Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network

• URL: http://arxiv.org/abs/2511.02238v1
• Date: Tue, 04 Nov 2025 04:00:20 GMT
• Title: Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network
• Authors: Keyu Zhao, Weiquan Lin, Qirui Zheng, Fengli Xu, Yong Li,
• Abstract summary: We propose a framework to integrate a scientific network that captures keyword co-occurrence and contextual relationships.<n>A critic engine, trained on real-world reviewer feedback, guides the process by providing continuous feedback on the novelty and feasibility of ideas.<n>Our approach improves the quality of generated ideas by 10.67% compared to other methods, with ideas surpassing top conference acceptance levels.
• Score: 9.317340414316446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Novel research ideas play a critical role in advancing scientific inquiries. Recent advancements in Large Language Models (LLMs) have demonstrated their potential to generate novel research ideas by leveraging large-scale scientific literature. However, previous work in research ideation has primarily relied on simplistic methods, such as keyword co-occurrence or semantic similarity. These approaches focus on identifying statistical associations in the literature but overlook the complex, contextual relationships between scientific concepts, which are essential to effectively leverage knowledge embedded in human literature. For instance, papers that simultaneously mention "keyword A" and "keyword B" often present research ideas that integrate both concepts. Additionally, some LLM-driven methods propose and refine research ideas using the model's internal knowledge, but they fail to effectively utilize the scientific concept network, limiting the grounding of ideas in established research. To address these challenges, we propose the Deep Ideation framework to address these challenges, integrating a scientific network that captures keyword co-occurrence and contextual relationships, enriching LLM-driven ideation. The framework introduces an explore-expand-evolve workflow to iteratively refine research ideas, using an Idea Stack to track progress. A critic engine, trained on real-world reviewer feedback, guides the process by providing continuous feedback on the novelty and feasibility of ideas. Our experiments show that our approach improves the quality of generated ideas by 10.67% compared to other methods, with ideas surpassing top conference acceptance levels. Human evaluation highlights their practical value in scientific research, and ablation studies confirm the effectiveness of each component in the workflow. Code repo is available at https://github.com/kyZhao-1/Deep-Ideation.

Related papers

• Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas [35.25560221100292]
  New ideas need to be situated within an ever-expanding landscape of existing knowledge.<n>Current embedding approaches conflate distinct conceptual aspects into single representations.<n>We introduce the Ideation Space, a structured representation that decomposes scientific knowledge into three distinct dimensions.
  arXiv  Detail & Related papers  (2026-01-13T18:56:11Z)
• The Budget AI Researcher and the Power of RAG Chains [4.797627592793464]
  Current approaches to supporting research idea generation often rely on generic large language models (LLMs)<n>Our framework, The Budget AI Researcher, uses retrieval-augmented generation chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers.<n>The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree.
  arXiv  Detail & Related papers  (2025-06-14T02:40:35Z)
• Harnessing Large Language Models for Scientific Novelty Detection [49.10608128661251]
  We propose to harness large language models (LLMs) for scientific novelty detection (ND)<n>To capture idea conception, we propose to train a lightweight retriever by distilling the idea-level knowledge from LLMs.<n> Experiments show our method consistently outperforms others on the proposed benchmark datasets for idea retrieval and ND tasks.
  arXiv  Detail & Related papers  (2025-05-30T14:08:13Z)
• Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents [64.64280477958283]
  An exponential increase in scientific literature makes it challenging for researchers to stay current with recent advances and identify meaningful research directions. Recent developments in large language models(LLMs) suggest a promising avenue for automating the generation of novel research ideas. We propose a Chain-of-Ideas(CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain.
  arXiv  Detail & Related papers  (2024-10-17T03:26:37Z)
• Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
  Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research.<n>VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.<n>We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
  arXiv  Detail & Related papers  (2024-10-12T07:16:22Z)
• IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback [26.860080743555283]
  Idea Synth is a research idea development system that uses literature-grounded feedback for articulating research problems, solutions, evaluations and contributions.<n>Our lab study (N) showed that participants, while using Idea Synth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline.<n>Our deployment study (N=7) demonstrated that participants effectively used Idea Synth for real-world research projects at various stages from developing initial ideas to revising framings of mature manuscripts.
  arXiv  Detail & Related papers  (2024-10-05T04:06:07Z)
• Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
  We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
  arXiv  Detail & Related papers  (2024-09-07T02:07:22Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Exploring and Verbalizing Academic Ideas by Concept Co-occurrence [42.16213986603552]
  This study devises a framework based on concept co-occurrence for academic idea inspiration. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. We generate a description of an idea based on a new data structure called co-occurrence citation quintuple.
  arXiv  Detail & Related papers  (2023-06-04T07:01:30Z)
• SciMON: Scientific Inspiration Machines Optimized for Novelty [68.46036589035539]
  We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. We take a dramatic departure with a novel setting in which models use as input background contexts. We present SciMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers.
  arXiv  Detail & Related papers  (2023-05-23T17:12:08Z)

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.