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Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures

Research LLMs & Foundation Models

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TL;DR - RLCDAlignBench evaluates Jev as a zero-shot detector across ten types of AI alignment failures. A single generic question achieves a median AUROC of 0.886 while costing 63Ă— less than LLM-judge scoring.

  • The benchmark covers 44 datasets and five target models, including failures such as jailbreaks, deception, hallucination, prompt injection, and reward hacking.
  • Jev answers multiple typed questions about an input with calibrated probabilities in one call, unlike generative judges that decode separately for each criterion.
  • Input context matters more than question wording, particularly when supplied fields encode information relevant to the label.
  • Jev beats supervised baselines on most benchmarks, matches reference scorers’ agreement with human labels, and exposes labeling defects in existing evaluations.

Sources (1)

Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures

arXiv cs.AI Ruoqi Guo, Yi Liu, Gelei Deng, Yuekang Li, Lida Zhao, Yutao Wu, Simin Chen, Ying Zhang, Leo Yu Zhang 2026-09-24 arXiv:2609.29429
Public signals Hugging Face upvotes 3
Providers: Hugging Face · Upvotes 3 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-26 14:04:03.466270 UTC

TL;DR - RLCDAlignBench evaluates Jev as a zero-shot detector across ten types of AI alignment failures. A single generic question achieves a median AUROC of 0.886 while costing 63Ă— less than LLM-judge scoring.

  • The benchmark covers 44 datasets and five target models, including failures such as jailbreaks, deception, hallucination, prompt injection, and reward hacking.
  • Jev answers multiple typed questions about an input with calibrated probabilities in one call, unlike generative judges that decode separately for each criterion.
  • Input context matters more than question wording, particularly when supplied fields encode information relevant to the label.
  • Jev beats supervised baselines on most benchmarks, matches reference scorers’ agreement with human labels, and exposes labeling defects in existing evaluations.
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