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ATLAS reveals that understanding both immediate request trajectories and long-term user interactions can dramatically enhance the performance of industrial tool-use agents.
Aligning LLM reasoning with a dedicated recommendation head via reinforcement learning yields state-of-the-art recommendation performance in real-world systems.
A lightweight architecture that distills long textual sequences using visual tokens as dynamic queries boosts LLM performance on 2D table understanding by 23.9%.
LLMs can now retrieve memories like humans, using a fast familiarity check or a deliberate recollection process, leading to better personalization without overwhelming the model with irrelevant context.
Forget independent feature extraction: a new architecture uses LVLMs to explicitly model the relationships between drone and satellite imagery, substantially boosting geolocalization accuracy.
