Research Idea Generation

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3 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

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Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies
Apr 22, 2026Shuai Chen, Chengzhi ZhangMulti-Agent LLM Systems Computational Creativity
Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094 China
Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature
Apr 14, 2026Jinkai Tao, Yubo Wang, Xiaoyu Liu +1Scientific Discovery Scientific Hypothesis Generation
TsingyuAI, Beijing, China · School of Information, Central University of Finance and Economics, Beijing, China · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China +1
Learning to Predict Future-Aligned Research Proposals with Language Models
Mar 28, 2026Heng Wang, Pengcheng Jiang, Jiashuo Sun +4LLM Evaluation Research Idea Generation
University of Illinois Urbana-Champaign
AI Can Learn Scientific Taste
Mar 15, 2026Jingqi Tong, Mingzhe Li, Hangcheng Li +20AI for Science Scientific Hypothesis Generation
Fudan University · Shanghai Innovation Institute · OpenMOSS Team +2
LDC: Learning to Generate Research Idea with Dynamic Control
Dec 19, 2024Ruochen Li, Liqiang Jing, Chi Han +2Controllable Text Generation Research Idea Generation
University of Texas at Dallas · UIUC · Stony Brook University
Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
May 27, 2024Xuemei Gu, Mario KrennAI-Assisted Scientific Research Scientific Hypothesis Generation
Max Planck Institute for the Science of Light, Staudtstrasse 2, 91058 Erlangen, Germany

Apr 22, 2026, cs.CLJ/K move · Enter open · S save

Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies

Shuai Chen, Chengzhi Zhang

Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094 China

Multi-Agent LLM Systems Computational Creativity Research Idea Generation Large Language Model-Guided Scientific Discovery

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Scientific progress depends on the continual generation of innovative re-search ideas. However, the rapid growth of scientific literature has greatly increased the cost of knowledge filtering, making it harder for researchers to identify novel directions. Although existing large language model (LLM)-based methods show promise in research idea generation, the ideas they produce are often repetitive and lack depth. To address this issue, this study proposes a multi-agent iterative planning search strategy inspired by com-binatorial innovation theory. The framework combines iterative knowledge search with an LLM-based multi-agent system to generate, evaluate, and re-fine research ideas through repeated interaction, with the goal of improving idea diversity and novelty. Experiments in the natural language processing domain show that the proposed method outperforms state-of-the-art base-lines in both diversity and novelty. Further comparison with ideas derived from top-tier machine learning conference papers indicates that the quality of the generated ideas falls between that of accepted and rejected papers. These results suggest that the proposed framework is a promising approach for supporting high-quality research idea generation. The source code and dataset used in this paper are publicly available on Github repository: https://github.com/ChenShuai00/MAGenIdeas. The demo is available at https://huggingface.co/spaces/cshuai20/MAGenIdeas.

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Related topics

Latest papers 36

Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies

Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature

Learning to Predict Future-Aligned Research Proposals with Language Models

AI Can Learn Scientific Taste

LDC: Learning to Generate Research Idea with Dynamic Control

Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders