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Hierarchical visual concepts learned through cascaded sparse autoencoders could revolutionize how we interpret and manipulate MLLM outputs.
Stop wasting time on manual LLM domain adaptation: AutoAdapt automates the process and boosts accuracy by 25% over existing AutoML methods.
A 4B parameter SLM can now rival frontier agent performance in complex tool-use environments, thanks to a novel reinforcement finetuning framework that teaches it how to strategically acquire context and execute actions.
