发表机构
Shanghai University; Second Military Medical University（上海大学; 第二军医大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Valhalla是面向长期科学知识工作的分层框架，采用FREG模型与服务治理架构，经抗体设计任务验证，可将个性化知识结构转化为可共享重组的协作知识状态。
AI 中文摘要
随着大语言模型（LLM）智能体在科学研究中应用日益广泛，外部知识库、知识图谱和长期记忆已提升了信息检索与任务连续性。然而，多数结构化知识系统仍以节点为中心，将文件、概念、结果和判断表示为图中的节点与关系，虽适用于个人知识管理，但这类结构常依赖个人组织习惯，限制了跨用户的知识共享、集成与重组。本文提出Valhalla，一种面向长期科学知识工作的分层知识状态与服务治理框架。Valhalla通过五层文件-资源-实体-关系-图（FREG）模型，以分层封装和稳定语义边界替代扁平图结构：文件与资源保留源身份与来源，实体表示知识对象，关系捕捉语义判断，图提供面向任务的知识视图，使不同研究者的知识状态能在统一结构下交换与重组。我们还引入受微内核范式启发的路由器-合约-工作流服务治理架构，约束语言模型访问、修改与扩展知识状态的方式，同时维持结构一致性与可审计的操作边界。我们实现了Valhalla原型，并通过包含26篇论文资源、80个知识实体和92个语义关系的抗体设计评审任务，验证了知识摄入、跨成员集成和科学写作支持的效果。Valhalla未提出新的知识提取算法，而是提供了一种组织协作科学知识的范式，将个性化知识结构转化为可迁移与重组的共享知识状态。
英文摘要
As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity. However, most structured knowledge systems remain node-centric, representing files, concepts, results, and judgments as nodes and relations in a graph. While suitable for personal knowledge management, such structures often depend on individual organizational practices, limiting knowledge sharing, integration, and reorganization across users. This paper presents Valhalla, a layered knowledge-state and service-governance framework for long-term scientific knowledge work. Valhalla replaces flat graphs with layered encapsulation and stable semantic boundaries through a five-layer File-Resource-Entity-Relationship-Graph (FREG) model. File and Resource preserve source identity and provenance, Entity represents knowledge objects, Relationship captures semantic judgments, and Graph provides task-oriented knowledge views, enabling knowledge states from different researchers to be exchanged and reorganized under a unified structure. We further introduce a Router-Contract-Workflow service-governance architecture, inspired by the microkernel paradigm, to constrain how language models access, modify, and extend knowledge states while maintaining structural consistency and auditable operational boundaries. We implement a Valhalla prototype and validate knowledge ingestion, cross-member integration, and scientific writing support through an antibody-design review task comprising 26 paper resources, 80 knowledge entities, and 92 semantic relations. Rather than proposing a new knowledge-extraction algorithm, Valhalla offers a paradigm for organizing collaborative scientific knowledge, transforming individualized knowledge structures into transferable and reorganizable shared knowledge states.
