Structural Certification for Reliable Physical Design with Language Models
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Abstract (arXiv)
An unreliable language model can be made to produce reliable physical designs if the authority to assert is moved out of the model: the model proposes, and a deterministic engine alone certifies, returning certified, impossible, or unknown. We introduce Physics-Anchored Certification (PHACT), a propose-certify loop spanning five scientific domains, and identify what makes such a certificate trustworthy. A checker that accepts a model-supplied value can be forged; deriving the certified quantity from fixed inputs instead makes forgery impossible by construction. Across eighty adversarial trials spanning two models, two decoding temperatures, and a deliberately faulted engine, this contract produced zero false certifications.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 29 Jun 2026.
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References · 15
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
- 2022Language Models (Mostly) Know What They Knowcited by 1,783
- 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
- 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
- 2020Learning to Simulate Complex Physics with Graph Networkscited by 867
- 2022PAL: Program-aided Language Modelscited by 635
3 more
- 2020The Next Decade in AI: Four Steps Towards Robust Artificial Intelligencecited by 194
- 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 193
- 2019Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoningcited by 149
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