Autonomous scientific discovery with large language model (LLM)-based agents has recently made substantial progress, demonstrating the ability to automate end-to-end research workflows. However, existing systems largely rely on runtime-centric execution paradigms, repeatedly reading, summarizing, and reasoning over large volumes of scientific literature online. This on-the-spot computation strategy incurs high computational cost, suffers from context window limitations, and often leads to brittle reasoning and hallucination. We propose Idea2Story, a pre-computation-driven framework for autonomous scientific discovery that shifts literature understanding from online reasoning to offline knowledge construction. Idea2Story continuously collects peer-reviewed papers together with their review feedback, extracts core methodological units, composes reusable research patterns, and organizes them into a structured methodological knowledge graph. At runtime, underspecified user research intents are aligned to established research paradigms, enabling efficient retrieval and reuse of high-quality research patterns instead of open-ended generation and trial-and-error. By grounding research planning and execution in a pre-built knowledge graph, Idea2Story alleviates the context window bottleneck of LLMs and substantially reduces repeated runtime reasoning over literature. We conduct qualitative analyses and preliminary empirical studies demonstrating that Idea2Story can generate coherent, methodologically grounded, and novel research patterns, and can produce several high-quality research demonstrations in an end-to-end setting. These results suggest that offline knowledge construction provides a practical and scalable foundation for reliable autonomous scientific discovery.

翻译：基于大语言模型（LLM）的智能体在自主科学发现领域近期取得了显著进展，展现出自动化端到端研究流程的能力。然而，现有系统主要依赖以运行时为中心的执行范式，需要反复在线读取、总结和推理大量科学文献。这种即时计算策略计算成本高昂，受限于上下文窗口，且常导致脆弱的推理过程和幻觉现象。我们提出了Idea2Story——一种基于预计算驱动的自主科学发现框架，将文献理解从在线推理转变为离线知识构建。Idea2Story持续收集经过同行评审的论文及其审稿反馈，提取核心方法单元，组合可复用的研究模式，并将其组织成结构化的方法知识图谱。在运行时，用户未充分明确的研究意图将与既定的研究范式对齐，从而实现高质量研究模式的高效检索与复用，而非开放式生成和试错。通过将研究规划与执行建立在预构建的知识图谱之上，Idea2Story缓解了LLM的上下文窗口瓶颈，并大幅减少了对文献的重复运行时推理。我们进行了定性分析和初步实证研究，结果表明Idea2Story能够生成连贯、方法学基础扎实且新颖的研究模式，并能在端到端设置中产出多个高质量的研究演示。这些发现表明，离线知识构建为可靠、可扩展的自主科学发现提供了实用基础。