Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis

• URL: http://arxiv.org/abs/2607.20431v1
• Date: Sun, 10 May 2026 13:00:32 GMT
• Title: Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis
• Abstract summary: Materials science literature analysis requires simultaneous attention to composition, processing, characterization, and property relationships.<n>Here we present AlphaAgent, a skill-driven agent framework that decouples retrieval-based question answering from paper-level report generation.<n>A dedicated skill rewrites user requests into material-specific intents, queries a curated index of more than 300,000 papers from the Journal Reports Citation and Metallurgical Engineering category, and reformulates queries when initial evidence is insufficient.
• Score: 8.055788977592144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Materials science literature analysis requires simultaneous attention to composition, processing, characterization, and property relationships, yet conventional retrieval-augmented generation pipelines struggle to reconcile heterogeneous tasks within a single retrieve-then-generate architecture. Here we present AlphaAgent, a skill-driven agent framework that decouples retrieval-based question answering from paper-level report generation through explicit skill contracts. A dedicated retrieval skill rewrites user requests into material-specific search intents, queries a curated index of more than 300,000 papers from the Journal Citation Reports Metallurgy and Metallurgical Engineering category, and reformulates queries when initial evidence is insufficient. A separate report-generation skill parses full-text PDFs to produce structured per-paper analytical reports and cross-paper summaries. In a blind evaluation on 40 materials-science questions, half of which required deep analytical reasoning, AlphaAgent substantially outperformed a baseline system matched for underlying model, document index, and retrieval scale, with the largest gains in mechanistic explanation and awareness of credibility boundaries. These results indicate that explicit task separation, refined retrieval intent, and evidence-aware generation improve large-language-model-based literature analysis for materials research.

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