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LingBot-VA 2.0 achieves few-shot generalization in complex robot manipulation tasks, outperforming traditional video generative models.
Explicit acoustic guidance in H-SAGE leads to significant performance improvements in multi-talker ASR, especially in challenging overlapping speech conditions.
Achieving a 93.1% improvement in training accuracy and 2x faster inference, Next Forcing redefines the efficiency of causal world modeling in video generation.
Training for speech editing with reinforcement learning not only enhances editing quality but also unexpectedly boosts zero-shot TTS performance.
LLM post-training isn't just about objectives; it's about strategically intervening on model behavior through support expansion, policy reshaping, and behavioral consolidation.
