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Seedance 2.0 leapfrogs existing models by unifying multi-modal inputs (text, image, audio, video) into a single architecture for generating high-quality, longer-duration audio-video content.
LLMs still fail to demonstrate expert-level proficiency, achieving only ~66% success on a new benchmark of real-world professional tasks spanning finance, healthcare, and law.
Recommendation systems can now systematically debias engagement signals across user, content, and model dimensions using a lightweight, in-model approach, leading to more accurate value models and stable ecosystem dynamics.
Image generation models can now reason about spatial relationships with significantly improved accuracy thanks to a novel reinforcement learning framework that iteratively refines images based on spatial consistency checks.
