π€ MultiAgent Research Ideator [Paper (PDF)]
Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Accepted at SIGDIAL 2025 π β Read on arXiv π
How can we design multi-agent LLM systems to generate better research ideas? MultiAgent Research Ideator is the research platform behind our SIGDIAL 2025 paper that systematically explores multi-agent dialogue design for research ideation. Through empirical analysis of 7,000 generated ideas, we identify optimal configurations for AI-powered research brainstorming.
Our systematic study investigates three key design dimensions:
- π€ Agent Parallelism: Multiple simultaneous critics vs. sequential feedback
- π Interaction Depth: Optimal number of critique-revision iterations
- π¬ Agent Diversity: Domain-specific expert personas (Physics-AI, Chemistry-AI, etc.)
- Agent Parallelism: Increasing the number of critics improves idea diversity but tends to reduce idea quality.
- Interaction Depth: Deepening the critique-revision interactions enhances diversity without compromising quality.
- Critic Diversity: Introducing diverse critic agents improves idea quality while maintaining diversity.
- Proposer/Reviewer Diversity: Increasing diversity among proposers or reviewers boosts idea diversity with little effect on quality.
- 7,000 Ideas Generated: Across 7 AI/NLP research topics with multiple multi-agent configurations.
- Diversity Measured by Duplicate Filtering: Based on semantic similarity after duplicate removal.
- Quality Assessed by GPT-4 Tournament Ranking: Systematic pairwise comparisons using a zero-shot judge model.
# Install dependencies
uv sync
# Set API keys
export api_key="your_openai_api_key"
export s2_key="your_semantic_scholar_api_key"
export anthropic_key="your_anthropic_key" # optional# 1. Literature Review
./scripts/run_lit_review.sh
# 2. Generate Ideas (Multiple Configurations)
./scripts/generate_ideas_and_dedup.sh
# 3. Tournament Evaluation
./scripts/evaluate_ideas.sh# Baseline: Single agent
python multiagent_research_ideator/src/grounded_idea_gen.py \
--discussion_type "single"
# Our optimal: 3 parallel critics
python multiagent_research_ideator/src/grounded_idea_gen.py \
--discussion_type "parallel_self_critique-3"
# Domain expertise injection
python multiagent_research_ideator/src/grounded_idea_gen.py \
--discussion_type "diff_personas_proposer_reviser"Literature Review β Multi-Agent Ideation β Semantic Deduplication β
Tournament Ranking β Top Research Proposals
single: No critiquebaseline: Self-critique with single iterationparallel_self_critique-N: N parallel critics provide simultaneous feedbackiterative_self_critique-N: N sequential critique-revision roundsdiff_personas_proposer_reviser: Domain-specific expert personas for different roles
multiagent_research_ideator/
src/ # Core system modules
lit_review.py # Literature discovery & scoring
grounded_idea_gen.py # Multi-agent idea generation
tournament_ranking.py # Evaluation framework
...
prompts/ # Expert personas & templates
scripts/ # End-to-end pipelines
logs/ # Generated ideas & evaluations
If you use this work in your research, please cite our SIGDIAL 2025 paper:
@inproceedings{ueda-etal-2025-exploring,
title = {Exploring the Design of Multi-Agent {LLM} Dialogues for Research Ideation},
author = {Ueda, Keisuke and Hirota, Wataru and Asakura, Takuto and Omi, Takahiro and Takahashi, Kosuke and Arima, Kosuke and Ishigaki, Tatsuya},
booktitle = {Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
month = aug,
year = {2025},
address = {Avignon, France},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.sigdial-1.26/},
pages = {322--337}
}
This project builds upon and extends ideas from the excellent AI-Researcher project. We gratefully acknowledge their contributions.
MIT License - feel free to use this for your research and projects!

