arXiv：cs.LG（机器学习，全量分类）· Kevin Tirta Wijaya, Alston Lo, Michael Sun, Wojciech Matusik, Vahid Babaei·· 14 小时前AI 评分36
PRISMS：用多保真度成对排序破解验证拥堵下的科学发现
Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings
AI 导读
针对候选设计过剩而实验验证能力稀缺的"验证拥堵"问题，研究者提出 PRISMS 框架，用来自计算工具或人类专家的成对排序替代依赖大量数据的回归模型来筛选候选设计。
正文
Abstract:Modern computational methods can now propose candidate molecules, materials, and other scientific designs at an unprecedented scale, creating a validation congestion where candidates are abundant, but experimental capacity to physically evaluate them remains scarce. Discovering novel scientific designs has therefore become increasingly dependent on curation: selecting a small set of promising designs for slow and costly experiments. Existing curation methods typically rely on data-driven regression models that predict absolute scores, but training these models requires substantial experimental data to begin with. Yet, useful curation signals do not have to take the form of absolute measurements, as scientific design discovery is often comparative in nature. Here, we propose that curation can instead be primarily driven by expert pairwise rankings, which are substantially easier to gather. The expertise can come from computational tools or human input of multiple levels of fidelity, ranging from empirical rules of thumb to agentic workflows and experienced scientists. We introduce PRISMS, a framework that uses pairwise rankings from one or more experts, potentially spanning multiple levels of expertise, to identify the most promising candidates without relying on data-hungry regressors. When experts differ in fidelity and cost, PRISMS escalates pairwise queries from lower- to higher-fidelity rankers based on a Fisher-information criterion. In iterative screening that selects designs from fixed drug discovery libraries, PRISMS achieves 50% top-10 discovery recall in ~42% fewer rounds than regression-only active learning, and in ~15% fewer rounds than the ranking-based method with no selective escalation. In optimization that generates new designs without restriction to a predefined library, PRISMS achieves ~18.8% higher hypervolume than the Bayesian optimization baseline.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01827 [cs.LG] |
| (or arXiv:2610.01827v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01827 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kevin Tirta Wijaya [view email]
[v1]
Thu, 1 Oct 2026 15:04:03 UTC (364 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org