MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis
  Discovery via Hierarchical Search
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.
683529e8ddbf19d1df9038fb
Zonglin Yang
claimed_verified
2025-05-27T07:50:55.453Z
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/avatars/b54d416ddca2f124492ba51bc4b95fd2.svg
Zonglin Yang
user
ZonglinY
683529e8ddbf19d1df9038fc
Wanhao Liu
admin_assigned
2025-05-27T12:33:14.160Z
6684b284dc7b0ae2cc67660c
/avatars/54b3c0c4d808f293d78085d4d504570a.svg
liuwanhao
user
wanhaoliu
683529e8ddbf19d1df9038fd
Ben Gao
683529e8ddbf19d1df9038fe
Yujie Liu
683529e8ddbf19d1df9038ff
Wei Li
683529e8ddbf19d1df903900
Tong Xie
683529e8ddbf19d1df903901
Lidong Bing
admin_assigned
2025-05-27T12:33:41.494Z
6454685a548f22be598414c4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
Lidong Bing
user
LidongBing
683529e8ddbf19d1df903902
Wanli Ouyang
683529e8ddbf19d1df903903
Erik Cambria
683529e8ddbf19d1df903904
Dongzhan Zhou
admin_assigned
2025-05-27T12:33:22.437Z
6538b861613fe158bd581e35
/avatars/6817dbfe903675721fd227058b0a91ac.svg
Dongzhan Zhou
user
schrodingers-tiger
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis
  Discovery via Hierarchical Search
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.
A method is proposed to generate detailed scientific hypotheses using LLMs by defining and optimizing a latent reward landscape, outperforming baselines in benchmark evaluations.
large language models fine-grained scientific hypothesis discovery combinatorial optimization latent reward landscape hierarchical search method