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Coding agents falter in collaborative settings, with a 7.7% drop in task resolution when users modify code, revealing a crucial gap in their adaptability.
Implicit activation steering enables LLMs to autonomously execute tasks with a memory system that outperforms traditional explicit instruction methods.
LLMs can be made to reason much better by directly optimizing their pre-training output distribution, even before fine-tuning on specific tasks.
Squeezing the most out of your MLLM's visual budget is now possible: ResAdapt learns to allocate visual tokens intelligently *before* encoding, boosting performance by 15% while processing 16x more frames at the same cost.
