做自动化科研或 AI 辅助创新的研究者，可以试试用引文关系图替代静态检索来激发 LLM 的创意，GoR 直接开源了数据和微调方法。
现有 LLM 生成研究想法的方法多依赖静态文献检索或复杂提示工程，忽略了文献间的结构关系。研究者提出 Graphs of Research (GoR)，通过提取每篇种子论文的 2 跳引用邻居，从引用位置、频率、前驱链接和发表时间推导关系，构建论文演化有向无环图 (DAG)。他们从五大 ML/NLP 会议收集数据，微调 Qwen2.5-7B-Instruct-1M 模型，在头对头 LLM 裁判锦标赛中击败 gpt-4o 基线，达到 SOTA。该方法证明了引文演化图作为监督信号的有效性，有望降低自动化科学创新的门槛。
Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation
Research idea generation is the innovation-driving step of automated scientific research. Recently, large language models (LLMs) have shown potential for automating idea generation at scale. However, existing methods mainly condition LLMs on eliciting idea generation through static retrieval of relevant literature or complex prompt engineering, without discarding the structural relations among references. We propose Graphs of Research (GoR), a supervised fine-tuning method that extracts a 2-hop reference neighborhood for each seed paper, derives the relations among those references from citation position, frequency, predecessor links, and publication time, and organizes them into a paper-evolution directed acyclic graph (DAG). We construct an automated extraction pipeline that draws data from five major ML/NLP venues, comprising 498/50/50 train/validation/test seed papers and approximately 7,600 cited references. Qwen2.5-7B-Instruct-1M is fine-tuned on a structured-text prompt that includes the citation graph, edge signals, reference information, and task definition to predict the idea for the seed paper. Across head-to-head LLM-judge tournaments against gpt-4o-driven baselines, GoR-SFT achieves SOTA, demonstrating the effectiveness of citation-evolution graphs as supervision signal for LLM-based idea generation. We hope that this reduces the barrier for citation evolution graphs as a supervision, accelerating automated scientific innovation.