Learning from fracture: Teaching language models to design through physical experience
We introduce FracLearn, an experience-abstraction-generalization framework evaluated through a deliberately unfamiliar, Tetris-inspired fracture game.
ProofPaper ↗
Key points
- How can language models learn to optimize physical designs through experience?
- 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.
- This approach offers a route to scientific agents that turn computational experiments into reusable physical knowledge for materials discovery and engineering design.
Sources (1)
- [1]Learning from fracture: Teaching language models to design through physical experiencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:30 AM
We introduce FracLearn, an experience-abstraction-generalization framework evaluated through a deliberately unfamiliar, Tetris-inspired fracture game.
How can language models learn to optimize physical designs through experience?
Extractive summary: sentences quoted from the sources.