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FACET achieves unprecedented task synthesis quality by preserving source intent and ensuring executable state consistency, leading to more reliable terminal agents.
Video-DeepResearch shatters the imitation-learning ceiling, achieving a 64% accuracy in complex video-based question answering, far surpassing existing models.
N眉shu-PitchVITS not only revives an endangered script but also sets a new benchmark in low-resource TTS systems by achieving superior speech synthesis quality through innovative pitch conditioning.
Unlock long-context reasoning in LLMs by turning agent trajectories into gold-standard QA pairs, outperforming models 8x larger on challenging reasoning tasks.
Even the best LLMs struggle to effectively discover, refine, and reuse skills over a lifetime of experience, suggesting current benchmarks significantly overestimate real-world agentic capabilities.
