_authors.name: Ben Gao
paper_authors._id: 683529e8ddbf19d1df9038fe
paper_authors.name: Yujie Liu
paper_authors._id: 683529e8ddbf19d1df9038ff
paper_authors.name: Wei Li
paper_authors._id: 683529e8ddbf19d1df903900
paper_authors.name: Tong Xie
paper_authors._id: 683529e8ddbf19d1df903901
paper_authors.name: Lidong Bing
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:33:41.494Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
paper_authors.fullname: Lidong Bing
paper_authors.type: user
paper_authors.user: LidongBing
paper_authors._id: 683529e8ddbf19d1df903902
paper_authors.name: Wanli Ouyang
paper_authors._id: 683529e8ddbf19d1df903903
paper_authors.name: Erik Cambria
paper_authors._id: 683529e8ddbf19d1df903904
paper_authors.name: Dongzhan Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:33:22.437Z
paper_authors._id: 6538b861613fe158bd581e35
paper_authors.avatarUrl: /avatars/6817dbfe903675721fd227058b0a91ac.svg
paper_authors.fullname: Dongzhan Zhou
paper_authors.type: user
paper_authors.user: schrodingers-tiger
paper_title: MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis
    Discovery via Hierarchical Search
paper_summary: 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.
paper_ai_summary: A method is proposed to generate detailed scientific hypotheses using LLMs by defining and optimizing a latent reward landscape, outperforming baselines in benchmark evaluations.
paper_ai_keywords: large language models fine-grained scientific hypothesis discovery combinatorial optimization latent reward landscape hierarchical search method