From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals

• URL: http://arxiv.org/abs/2605.29555v1
• Date: Thu, 28 May 2026 08:09:35 GMT
• Title: From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals
• Abstract summary: MaterEval produces evaluations that follow expert rules and provide supporting evidence, and a rule-removed blind guess.<n>We introduce a fast-slow reasoning scheme that decouples large-scale rapid screening from in-depth review on a small subset.<n>Results show that expert rules can be systematically transformed into learnable preference signals, enabling a low-cost and deployable evaluation module.
• Score: 13.95417871252485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As candidate generation and high-throughput experimentation advance, the primary bottleneck in materials discovery is shifting from property prediction to making reliable evaluations among massive candidate sets. We propose a Knowledge-Augmented Preference Signals Framework, MaterEval, that automatically produces, for the same candidate, two evaluations: an informed judgment that follows expert rules and provides supporting evidence, and a rule-removed blind guess. By pairing the two evaluations as preference data, we guide general-purpose large language models (LLMs), originally lacking materials-specific criteria, from intuitive judgment toward reliable evaluation supported by explicit evidence. To balance throughput, cost, and reliability, we further introduce a fast-slow reasoning scheme that decouples large-scale rapid screening from in-depth review on a small subset. Using high-entropy alloy (HEA) assessment as a case study, we show that, without external retrieval and relying solely on internalized capabilities, small open-source LLMs achieve substantial gains in accuracy, conclusion consistency, and evidence discrimination, approaching the performance of rule-based closed-source LLMs. These results demonstrate that expert rules can be systematically transformed into learnable preference signals, enabling a low-cost and deployable evaluation module for autonomous materials discovery loops.

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