开源工具“BootLoops”支持 AI 模型执行精确科学计算

Harvard physicist Matthew Schwartz built "BootLoops," an open-source harness that uses language models for scientific calculations and bridges knowledge gaps between disciplines. In three months, Schwartz and 19 co-authors produced 36 manuscripts across 18 fields, from particle physics calculations to ecological modeling and genome analysis. Schwartz warns that the models often declare victory too early or draw wrong conclusions from correct calculations, making human oversight and verification essential. Automated problem-solving and relevant research aren't the same thing. That's the takeaway from an Anthropic guest post by Prof. Matthew Schwartz, a physicist at Harvard University . He describes how he stopped trying to use Claude like a human researcher and instead started looking for "Claude-shaped problems," tasks that play to the strengths of today's AI models. Schwartz is also currently a visiting researcher at Anthropic. That approach led to BootLoops , an open-source harness for exact scientific calculations. The source code is available on GitHub . Ad Using the harness, Claude found connections between particle physics, ecology, population genetics, economics, and linguistics, according to Schwartz. But the results often only became scientifically valuable once domain experts stepped in to set the direction. Ad Schwartz uses the concept of the "convex hull" to illustrate how AI can help in science. Human knowledge is fragmented, with individual fields extending in different directions while the gaps between them go unexplored. A lab might spend 20 years studying one set of genes with a single method, leaving neighboring genes and alternative approaches untouched. An AI harness like BootLoops is supposed to fill those gaps by drawing connections across the jagged frontiers of knowledge . Over three months, the team produced 36 manuscripts across 18 fields with 19 co-authors. Schwartz started with particle physics calculations, specifically scattering amplitudes and elliptic integrals. Within weeks, Claude computed 30 integrals using BootLoops, fifteen reproducing known results and fifteen computed for the first time. Ad Then he expanded the search to other fields. In ecology, Claude solved a 20-year-old equation from neutral biodiversity theory that had previously been impossible to compute at scale. When applied to data, the results showed that tree species composition on Barro Colorado Island in the Panama Canal is changing 4.5 times faster than the theory allows. Ecologist James O'Dwyer then helped turn that finding into a better predictive model. In population genetics, the team analyzed 5.7 billion mutation pairs from the 1000 Genomes Project and found evidence for a mechanism called gene conversion. Other projects included an AI data editor for economics journals that automatically checked 4,452 replication packages (published as an NBER Working Paper ), as well as a word stress database covering 6,072 languages, built with three linguists. Ad Schwartz describes how fast AI is changing science, to the point where planning ahead is becoming nearly impossible. Why apply for a three-year grant to fund a calculation that an AI model might solve overnight? Ad