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Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

Research AI for Discovery

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Merged summary

TL;DR - Large Discovery Model combines generative proposals with a continually updated Bayesian reward surrogate, enabling uncertainty-aware search across open-ended scientific design spaces. It outperforms LLM-only reflection and traditional statistical search in neural-network, antibody, and molecular optimization tasks.

  • The surrogate estimates candidate performance and epistemic uncertainty from experimental observations.
  • These estimates guide candidate generation, refinement, and selection while discovery memory updates recurrently.
  • Reported gains include 2.4Ă— greater validation-BPB reduction, 18.2% lower binding energy, and over 60% relative improvement in molecular multi-objective performance.

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Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

arXiv cs.LG Zhongwei Yu, Yan Song, Xue Yan, Anjie Liu, Xingyu Lu, Yihang Chen, Huichi Zhou, Siyuan Guo, Luoyang Sun, Sihan Chen, Xiangning Yu, Jun Wang 2026-08-16 arXiv:2608.15669
Public signals Hugging Face upvotes 63
Providers: Hugging Face · Upvotes 63 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-15 14:32:26.908150 UTC

TL;DR - Large Discovery Model combines generative proposals with a continually updated Bayesian reward surrogate, enabling uncertainty-aware search across open-ended scientific design spaces. It outperforms LLM-only reflection and traditional statistical search in neural-network, antibody, and molecular optimization tasks.

  • The surrogate estimates candidate performance and epistemic uncertainty from experimental observations.
  • These estimates guide candidate generation, refinement, and selection while discovery memory updates recurrently.
  • Reported gains include 2.4Ă— greater validation-BPB reduction, 18.2% lower binding energy, and over 60% relative improvement in molecular multi-objective performance.
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