ResearchLoop: An Evidence-Gated Control Plane for AI-Assisted Research
For researchers using AI tools, this work addresses the risk of unverifiable claims in AI-assisted publications by providing a structured protocol for evidence tracking.
ResearchLoop introduces an evidence-gated control plane for AI-assisted research that enforces claim verification through durable project state and transition rules. The system was evaluated across nine versions with multiple studies including a self-hosting case study and controlled task-suite ablations.
AI-assisted research compresses ideation, implementation, evaluation, and manuscript writing into a single interactive loop. This compression is useful, but it also creates a publication risk: paper claims can become easier to state than to audit. We present ResearchLoop, an evidence-gated control plane for AI-assisted computational research. ResearchLoop treats research questions, task contracts, evidence objects, claim ledgers, closeouts, and paper bindings as durable project state, realized here as a repository-backed runtime. This technical report provides the complete protocol specification, state model, transition rules, claim-admission algorithm, and insight-compounding mechanism. It also reports the full experimental record spanning nine versions (V0--V9), including a self-hosting case study, a controlled task-suite study with component ablations, a mathematical olympiad evaluation, and a supplementary SciCode boundary experiment evaluated with the official generated-code harness. All artifacts, manifests, and verification reports are preserved in the project repository.