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University of California, Santa Cruz
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Agents are great at setting up tasks but falter at validating and submitting, revealing a critical weakness in autonomous medical research workflows.
Stop hand-feeding your LLM clinical data: ClinSeekAgent actively seeks and synthesizes multimodal evidence, boosting Claude Opus's performance by 15% on multimodal tasks.
LLMs are still far from being autonomous scientists, failing to master even simplified, end-to-end physics research workflows.
LVLMs can be made significantly less prone to hallucinations, without any training, by explicitly grounding them in visual evidence and iteratively self-refining their answers based on verified information.
Current reward models for spoken dialogue systems are missing crucial paralinguistic and natural speech elements, but this new model closes the gap by operating directly on speech and outperforming existing audio LLMs.
Achieve adaptive, perception-aware image compression without any training by simply steering a pre-trained diffusion model.
Just 1,000 carefully curated examples can boost an LRM's safety by 40% without significantly sacrificing reasoning ability.
