ResearchArcade: Graph Interface for Automated Research

AutoResearch systems increasingly assist with literature discovery, scientific writing, peer review, and manuscript revision. Yet they commonly rely on fragmented, task-specific views of academic data that fail to expose relations across research artifacts. We introduce ResearchArcade, a graph interface that unifies multi-source, multi-modal, and temporally evolving academic data for diverse research models and workflows. ResearchArcade organizes ArXiv papers and OpenReview peer-review activities in a coherent multi-table graph and exposes task-relevant neighborhoods through a common interface. We evaluate ResearchArcade across six academic tasks spanning the research process and two evidence-discovery workflows under both retrieval and agentic settings. The task-specific evaluations show that ResearchArcade supports diverse predictive and generative models and enables tasks that combine cross-source and multi-modal information. Controlled evaluations in the two AutoResearch workflows further show that correct graph connectivity enhances evidence-discovery capabilities in both fixed retrieval pipelines and interactive agents. Together, these findings establish ResearchArcade as a general graph interface for supporting diverse research models and workflows. Code, frozen splits, and evaluation artifacts are provided in the anonymous supplementary repository.