Toward Domain-specific Innovation Discovery in Computer Science Research

Recent advances in Large Language Models (LLMs) have substantially improved scientific hypothesis formulation and research idea generation. However, existing approaches often produce superficial recombinations of prior solutions, while external literature resources rarely provide the fine-grained, structured evidence required to develop technically coherent ideas for complex and interdisciplinary research problems. To address these limitations, we present CSMuse, an evidence-grounded idea-generation system that leverages cross-domain knowledge. CSMuse incorporates three key designs: (1) domain-specific scientific knowledge construction, (2) evidence-grounded idea generation and validation, and (3) cross-domain multi-agent ideation and refinement. Evaluations across multiple LLM backbones and research domains demonstrate that \system consistently generates high-quality, novel ideas and outperforms state-of-the-art automated scientific discovery systems.