RIGOR: Dual Governance for Research Intent Integrity in Long-Horizon Autonomous Research

Long-horizon autonomous research requires agents to maintain project-level scientific intent while repeatedly designing, executing, and interpreting experiments. Persistent single-agent loops often mix long-term research objectives with transient implementation details and debugging histories, creating substantial context accumulation. Decomposing the workflow into a persistent Research Agent and transient Execution Agents can reduce this burden, but it also introduces new handoff boundaries where execution fidelity may be compromised and unsupported evidence may enter the scientific state. We formalize Research Intent Integrity (RII) as the requirement that material deviations become part of the authoritative research state only when they are authorized, justified, and provenance-preserving. Based on this principle, we introduce a dual-governance architecture consisting of an Intent-Grounded Research Loop (IGRL) for governing evidence-to-state updates and research termination, and Scientific Execution Governance (SEG) for binding experiment briefs with candidate implementations, formal executions, and sealed evidence receipts. We instantiate these mechanisms in RIGOR (Research Intent Governance for Open-ended Research), an end-to-end role-decomposed AutoResearch system for long-horizon, evidence-driven discovery. Execution-level evaluations show that SEG improves experimental fidelity and prevents unsupported deviations from entering canonical evidence with modest computational overhead. Long-horizon studies further demonstrate that role decomposition reduces persistent context accumulation, while integrating IGRL and SEG preserves research intent throughout iterative research updates compared with ungoverned architectures. Evaluations on public AutoResearch benchmarks show that RIGOR maintains competitive end-to-end performance while providing stronger control over scientific state transitions.