Abstract: As the volume of scientific literature continues to grow, researchers' information needs are shifting from document-level relevance retrieval toward the acquisition of fine-grained scientific knowledge, including research problems, technical methodologies, experimental settings, and supporting evidence for scientific conclusions. Existing scientific paper representation methods mainly rely on abstracts or fixed-length text chunks, making it difficult to simultaneously preserve semantic completeness and evidence traceability. Moreover, using a single large language model to construct such representations is constrained by limited context windows and insufficient factual consistency, resulting in compromised quality and reusability. To address these challenges, this paper proposes a multi-agent collaborative framework for fine-grained semantic mining of scientific papers. Specifically, it adopts a three-layer architecture consisting of control, state, and operation layers. A deterministic task orchestrator manages stage transitions and feedback-driven rollback. A shared semantic evidence space maintains intermediate objects with explicit types, provenance, and dependencies. Four functional agents perform text enhancement, atomic fact extraction, semantic organization, and verification, progressively transforming multimodal evidence into semantic representations. The verification agent produces structured reports identifying error types and affected objects, enabling the orchestrator to perform local corrections and recomputation along dependency chains. Based on the generated semantic representations, we further develop a semantic representation-based framework for academic paper retrieval and knowledge services to facilitate scientific knowledge discovery. Experiments conducted on a self-constructed arXiv dataset demonstrate that the proposed method achieves the best performance in golden-fact coverage, information amount, and faithfulness, while maintaining a relatively low level of cross-representation semantic redundancy. Furthermore, the proposed semantic representation effectively improves academic paper retrieval and knowledge services by providing researchers with more relevant and trustworthy results. Qualitative cases further show that, under comparable token budgets, semantic-representation-based services improve evidence coverage and the specificity and query alignment of answers and recommendation rationale. These experiments validate the effectiveness of our method in supporting scientific knowledge discovery and offering a new technical paradigm for trustworthy scientific knowledge organization and intelligent knowledge service.

摘要： 随着科学文献规模的快速增长，科研人员的信息需求正由文献级相关性检索逐渐转向对研究问题、技术路线、实验条件及结论证据等细粒度科学知识的获取。然而，现有科学论文表示方法主要依赖论文摘要或固定长度文本切块构建语义表示，难以同时兼顾科学知识的完整表达与证据可追溯性；与此同时，利用单一大语言模型构建论文语义表示受限于上下文窗口长度以及事实一致性不足等问题，难以生成高质量、可复用的语义表示。针对上述问题，本文提出一种基于多智能体协同的科学论文细粒度语义挖掘方法，采用“控制—状态—操作”三层架构，由确定性任务编排器管理阶段推进与反馈回溯，以共享语义证据空间统一维护具有类型、来源和依赖关系等信息的中间对象，通过文本增强、原子事实抽取、语义组织和核验四类功能智能体，逐级完成从多模态证据到语义表示的构建。核验智能体输出包含错误类型及对象标识的结构化报告，驱动编排器沿依赖链进行局部修正与重算，形成核验驱动的闭环协同机制。在此基础上，进一步构建了面向科研知识发现的基于语义表示学术论文检索与知识服务框架。基于自建的arXiv科学论文数据集开展实验，结果表明，本文方法在关键事实覆盖率、文本信息量和原文忠实性方面均取得最优结果，并将跨单元语义冗余控制在较低水平。自动化评估的结果表明，基于该语义表示实现的学术论文检索与知识服务能够为科研人员提供更相关且更可信的结果；定性案例进一步显示，在相近词元消耗下，基于语义表示的服务能够提高证据覆盖率，并增强回答及推荐理由的具体性和查询契合度，从而验证了