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OPSD-V shows that incorporating real video context during training can dramatically enhance the performance of autoregressive video generators, leading to superior visual quality and motion fidelity.
Self-distillation in a verifiable environment enables web agents to achieve competitive performance without reliance on external teacher models.
Forget scalar rewards: GenEvolve distills structured visual experiences from successful and failed generation trajectories, enabling token-level supervision for self-improving image generation agents.
Ditch raster scanning: this new RWKV-based pan-sharpening method uses semantic prototypes for context-aware token reordering, boosting performance.
Achieve lifelike character animation with 10x faster inference using Kling-MotionControl, a DiT-based framework that intelligently handles body, face, and hand motions.
