BizSage：面向商业研究的高效知识检索自进化多智能体框架
BizSage: A Self-Evolving Multi-Agent Framework for Business Research with Efficient Knowledge Retrieval
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中文总结 AI 辅助
BizSage提出结合细粒度检索与自进化机制的多智能体框架，通过横向知识图谱和个性化PageRank提升商业研究证据检索，在基准测试中多数指标领先且零幻觉引用。
中文摘要 AI 辅助
尽管基于大型语言模型（LLM）的多智能体系统在自动化学术研究的渐进式工作流程方面展现出潜力，但将其扩展到经济学和商业研究领域仍面临两大挑战，在这些领域中，专业领域知识跨越相邻学科，却难以以结构化方式获取。首先，现有方法大多在论文层面进行检索，而研究任务所需的证据往往分布在不同章节，造成粒度不匹配，阻碍了检索的覆盖率和精确度。其次，这些领域要求严格的实证严谨性，而当前系统从评估反馈中学习的机制有限。我们提出了BizSage，一个结合语料库级细粒度检索与质量驱动自进化的多智能体框架。我们通过合并章节级知识图谱构建了横向知识图谱（LKG），并应用个性化PageRank（PPR）来突出语义相关且结构上重要的章节。七个专门智能体在元评审自进化机制下协作，该机制从评估轨迹中提炼失败模式，转化为可复用的策略。在一个涵盖四个领域和三项任务的基准测试上，BizSage在大多数指标上排名第一，对六个基线模型的成对胜率超过60%，且产生零幻觉引用。我们希望BizSage能为经济学、商业及更广泛的社会科学领域的可靠研究辅助铺平道路。
英文摘要
While multi-agent systems based on large language models (LLMs) have shown promise in automating the progressive workflow of academic research, extending them to economics and business research, where specialized domain knowledge spans neighboring disciplines yet remains difficult to access in a structured way, presents two challenges. First, existing methods mostly retrieve at the paper level, yet the evidence needed for research tasks is often distributed across different sections, creating a granularity mismatch that hinders retrieval coverage and precision. Second, these fields demand strict empirical rigor, yet current systems provide limited mechanisms for learning from evaluation feedback. We present \textbf{BizSage}, a multi-agent framework combining corpus-level fine-grained retrieval with quality-driven self-evolution. We build a Lateral Knowledge Graph (LKG) by merging section-level knowledge graphs and apply Personalized PageRank (PPR) to surface semantically relevant and structurally important sections. Seven specialized agents collaborate under a Meta-Review self-evolution mechanism that distills failure modes from evaluation traces into reusable strategies. On a benchmark spanning four domains and three tasks, BizSage ranks first on the majority of metrics, achieves pairwise win-rates above 60\% against six baselines, and produces zero hallucinated citations. We hope BizSage paves the way for reliable research assistance in economics, business, and the broader social sciences.
发表机构
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
