Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation

• URL: http://arxiv.org/abs/2605.14790v1
• Date: Thu, 14 May 2026 12:57:56 GMT
• Title: Graphs of Research: Citation Evolution Graphs as Supervision for Research Idea Generation
• Abstract summary: Graphs of Research (GoR) is a supervised fine-tuning method that extracts a 2-hop reference neighborhood for each seed paper.<n>GoR organizes references into a paper-evolution directed acyclic graph (DAG)<n>We construct an automated extraction pipeline that draws data from five major ML/NLP venues.
• Score: 24.044079363031035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts [3.548612755859995]
  Graph2Idea is a knowledge graph-guided framework for retrieval-augmented scientific idea generation.<n>It first retrieves papers according to the input topic, transforms them into structured knowledge triples, and dynamically constructs a target-centered knowledge graph.<n>It then extracts compact graph-derived contexts that retain target-relevant relational evidence while reducing noisy textual input.
  arXiv  Detail & Related papers  (2026-06-08T06:58:21Z)
• NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code [49.610331036334316]
  We introduce NERFIFY, a framework that reliably converts NeRF research papers into trainable Nerfstudio plugins.<n>Code, data and implementations will be publicly released.
  arXiv  Detail & Related papers  (2026-02-28T20:57:32Z)
• Graph-Anchored Knowledge Indexing for Retrieval-Augmented Generation [53.42323544075114]
  We propose GraphAnchor, a novel Graph-Anchored Knowledge Indexing approach.<n> Experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of GraphAnchor.
  arXiv  Detail & Related papers  (2026-01-23T05:41:05Z)
• What Should I Cite? A RAG Benchmark for Academic Citation Prediction [24.99107629089983]
  Citation prediction aims to automatically suggest appropriate references, helping scholars navigate the expanding scientific literature.<n>Here we present textbfCiteRAG, the first comprehensive retrieval-augmented generation (RAG)-integrated benchmark for evaluating large language models on academic citation prediction.
  arXiv  Detail & Related papers  (2026-01-21T12:51:47Z)
• Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation [1.2232326171442904]
  This paper proposes a scientific idea generation system called GYWI.<n>It combines author knowledge graphs with retrieval-augmented generation (RAG) to form an external knowledge base.<n>The generated ideas are evaluated from the following five dimensions: novelty, feasibility, clarity, relevance, and significance.
  arXiv  Detail & Related papers  (2025-12-05T03:38:23Z)
• VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification [107.75781898355562]
  We introduce a novel framework, called VeriCite, designed to rigorously validate supporting evidence and enhance answer attribution.<n>We conduct experiments across five open-source LLMs and four datasets, demonstrating that VeriCite can significantly improve citation quality while maintaining the correctness of the answers.
  arXiv  Detail & Related papers  (2025-10-13T13:38:54Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [79.75818239774952]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Attribution in Scientific Literature: New Benchmark and Methods [41.64918533152914]
  Large language models (LLMs) present a promising yet challenging frontier for automated source citation in scientific communication.<n>We introduce REASONS, a novel dataset with sentence-level annotations across 12 scientific domains from arXiv.<n>We conduct extensive experiments with models such as GPT-O1, GPT-4O, GPT-3.5, DeepSeek, and other smaller models like Perplexity AI (7B)
  arXiv  Detail & Related papers  (2024-05-03T16:38:51Z)
• Scientific Paper Extractive Summarization Enhanced by Citation Graphs [50.19266650000948]
  We focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
  arXiv  Detail & Related papers  (2022-12-08T11:53:12Z)
• Momentum Decoding: Open-ended Text Generation As Graph Exploration [49.812280360794894]
  Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. We formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. We propose a novel decoding method -- textitmomentum decoding -- which encourages the LM to explore new nodes outside the current graph.
  arXiv  Detail & Related papers  (2022-12-05T11:16:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.