Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation
Large language models frequently violate fundamental scientific principles when generating technical content, undermining their reliability in scientific applications. We introduce Scientific Feasibility Control SFC, a graph-structured conformal prediction framework that provides statistical guarantees for scientific reasoning validity through progressive absolute-coherent-factuality validation. Our approach decomposes scientific reasoning into atomic absolute-coherent-factuality units requiring both individual correctness against physical laws and logical substantiation from preceding context, addressing the cascade effect where early scientific errors contaminate subsequent reasoning st...
| Authors | Mingqiao Mo, Yunlong Tan, Hao Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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