Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives

Key points

• Problem: Existing research agents rely on a runtime-centric approach that re-retrieves, reads, and summarizes large volumes of papers for every research attempt. This is computationally costly, runs into context window limits, and often leads to brittle reasoning and hallucination. The authors cite a prior pipeline that took up to 15 hours from ideation to experimentation.
• Input and output: This paper proposes a framework, implements part of it, and shows it through case studies. The input is a vague research idea written in natural language, and the output is a structured research pattern with a problem definition, research gap, method skeleton, and innovation claims. The experiments in the body cover research pattern generation, and connecting experiment agents and producing complete paper drafts are listed as future work.
• Method: Offline, the system collects accepted peer-reviewed papers with their reviews, anonymizes them, extracts method units that capture each paper's problem formulation and solution, reduces their embeddings with UMAP, and clusters them with DBSCAN into research patterns. It builds a knowledge graph whose nodes are method units and meta-methods and whose edges link units used together in the same paper. Online, it ranks patterns by combining idea-level, domain-level, and paper-level scores, then refines them in a loop where an LLM reviewer rates novelty and soundness and only revisions that raise the scores are kept.
• Implementation: The knowledge graph is built from about 5,000 NeurIPS and 8,000 ICLR papers from the last three years, about 13K papers in total, with their reviews, and research patterns are generated with GLM-4.7 as the backbone. The official README provides a pipeline script that turns a one-line idea into a final story JSON, a four-step builder for constructing a graph from your own paper data, and an anchored multi-agent review that uses real review scores as reference points. The README states that the prebuilt graph comes from ICLR data.
• Results: Results are given only as qualitative comparisons. For 3 user ideas curated by an external collaborator, Idea2Story was compared with a baseline that asks the same model to write a story directly, and Gemini 3 Pro, which took no part in generation, judged with the method hidden and favored Idea2Story in all evaluated cases. In an e-commerce user intent case, Idea2Story reframed intent classification as diffusion-based structural reasoning, while the baseline added components within the conventional classification setup.
• Limits: The evaluation in the body is a preliminary qualitative study relying on 3 ideas curated by an external collaborator and a single LLM evaluator (Gemini 3 Pro). The knowledge graph is built only from ML papers at NeurIPS and ICLR, and the authors leave a closed-loop pipeline with experiment agents and complete paper drafting to future work. In their related work, the authors also write that automated evaluation systems remain less reliable than human experts at drawing accept and reject boundaries.

Paper links

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