The Year in AI Papers
The year/Independent research

Paper 2607.04439

ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes

Qihao Zhao, Yangyu Huang, Yalun Dai, Lingao Xiao, Jianjun Gao, Xin Zhang, Wenshan Wu, Scarlett Li, Yang He, Yan Lu, Yap Kim Hui

Published
Jul 2026
Research lab
Independent
Citations
1
GitHub
2.1K stars

01 In brief

Summary

ResearchStudio-Idea is a suite of three skills for evidence-grounded research ideation in machine learning.

It includes Paper-Search for multi-source literature grounding, Scoop-Check for prior-art collision checking, and IdeaSpark, an end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card rendering.

The suite is built from a corpus of 1,947 papers from ICLR, ICML, and NeurIPS (2021-2025), including Oral, high-citation, and rejected submissions.

Analysis of these outcomes revealed 31 recurring ideation sub-patterns, consolidated into 15 reusable ideation patterns, each operationalized as a structured card.

IdeaSpark evaluates evidence readiness, identifies bottlenecks, selects patterns, instantiates a candidate direction, retrieves conflicting prior work, and performs outcome-informed auditing.

Blind automated-judge evaluations show IdeaSpark produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty.

The results suggest that large-scale conference outcomes contain reusable signals about how impactful research directions are formulated, differentiated, and evaluated, and that these signals can be operationalized as practical skills for evidence-grounded research ideation.

02 From the paper

Abstract

Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate directions. Researchers must ground a problem in current literature, identify meaningful bottlenecks, differentiate from existing solutions, and evaluate risks before committing to implementation. We present ResearchStudio-Idea as a reusable skill suite for this first mile of research ideation. The suite includes Paper-Search, a standalone multi-source literature search skill; Scoop-Check, a standalone prior-art collision checker for novelty claims; and IdeaSpark, the end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card rendering into one workflow. IdeaSpark is constructed from a corpus of 1,947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025, including Oral papers, a separately tracked high-citation subset, and rejected submissions. Analysis of these outcomes reveals 31 recurring ideation sub-patterns, consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research contexts, bottleneck types, differentiation strategies, supporting precedents, and common failure modes. Given a research problem and an evidence bundle, IdeaSpark evaluates evidence readiness, reconstructs the surrounding research context, identifies unresolved bottlenecks, selects relevant patterns, instantiates one candidate direction, retrieves potentially conflicting prior work, and performs outcome-informed auditing. This workflow transforms reusable ideation patterns into traceable research proposals. Blind automated-judge evaluations show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty.