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Decouple complex audio scenes into individual, controllable sound sources directly from a single generative model.
Humanoid robots struggle to bridge the gap between selecting the right tool and executing complex tasks, with current policies showing significant performance degradation on unseen tools and unrelated instructions.
RLMF not only boosts LLMs' ability to accurately express uncertainty but also enhances their self-assessment capabilities, fundamentally reshaping their trustworthiness.
Cross-lingual exploration can unlock hidden knowledge in LLMs, improving factual recall and consistency across 17 languages.
Autoregressive policies can achieve real-time execution with superior performance and speed, challenging the dominance of diffusion-based approaches.
VLMs confidently hallucinate answers to spatial reasoning questions even when visual evidence is occluded or misleading, achieving near-random performance in identifying viewpoints that could resolve the ambiguity.