Paper page - Evaluation-driven Scaling for Scientific Discovery

Source: https://huggingface.co/papers/2604.19341 Authors:

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Abstract

SimpleTES framework scales evaluation-driven discovery loops for scientific problems, achieving state-of-the-art results across multiple domains through parallel exploration and feedback-driven refinement.

Language models are increasingly used in scientific discovery to generate hypotheses, propose candidate solutions, implement systems, and iteratively refine them. At the core of these trial-and-error loops lies evaluation: the process of obtaining feedback on candidate solutions via verifiers, simulators, or task-specific scoring functions. While prior work has highlighted the importance of evaluation, it has not explicitly formulated the problem of howevaluation-driven discovery loopscan be scaled up in a principled and effective manner to push the boundaries of scientific discovery, a problem this paper seeks to address. We introduce Simple Test-time Evaluation-driven Scaling (SimpleTES), a general framework that strategically combinesparallel exploration,feedback-driven refinement, and local selection, revealing substantial gains unlocked by scalingevaluation-driven discovery loopsalong the right dimensions. Across 21 scientific problems spanning six domains, SimpleTES discovers state-of-the-art solutions using gpt-oss models, consistently outperforming both frontier-model baselines and sophisticated optimization pipelines. Particularly, we sped up the widely usedLASSO algorithmby over 2x, designedquantum circuit routingpolicies that reduce gate overhead by 24.5%, and discovered newErdos minimum overlap constructionsthat surpass the best-known results. Beyond novel discoveries, SimpleTES produces trajectory-level histories that naturally supervise feedback-driven learning. When post-trained on successful trajectories, models not only improve efficiency on seen problems but also generalize to unseen problems, discovering solutions that base models fail to uncover. Together, our results establish effective evaluation-driven loop scaling as a central axis for advancingLLM-driven scientific discovery, and provide a simple yet practical framework for realizing these gains.

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