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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.
Autonomous agents struggle to retain instructions when burdened with retrieving information from the open web, exposing a critical retrieval-reasoning trade-off.
Domain-specialized LLMs can regain lost general skills without sacrificing their expertise, thanks to a new distillation method that disentangles conflicting training signals.
