arXiv cs.AI· Bo Ni·· 3 天前AI 评分27
FracLearn：让语言模型通过物理经验学习设计
Learning from fracture: Teaching language models to design through physical experience
AI 导读
FracLearn 框架让语言模型在受 Tetris 启发的断裂游戏中通过模拟断裂响应积累经验，抽象出钢筋布置、裂纹演化与载荷传递的条件性原理，再在新架构中组合这些原理以超越此前设计记录，最佳综合得分比经验阶段记录高 11.0%。断裂分析显示性能提升源于裂纹偏转与暂时捕获等增韧机制及更高的残余承载能力。学习通过外部知识与决策完成，无需更新模型权重。
正文 · 原文
Abstract:How can language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framework evaluated through a deliberately unfamiliar, Tetris-inspired fracture game. The model first proposes architectures and examines simulated fracture responses to accumulate experience. Abstraction organizes these observations into conditional principles linking reinforcement placement, crack evolution and load transfer, with supporting evidence and failure conditions. Generalization challenges the model to combine these principles in new architectures and surpass the earlier design record. Campaigns demonstrate progressive refinement and recovery through hypothesis revision, yielding a best composite score 11.0% above the experience-stage record. Fracture analyses connect improved performance to crack deflection and temporary trapping as toughening mechanisms, alongside greater residual load capacity. Learning occurs through external knowledge and decisions, without model-weight updates. This approach offers a route to scientific agents that turn computational experiments into reusable physical knowledge for materials discovery and engineering design.
| Comments: | 21 pages, 7 figures, 3 tables. Includes Supplementary Materials |
| Subjects: | Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.17804 [cond-mat.mtrl-sci] |
| (or arXiv:2610.17804v1 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17804 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Ni [view email]
[v1]
Tue, 13 Oct 2026 06:30:52 UTC (1,045 KB)
来源：arXiv cs.AI · arxiv.org