面向编码智能体的本体论基础项目记忆
Ontology-Grounded Project Memory for Coding Agents
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中文总结 AI 辅助
针对编码智能体追踪代码变更原因的难题，提出MOOSEDev系统，该系统通过知识图谱存储项目信息，实验显示其在关键查询任务上显著优于基线工具，且相关性召回率与令牌成本相当。
中文摘要 AI 辅助
编码智能体已成为许多软件项目中生成新代码的主要手段，而由此产生的变更速度使得追踪这些变更背后的原因颇具挑战性。本文介绍MOOSEDev，这一为编码智能体提供结构化、本体论基础项目记忆的系统。该系统通过模型上下文协议（MCP）接口向智能体暴露的知识图谱中捕获架构决策、经验、约束和基本原理。记录带有生命周期状态、来源和替代链接，可通过MOOSE查询，MOOSE是一个专有神经符号引擎，将符号层作为主要推理基质。我们在包含835条类型化记录的中立公开语料库上，将MOOSEDev与生产级向量记忆工具进行对比。MOOSEDev在替代、集合完整性和否定问题上基本完整返回预期答案集（0.98-1.00），而基线的top-k检索仅达到6%至27%。相反，两个系统的相关性召回率和令牌成本大致相当。我们还描述了自身代码库的时间提交历史引导、预注册的实时试验以及所获经验。
英文摘要
Coding agents have become the primary means of generating new code in many software projects, and the resulting velocity of changes makes keeping track of the reasons behind those changes challenging. This paper introduces MOOSEDev, a system designed to give coding agents structured, ontology-grounded project memory. The system captures architectural decisions, lessons, constraints, and rationales in a knowledge graph exposed to agents via a Model Context Protocol (MCP) interface. Records carry lifecycle status, provenance, and supersession links, queryable via MOOSE, a proprietary neurosymbolic engine that treats the symbolic layer as the primary reasoning substrate. We compared MOOSEDev against a production vector-memory tool on a neutral public corpus of 835 typed records. MOOSEDev returned the expected answer set essentially in full (0.98-1.00) on supersession, set-completeness, and negation questions, whereas the baseline's top-k retrieval surfaced between 6% and 27%. Conversely, relevance recall and token cost were largely equivalent between the two systems. We also describe a temporal commit-history bootstrap of our own codebase, a pre-registered live trial, and lessons learned.
发表机构
- Trivyn（特里文公司）
机构由 AI 辅助整理，请以论文原文为准。
