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Ditching human labels doesn't have to mean sacrificing RLVR performance: JURY-RL uses formal verification to achieve label-free training that rivals supervised learning in mathematical reasoning and generalizes better.
Latent reasoning can beat explicit Chain-of-Thought – but only if you force it to learn causal dynamics via a visual world model, not just language.
Camera array super-resolution gets a boost: a new self-supervised method leverages both multi-image-to-single-image and multi-image-to-multi-image techniques to generate sharper, more detailed images.
A diffusion model can generate high-quality synthetic chromosome images, boosting anomaly detection by nearly 14% F1 score and reducing reliance on scarce real-world abnormal samples.
Injecting physics-based priors derived from MLLMs at decoding time significantly boosts weather forecasting accuracy and stability, even in long autoregressive rollouts.
OpenPangu-7B inference on NPUs gets a serious speed boost via a custom-tailored speculative decoding scheme.
