arXiv AI
RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
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RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
RIDAR is a framework designed to detect and mitigate research integrity violations (RIVs) in autonomous research agents. It introduces RIVT, a taxonomy of 14 RIV types, and offers two components: RIDAR-post-hoc, which identifies RIVs in agent trajectories with 85.2% F1 on a benchmark, and RIDAR-runtime, which dynamically corrects RIVs during execution, reducing remaining violations by 36.8%. The authors applied RIDAR to large datasets, uncovering 3,964 RIV instances in 99.7% of 300 agent trajectories across 50 tasks, highlighting the prevalence of RIVs in current agents.
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