Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives
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.
697b9192a67238fac88cbee8
Tengyue Xu
697b9192a67238fac88cbee9
Zhuoyang Qian
697b9192a67238fac88cbeea
Gaoge Liu
697b9192a67238fac88cbeeb
Li Ling
697b9192a67238fac88cbeec
Zhentao Zhang
claimed_verified
2026-04-02T08:18:35.326Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AlFHn-d0sEOvJZxI3TOWk.jpeg
Zhentao Zhang
santasoda
user
santasoda
697b9192a67238fac88cbeed
Biao Wu
697b9192a67238fac88cbeee
Shuo Zhang
697b9192a67238fac88cbeef
Ke Lu
697b9192a67238fac88cbef0
Wei Shi
697b9192a67238fac88cbef1
Ziqi Wang
697b9192a67238fac88cbef2
Zheng Feng
697b9192a67238fac88cbef3
Yan Luo
697b9192a67238fac88cbef4
Shu Xu
697b9192a67238fac88cbef5
Yongjin Chen
697b9192a67238fac88cbef6
Zhibo Feng
697b9192a67238fac88cbef7
Zhuo Chen
697b9192a67238fac88cbef8
Bruce Yuan
697b9192a67238fac88cbef9
Harry Wang
697b9192a67238fac88cbefa
Kris Chen
Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives
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.
Offline knowledge construction through structured methodological graphs enables more reliable and scalable autonomous scientific discovery by reducing reliance on real-time literature processing.
large language model autonomous scientific discovery runtime-centric execution context window limitations hallucination pre-computation-driven framework peer-reviewed papers research patterns methodological knowledge graph end-to-end research workflows