ParanoiaEval: Benchmarking Unnecessary Defensive Work in Agentic Coding
[Sztuczna inteligencja] Autorzy: Hanjun Luo, Xiucheng Zhang, Zhuoning Xu i 4 więcej
As coding agents increasingly undertake real-world work autonomously, judging whether their risk treatments are warranted has become important. Existing work evaluates related agent behaviors from separate perspectives, but lacks a systematic framework for unifying these behaviors. To bridge this gap, we introduce ParanoiaEval, the first benchmark for unified evaluation of risk-treatment capabilities in coding agents. Grounded in the well-established Avoidance-Transfer-Mitigation-Acceptance framework in software engineering risk management, ParanoiaEval operationalizes its 4 fundamental treatments for coding-agent settings and contains 200 evidence-controlled repository-level task pairs, each differing only in treatment-defining evidence. We further introduce dedicated metrics for risk-treatment violations and evidence responsiveness, using a human-calibrated agentic judge for reliable …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja