Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization
Mechanical checks plus LLM review cut errors when verifying Lean formalizations of NIST math functions.
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Mechanical checks plus LLM review cut errors when verifying Lean formalizations of NIST math functions.
Manacá-1B-Instruct-2 gained safety from iterated safety finetuning after three failed DPO runs, beating four Portuguese baselines.
A position-masking self-distillation method trains LLM judges from language feedback and beats outcome-only RL on subjective tasks.
RLCDAlignBench benchmarks Jev, a calibrated-decision model detecting ten AI alignment failure types zero-shot at 0.886 median AUROC, 63x cheaper than LLM judges.
OSWorld-Pro adds process-level subgoals so computer-use agent failures can be diagnosed beyond final outcomes.
OSWorld-Pro scores computer-use agents on over 300 tasks; Claude Opus 5 reaches 75.7%.
Checks that trust attacker-controlled data can forge success verdicts in automated AI security testing.
Researchers introduce Live-ProsodyJudge and D-LPJ, Gemini-distilled Qwen3-Omni judges that decouple multi-dimensional prosody scores for live streaming TTS evaluation.
Training LLM reviewers on model-generated reviews compresses rating distributions and diversity, a failure the authors name scientific-judgment collapse and mitigate with TrustReviewer.
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