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Turns out, LLMs rely far more on raw code access than documentation when answering repository-level questions, challenging the assumption that documentation is the primary driver of code understanding.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
Revitalizing target-specific control within latent-query architectures for sequential CTR prediction yields consistent performance gains across diverse datasets and backbones, especially when combined with a simple position-aware reference.
Forget slow, complex training: you can now distill diffusion models to just 4 steps and still beat the state-of-the-art in preference alignment, aesthetics, and composition.
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.
Style transfer just got a whole lot easier: MegaStyle's 1.4M image dataset, generated via text-to-image models, unlocks reliable style similarity measurement and generalizable style transfer.
