Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis
AlphaAgent is a skill-driven framework that decouples retrieval-based QA from paper-level report generation using explicit skill contracts. It outperforms baselines in blind evaluations, particularly in mechanistic explanation and credibility awareness.
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[Submitted on 10 May 2026]
Title:Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis
View a PDF of the paper titled Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis, by Bixuan Li and 5 other authors
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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.
Comments: 9 pages, 5 figures
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2607.20431 [cs.CL]
(or arXiv:2607.20431v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20431
arXiv-issued DOI via DataCite
Submission history
From: Peng Kang PhD [view email] [v1] Sun, 10 May 2026 13:00:32 UTC (4,444 KB)
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