cond-mat.mtrl-sciOct 13, 2026

Learning from fracture: Teaching language models to design through physical experience

Authors: Bo Ni

Organizations: Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA · Generative Mechanics Laboratory (GenMech Lab), independent research initiative

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.

Figures & tables

Explore similar work

CardsList
  1. Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation

    Apr 30, 2026João Pedro Gandarela, Thiago Rios, Stefan Menzel +1Mechanism DesignLarge Language Model-Guided Optimization

  2. Learning to Coach for Experiential Learning

    Sep 14, 2026Guanheng Chen, Tianzhu Ye, Li Dong +3RL for Language ModelsRL for Language Model Reasoning