SFC framework boosts LLM scientific accuracy by 73% with conformal prediction

A new paper from researchers Mingqiao Mo, Yunlong Tan, and Hao Zhang tackles the persistent problem of LLMs violating fundamental scientific principles when generating technical content. Their solution, Scientific Feasibility Control (SFC), is a graph-structured conformal prediction framework that provides formal statistical guarantees for scientific reasoning validity. Unlike previous independence-based methods that treat each claim in isolation, SFC decomposes reasoning into atomic "absolute-coherent-factuality" units that require both individual correctness against physical laws and logical substantiation from preceding context. This approach addresses the cascade effect where early scientific errors contaminate subsequent reasoning steps.

SFC operates through real-time validation with dynamic branching: when scientific violations are detected, the system branches to alternative generation paths using verified context as foundation. The framework was tested across established scientific reasoning benchmarks including PhyX (multimodal physics), MATH, ScienceQA, and ARC Challenge. Results show SFC achieving 50.1% accuracy on PhyX physics reasoning, outperforming DeepSeek-R1 (49.8%) and GPT-4 (45.8%), while maintaining 91.7% scientific validity with formal conformal coverage guarantees at α=0.10 confidence level. Most notably, SFC reduces scientific law violations by 73% across multiple model architectures, marking a significant step toward reliable AI for scientific applications.