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A single verification prompt is insufficient; learning a reliability score from multiple, complementary perspectives unlocks robust self-verification for VLMs.
A self-evolving memory paradigm for generative recommendation is proposed, aiming to enable effective evolution across heterogeneous behavioral patterns, and three key principles for effective self-evolving recommendation systems are identified, including isolated memorization, reinforced evolution, and scalable application.
Self-verification in SVR-R1 not only boosts multimodal reasoning accuracy but also reduces the need for extensive verification, showcasing a path to more confident model outputs.
