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LUMI revolutionizes lossless image compression by decoupling it from tokenizer behavior, achieving superior performance with a unified approach across different LLM architectures.
Long-tailed organ types in pathology reports can now be accurately generated, overcoming biases that have plagued previous automated systems.
Discretizing reward models can significantly enhance policy performance by reducing oversensitivity without sacrificing discriminative ability.
Achieving nearly 100% annotation acceptance on flowcharts and a 35.1% accuracy boost for VLMs, ScreenAnnotator redefines data annotation for complex visual reasoning tasks.
Robots can now maintain high performance in cable routing tasks even when faced with distorted image observations, thanks to a novel framework that intelligently assesses image quality.
