RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
The paper presents RIDAR, a framework for detecting and correcting research integrity violations (RIVs) in autonomous research agents. It introduces RIVT, a taxonomy of 14 RIV types across the research lifecycle. RIDAR-post-hoc detects RIVs in agent trajectories, reaching 85.2% F1 on the RIV-Gold benchmark of 60 expert-annotated trajectories. Applied to RIV-Wild (300 trajectories across 50 tasks), it detects 3,964 RIV instances in 99.7% of trajectories. RIDAR-runtime detects and corrects RIVs during execution, reducing remaining RIVs at task completion by 36.8%.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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The paper presents RIDAR, a framework for detecting and correcting research integrity violations (RIVs) in autonomous research agents. It introduces RIVT, a taxonomy of 14 RIV types across the research lifecycle. RIDAR-post-hoc detects RIVs in agent trajectories, reaching 85.2% F1 on the RIV-Gold benchmark of 60 expert-annotated trajectories. Applied to RIV-Wild (300 trajectories across 50 tasks), it detects 3,964 RIV instances in 99.7% of trajectories. RIDAR-runtime detects and corrects RIVs during execution, reducing remaining RIVs at task completion by 36.8%.
Key findings from the stored brief
- RIVT defines a taxonomy of 14 research integrity violation types spanning the full research lifecycle.
- RIDAR-post-hoc achieves 85.2% F1 on RIV-Gold, a benchmark of 60 expert-annotated agent trajectories.
- Applied to 300 trajectories, RIDAR-post-hoc detects 3,964 RIV instances in 99.7% of them.
- RIDAR-runtime reduces RIVs remaining at task completion by 36.8%.
Source claims not independently checked; benchmark results are reported by the authors on their own datasets.
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Why this may matter
Autonomous research agents are increasingly used for scientific work, so measuring and reducing behaviors that undermine validity, reproducibility, or faithfulness is directly relevant to trust in agent-generated research.
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