ResearchLoop: An Evidence-Gated Control Plane for AI-Assisted Research
A recent technical report unveils ResearchLoop, a control plane that utilizes evidence-gating to mitigate publication risks in AI-driven computational research. This innovative system categorizes research inquiries, task agreements, evidence items, claim records, project closures, and paper bindings as stable project states, functioning as a repository-supported runtime. Included in the report are detailed specifications of the protocol, state model, transition guidelines, claim-admission algorithm, and insight-compounding process. It also presents a comprehensive experimental record spanning nine versions (V0–V9), featuring a self-hosting case study, a controlled task-suite analysis with component ablations, a mathematical olympiad assessment, and an additional SciCode boundary experiment. This initiative addresses the merging of ideation, execution, evaluation, and manuscript preparation into a singular interactive loop, complicating the audit of paper claims.
Key facts
- ResearchLoop is an evidence-gated control plane for AI-assisted computational research.
- It treats research questions, task contracts, evidence objects, claim ledgers, closeouts, and paper bindings as durable project state.
- The system is realized as a repository-backed runtime.
- The report includes the complete protocol specification, state model, transition rules, claim-admission algorithm, and insight-compounding mechanism.
- The experimental record spans nine versions (V0–V9).
- Experiments include a self-hosting case study, a controlled task-suite study with component ablations, a mathematical olympiad evaluation, and a supplementary SciCode boundary experiment.
- The work addresses publication risks from compressing ideation, implementation, evaluation, and manuscript writing into a single interactive loop.
- The report is published on arXiv with identifier 2605.28282.
Entities
Institutions
- arXiv