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A framework in which an AI builder turns a scientific brief into an executable AI lab: a task-specific solver that integrates models, knowledge resources, tools, and a controller behind a fixed interface is presented.
Findings suggest that AI can be easily persuaded by what people say, who says it, and how the opinion is presented, enabling its safe and reliable use in high stakes medical decision making.
Under matched backbone and inference settings, J-CoT-Zero matches or exceeds the strongest evaluated latent-reasoning baseline on every benchmark, while J-CoT-Train obtains the highest score across the evaluated mathematical, scientific, coding, and structured path-reasoning tasks.
A Dynamic, Automatic and Systematic red-teaming audit framework that continuously stress-tests LLMs for health across four safety-critical axes: robustness, privacy, bias and hallucination, which provides a scalable framework for surfacing latent risks before such systems are deployed in consumer-facing health assistan...
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