arXiv cs.AI· Jiening Siow, Amy Xin, Junjie Wang, Ziyu Tang, Zijun Yao, Yunjia Qi, Fanjin Zhang, Lei Hou, Jie Tang, Juanzi Li·· 2 天前AI 评分38
RIDAR：通过自动检测与运行时干预保障自主研究智能体的科研诚信
RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
AI 导读
RIDAR 框架用于自动检测并干预自主研究智能体的科研诚信违规（RIV），其检测框架 RIDAR-post-hoc 在 60 条专家标注轨迹的 RIV-Gold 基准上取得 85.2% F1。
正文 · 原文
Abstract:Autonomous research agents are increasingly used to automate large portions of scientific research, yet they are prone to research integrity violations (RIVs): behaviors that compromise the validity, reproducibility, or faithfulness of the research process. We present RIDAR, a framework for automated RIV detection and runtime intervention. First, we introduce RIVT, a comprehensive taxonomy of 14 RIV types spanning the full research lifecycle. Building on RIVT, we present RIDAR-post-hoc, a framework to detect RIVs within agent trajectories. RIDAR-post-hoc achieves 85.2% F1 performance on RIV-Gold, a benchmark for RIV detection with 60 expert-annotated trajectories. Applying RIDAR-post-hoc to RIV-Wild, a dataset of 300 agent trajectories across 50 distinct research tasks, we detect 3,964 RIV instances in 99.7% of trajectories, revealing the prevalence of RIVs in current agents. Finally, we present RIDAR-runtime, a framework for dynamically detecting and correcting RIVs during agent runtime. RIDAR-runtime effectively reduces agent RIVs remaining at task completion by 36.8%. We will release our code and data to facilitate future research.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.31372 [cs.AI] |
| (or arXiv:2610.31372v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31372 arXiv-issued DOI via DataCite |
Submission history
From: Jiening Siow [view email]
[v1]
Thu, 22 Oct 2026 14:43:36 UTC (857 KB)
[v2]
Sun, 25 Oct 2026 14:33:29 UTC (857 KB)
来源：arXiv cs.AI · arxiv.org