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Recommender systems can move beyond passive item lists: RecPilot's multi-agent framework autonomously explores item spaces and generates user-centric reports, significantly reducing user effort in item evaluation.
Image-to-video models can be jailbroken by hiding malicious instructions in seemingly harmless reference images, achieving an 83.5% attack success rate on commercial systems.
LLMs learn to recommend better by looking inside themselves, using intermediate layer activations to generate harder negatives on the fly.
