💡 The paper presents ResearchStudio-Idea, a skill suite designed to support effective research ideation by combining literature search, novelty checking, and pattern-guided generation. The goal is to help researchers develop well-grounded research proposals by identifying meaningful bottlenecks, differentiating from existing solutions, and evaluating risks. The suite consists of three main components: Paper-Search, a multi-source literature search skill, Scoop-Check, a prior-art collision checker, and IdeaSpark, an end-to-end skill that composes evidence grounding, pattern-guided generation, and idea-card rendering into one workflow.

IdeaSpark is constructed from a corpus of 1947 machine learning conference papers collected from ICLR, ICML, and NeurIPS between 2021 and 2025. Analysis of these papers reveals 31 recurring ideation sub-patterns, which are consolidated into 15 reusable ideation patterns. Each pattern is operationalized as a structured card containing research context, 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.

The results show that IdeaSpark consistently produces stronger research proposals than no-skill and generic-skill baselines while maintaining competitive novelty. The evaluation is based on blind automated-judge assessments, which demonstrate the effectiveness of ResearchStudio-Idea in supporting research ideation. Overall, the paper contributes a reusable skill suite that can help researchers develop well-grounded research proposals by leveraging evidence-grounded research ideation and pattern-guided generation.