Graph2Idea builds dynamic knowledge graphs from retrieved literature to supply compact, relational contexts that guide LLMs in generating novel, feasible, and high-quality scientific ideas, outperforming flat-text baselines on automatic metrics.
Weiyan Shi and Kenny Tsu Wei Choo
3 Pith papers cite this work. Polarity classification is still indexing.
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AutoBG is a multi-module AI assistant that uses critic-driven iterative refinement on LLM-generated rulebooks, trained on 2.2K rulebooks and 180K reviews, to produce audience-tested designs that outperform GPT-5.4 baselines.
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
citing papers explorer
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Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts
Graph2Idea builds dynamic knowledge graphs from retrieved literature to supply compact, relational contexts that guide LLMs in generating novel, feasible, and high-quality scientific ideas, outperforming flat-text baselines on automatic metrics.
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AutoBG: A Board Game Design Assistant with Interactive Ideation, Iterative Rulebook Generation, and Individualized Feedback
AutoBG is a multi-module AI assistant that uses critic-driven iterative refinement on LLM-generated rulebooks, trained on 2.2K rulebooks and 180K reviews, to produce audience-tested designs that outperform GPT-5.4 baselines.
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AI for Auto-Research: Roadmap & User Guide
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.