title: MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated
    Experimental Feedback
summary: Hypothesis ranking is a crucial component of automated scientific discovery,
  particularly in natural sciences where wet-lab experiments are costly and
  throughput-limited. Existing approaches focus on pre-experiment ranking,
  relying solely on large language model's internal reasoning without
  incorporating empirical outcomes from experiments. We introduce the task of
  experiment-guided ranking, which aims to prioritize candidate hypotheses based
  on the results of previously tested ones. However, developing such strategies
  is challenging due to the impracticality of repeatedly conducting real
  experiments in natural science domains. To address this, we propose a simulator
  grounded in three domain-informed assumptions, modeling hypothesis performance
  as a function of similarity to a known ground truth hypothesis, perturbed by
  noise. We curate a dataset of 124 chemistry hypotheses with experimentally
  reported outcomes to validate the simulator. Building on this simulator, we
  develop a pseudo experiment-guided ranking method that clusters hypotheses by
  shared functional characteristics and prioritizes candidates based on insights
  derived from simulated experimental feedback. Experiments show that our method
  outperforms pre-experiment baselines and strong ablations.
paper_authors._id: 68341f661d53989a8ecb685d
paper_authors.name: Wanhao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:10:54.880Z
paper_authors._id: 6684b284dc7b0ae2cc67660c
paper_authors.avatarUrl: /avatars/54b3c0c4d808f293d78085d4d504570a.svg
paper_authors.fullname: liuwanhao
paper_authors.type: user
paper_authors.user: wanhaoliu
paper_authors._id: 68341f661d53989a8ecb685e
paper_authors.name: Zonglin Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:08:35.046Z
paper_authors._id: 646a11791556443f24b582e9
paper_authors.avatarUrl: /avatars/b54d416ddca2f124492ba51bc4b95fd2.svg
paper_authors.fullname: Zonglin Yang
paper_authors.type: user
paper_authors.user: ZonglinY
paper_authors._id: 68341f661d53989a8ecb685f
paper_authors.name: Jue Wang
paper_authors._id: 68341f661d53989a8ecb6860
paper_authors.name: Lidong Bing
paper_authors._id: 68341f661d53989a8ecb6861
paper_authors.name: Di Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:40:05.397Z
paper_authors._id: 64bce15bafd1e46c5504ad38
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64bce15bafd1e46c5504ad38/vkEjiu-mIagKlrXzDH75o.png
paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: di-zhang-fdu
paper_authors._id: 68341f661d53989a8ecb6862
paper_authors.name: Dongzhan Zhou
paper_authors._id: 68341f661d53989a8ecb6863
paper_authors.name: Yuqiang Li
paper_authors._id: 68341f661d53989a8ecb6864
paper_authors.name: Houqiang Li
paper_authors._id: 68341f661d53989a8ecb6865
paper_authors.name: Erik Cambria
paper_authors._id: 68341f661d53989a8ecb6866
paper_authors.name: Wanli Ouyang
paper_title: MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated
    Experimental Feedback
paper_summary: Hypothesis ranking is a crucial component of automated scientific discovery,
  particularly in natural sciences where wet-lab experiments are costly and
  throughput-limited. Existing approaches focus on pre-experiment ranking,
  relying solely on large language model's internal reasoning without
  incorporating empirical outcomes from experiments. We introduce the task of
  experiment-guided ranking, which aims to prioritize candidate hypotheses based
  on the results of previously tested ones. However, developing such strategies
  is challenging due to the impracticality of repeatedly conducting real
  experiments in natural science domains. To address this, we propose a simulator
  grounded in three domain-informed assumptions, modeling hypothesis performance
  as a function of similarity to a known ground truth hypothesis, perturbed by
  noise. We curate a dataset of 124 chemistry hypotheses with experimentally
  reported outcomes to validate the simulator. Building on this simulator, we
  develop a pseudo experiment-guided ranking method that clusters hypotheses by
  shared functional characteristics and prioritizes candidates based on insights
  derived from simulated experimental feedback. Experiments show that our method
  outperforms pre-experiment baselines and strong ablations.
paper_ai_summary: A novel simulator and experiment-guided ranking method improve hypothesis prioritization in scientific discovery by incorporating simulated experimental outcomes.
paper_ai_keywords: hypothesis ranking automated scientific discovery natural sciences wet-lab experiments large language model pre-experiment ranking experiment-guided ranking hypothesis performance similarity noise dataset pseudo experiment-guided ranking clustering functional characteristics simulated experimental feedback