arXiv AI
Benchmarking candidate coverage and rejection policy transfer in typed decision models
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Benchmarking Candidate Coverage in Typed Decision Models
The paper introduces a paired candidate‑coverage benchmark protocol for typed decision models, evaluating two models—Laya and Jev—on datasets such as AG News, DBpedia, Emotion, and TREC. It reports that Laya detects a high percentage of missing-answer cases but also falsely rejects many valid candidates, whereas Jev shows lower false rejection rates but also lower detection of missing answers. The study highlights the need for separate measurements of classification, score ranking, and rejection policies, noting that the benchmark is descriptive and limited to reference‑label omission.
arXiv AI
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Evaluating and Benchmarking the System One Model Jev
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Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
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Small Language Models as Judges for Rubric-Based Reinforcement Learning
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Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures
The paper introduces Jev, a reinforcement‑learning‑trained model that provides calibrated probability answers to typed questions about a single input in one call. Jev is evaluated on RLCDAlignBench, a benchmark covering ten alignment failures across 44 tests and five target models, achieving a median AUROC of 0.886 zero‑shot and outperforming supervised baselines on most tasks. The study shows that question wording has little impact, while contextual fields that encode labels are more influential, and that Jev matches human‑label agreement while being 63× cheaper than LLM‑judge scorers.
arXiv AI
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Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling
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