🤖 AI Summary
This work addresses the unreliability of language models in physical design by introducing the Physics-Anchored Certification (PHACT) framework, which shifts certification authority from the model to a deterministic verification engine grounded in physical laws. In this paradigm, the language model serves solely as a proposal mechanism for candidate designs, while formal certification is performed by the engine through structured metrics derived from fixed inputs, thereby eliminating the possibility of fabrication at its source. The resulting propose-and-certify closed-loop system demonstrates robustness across five scientific domains and achieves zero erroneous certifications in 80 adversarial trials—spanning two language models, two decoding temperatures, and deliberately compromised verification engines—significantly enhancing the trustworthiness of generated outputs.
📝 Abstract
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.