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Today's video world models excel at hallucinating plausible-looking frames, but their ability to actually preserve initial object identities drops by over 40% as scene complexity scales beyond 40 instances.
Reward-guided optimization can be selectively applied to enhance generative recommendation performance, avoiding the pitfalls of uniform reinforcement learning.
GenRec proves that generative recommendation can beat existing pipelines in a large-scale industrial setting, achieving nearly 10% gains in key metrics by focusing on preference alignment and efficient sequence encoding.
