AI 中文总结
研究针对材料科学文献分析难题，提出技能驱动的AlphaAgent框架，通过明确技能契约解耦任务。其含专门检索技能和报告生成技能，在盲评中显著优于基线系统，提升了文献分析效果。
AI 中文摘要
材料科学文献分析需要同时关注成分、加工、表征和性能关系，但传统的检索增强生成管道难以在单个检索然后生成架构中协调异构任务。本文提出了AlphaAgent，这是一个技能驱动的代理框架，通过明确的技能契约将基于检索的问答与论文级报告生成解耦。一个专门的检索技能将用户请求重写为特定材料的搜索意图，查询来自《期刊引证报告：冶金与冶金工程》类别的30多万篇论文的精选索引，并在初始证据不足时重新制定查询。一个单独的报告生成技能解析全文PDF以生成结构化的每篇论文分析报告和跨论文摘要。在对40个材料科学问题的盲评中，AlphaAgent在很大程度上优于一个在基础模型、文档索引和检索规模上匹配的基线系统，在机械解释和可信度边界意识方面有最大的提升。这些结果表明，明确的任务分离、精细的检索意图和证据感知生成改进了基于大语言模型的材料研究文献分析。
英文摘要
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.
Comments9 pages, 5 figures
