GlobalDentBench shows frontier LLMs drop from 81% accuracy on multiple-choice questions to 22% on case-based questions with 31% unsafe rates in dental clinical recommendations.
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5 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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Iterative self-finetuning of LLMs mostly fails to amplify seeded behavioral traits, with amplification limited to specific DPO setups and often harming coherence.
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
LLM safety judges resist adjusting evaluations when given contradictory context or new safety definitions, despite some ability to learn from new information.
An LLM produces consistent categorical judgments and appropriate confidence declines when evaluating powerline segmentation quality under controlled visual degradations, suggesting it can serve as a reliable watchdog.
citing papers explorer
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GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration
GlobalDentBench shows frontier LLMs drop from 81% accuracy on multiple-choice questions to 22% on case-based questions with 31% unsafe rates in dental clinical recommendations.
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Iterative Finetuning is Mostly Idempotent
Iterative self-finetuning of LLMs mostly fails to amplify seeded behavioral traits, with amplification limited to specific DPO setups and often harming coherence.
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Towards an AI co-scientist
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
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Safety is Contextual, LLM-Judges Are Not: Navigating the Rigid Priors of Evaluators
LLM safety judges resist adjusting evaluations when given contradictory context or new safety definitions, despite some ability to learn from new information.
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LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection
An LLM produces consistent categorical judgments and appropriate confidence declines when evaluating powerline segmentation quality under controlled visual degradations, suggesting it can serve as a reliable watchdog.