Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation
Quick Answer
This paper shows that The Scientific Feasibility Control (SFC) framework enhances large language models' reliability in scientific generation by validating reasoning through a graph-structured conformal prediction approach.
Quick Take
SFC achieved 50.1% accuracy on the PhyX physics benchmark, outperforming DeepSeek-R1 and GPT-4, while reducing scientific law violations by 73% and ensuring 91.7% scientific validity.
Key Points
- SFC provides statistical guarantees for scientific reasoning validity.
- Achieved 50.1% accuracy on PhyX, outperforming DeepSeek-R1 (49.8%) and GPT-4 (45.8%).
- Reduced scientific law violations by 73% across multiple model architectures.
- Utilizes real-time validation and dynamic branching for error correction.
- Ensures 91.7% scientific validity at a 0.10 confidence level.
DeepSignal Analysis
What happened
The Scientific Feasibility Control (SFC) framework was introduced to improve the reliability of large language models in scientific generation. It employs a graph-structured conformal prediction approach to validate scientific reasoning, achieving notable accuracy on the PhyX benchmark.
Key evidence
- SFC achieved 50.1% accuracy on the PhyX physics benchmark, outperforming DeepSeek-R1 at 49.8% and GPT-4 at 45.8%.
- The framework reduced scientific law violations by 73% across multiple model architectures, indicating a significant improvement in reliability.
- SFC provides 91.7% scientific validity with formal conformal coverage guarantees at a 0.10 confidence level.
Why it matters
The introduction of SFC addresses the frequent violations of scientific principles by large language models, which can undermine their application in technical fields. By validating reasoning through a structured approach, SFC enhances the trustworthiness of generated scientific content, potentially impacting research and development in various scientific domains.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 steps. Unlike independence-based methods that treat claims in isolation, SFC models logical dependencies as approximate deducibility graphs and 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. We demonstrate SFC across established scientific reasoning benchmarks including PhyX multimodal physics, MATH, ScienceQA, and ARC Challenge, achieving 50.1 percent accuracy on PhyX physics reasoning, substantially outperforming recent reasoning models including DeepSeek-R1 49.8 percent and GPT-4 45.8 percent while providing 91.7 percent scientific validity with formal conformal coverage guarantees at alpha equals 0.10 confidence level and reducing scientific law violations by 73 percent across multiple model architectures.
| Comments: | 25 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.16704 [cs.CL] |
| (or arXiv:2607.16704v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16704 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hao Zhang [view email]
[v1]
Sat, 18 Jul 2026 08:38:19 UTC (389 KB)
— Originally published at arxiv.org
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