Introduces Defensibility Index, Ambiguity Index, and Probabilistic Defensibility Signal to evaluate AI moderation decisions by logical derivability from explicit rules rather than agreement with historical labels, with validation on 193k+ Reddit cases showing 33-46.6 pp metric gaps and a Governance
Examining reasoning llms-as-judges in non-verifiable llm post-training
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Auto-Rubric as Reward externalizes VLM preferences into structured rubrics and applies Rubric Policy Optimization to create more reliable binary rewards for multimodal generation, outperforming pairwise models on text-to-image and editing benchmarks.
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Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI
Introduces Defensibility Index, Ambiguity Index, and Probabilistic Defensibility Signal to evaluate AI moderation decisions by logical derivability from explicit rules rather than agreement with historical labels, with validation on 193k+ Reddit cases showing 33-46.6 pp metric gaps and a Governance
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Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
Auto-Rubric as Reward externalizes VLM preferences into structured rubrics and applies Rubric Policy Optimization to create more reliable binary rewards for multimodal generation, outperforming pairwise models on text-to-image and editing benchmarks.