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A million-scale dataset for identity-preserving video generation enables a new benchmark that outperforms existing models with minimal parameter overhead.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
Model-generated skills can actually hurt agent performance, and bigger models don't necessarily make for better skill extractors or consumers.
SkillOpt transforms agent skill development into a reproducible optimization process, achieving state-of-the-art results by treating skills as trainable parameters.
Hierarchical planning and self-reflection can finally wrangle AIGC tools into producing coherent, visually consistent webpages.
Today's best text-to-audio-video models may look and sound impressive, but they still struggle with basic physics, coherent speech, and even rendering text correctly.
