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Current multimodal large language models struggle with dynamic stance understanding, particularly when relational inference is required, revealing critical gaps in their reasoning capabilities.
ShopX transforms agentic shopping by seamlessly translating complex intents into item-space actions, outperforming traditional retrieval-based systems.
Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
Forget retraining: GenRecEdit injects knowledge about new items into generative recommendation models, boosting cold-start performance by up to 10x while slashing training time by 90%.
