research
New method uses structural graphs to fix LLM reasoning collapse in multi-step theorem prediction
Researchers have identified and solved a critical scaling problem in LLM-based theorem prediction called Structural Drift, where in-context learning performance collapses as reasoning depth increases. Using Theorem Precedence Graphs to encode topological dependencies, they achieved 89.29% accuracy on the FormalGeo7k benchmark—matching state-of-the-art supervised approaches without any gradient-based training.