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arXiv cs.CL
arXiv cs.CL
7/21/2026
Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation

Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation

Short summary

Scientific Feasibility Control (SFC) is a graph-structured conformal prediction framework that provides statistical guarantees for LLM scientific reasoning validity. It decomposes reasoning into atomic factuality units, models logical dependencies as deducibility graphs, and branches to alternative generation paths when violations are detected. SFC achieves 50.1% accuracy on PhyX physics reasoning, outperforming DeepSeek-R1 and GPT-4, with 91.7% scientific validity and a 73% reduction in scientific law violations.

  • •SFC uses graph-structured conformal prediction to guarantee scientific reasoning validity in LLMs
  • •Branches to alternative generation paths when scientific violations are detected, using verified context
  • •50.1% accuracy on PhyX, outperforming DeepSeek-R1 (49.8%) and GPT-4 (45.8%), with 73% fewer law violations

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Read full article at arXiv cs.CL

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