Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures
Jev detects ten alignment failures zero-shot, with a median AUROC of 0.886 on RLCDAlignBench.
Researchers introduce Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), which answers many typed questions about one input and returns calibrated probabilities in a single call. RLCDAlignBench evaluates it on ten alignment failures, including sycophancy, jailbreaks, deception, prompt injection, hallucination, and power seeking, across 44 benchmarks and five target models. A single generic question reaches a median AUROC of 0.886 zero-shot, beats supervised baselines on most benchmarks, matches reference scorers' agreement with human labels, and costs 63 times less than LLM-judge scorers. Question wording matters little; performance depends more on input fields, especially those that encode the label.
- Jev scores many typed questions about one input with calibrated probabilities in one call.
- RLCDAlignBench covers ten failure types across 44 benchmarks and five target models.
- A generic question reaches median AUROC 0.886 zero-shot and beats most supervised baselines.
- Jev matches human-label agreement of reference scorers and costs 63x less than LLM judges.
- Input context, especially label-encoding fields, matters more than question wording.
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Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on every criterion, and classifiers that read token probabilities, such as Llama Guard, still score one fixed label per call. Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), answers many typed questions about one input with calibrated probabilities in a single call. Whether it detects alignment failures has not been measured. We present RLCDAlignBench, which benchmarks Jev on ten alignment failures: sycophancy, jailbreaks, deception, prompt injection, hallucination, privacy violation, social bias, reward hacking, concealing uncertainty, and power seeking. It spans 44 benchmarks and five target models, labelled by each benchmark's scorer and, on two, by humans. Many of these failures are relational, defined against a reference, such as the user's belief or an injected instruction, that the response alone does not reveal. Our key idea is therefore to vary what Jev is asked separately from what it sees: the question's wording and answer type on one side, the fields of the input on the other. A single generic question reaches a median AUROC of 0.886 zero-shot and beats supervised baselines on most benchmarks. Question wording matters little, while context matters more, mostly through fields that encode the label. Jev matches the reference scorer's agreement with human labels, surfaces label defects in existing benchmarks, and costs 63x less than LLM-judge scorers. Code and data: https://github.com/sumleo/RLCDAlignBench.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.29429