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Hidden Decoding achieves unprecedented performance improvements in large language models by scaling computation along the sequence length without modifying the Transformer architecture.
Skill augmentation can elevate CLI agent performance beyond GUI counterparts, revealing that the true challenge lies in skill coverage rather than model capability.
VLMs struggle to spot even simple CSS changes in web interfaces, achieving only 40% accuracy on a new benchmark, DiffSpot, suggesting a surprising lack of fine-grained visual understanding.
Today's best AI agents can only solve 55% of real-world academic tasks that university students find challenging, revealing a significant gap between current AI capabilities and the demands of academic workflows.
Training a multimodal agent from scratch beats retrofitting existing LMMs with search tools, especially when you compress long interaction histories into visual summaries.
ALMs can now pinpoint sounds in time with far greater accuracy, thanks to a new training method that stops them from hallucinating timestamps.
AudioLLMs don't have to choose between reasoning and perception: a unified schema can boost both.
