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Achieving millimeter-level precision in dynamic environments, FAST-LIVGO outperforms existing odometry systems by effectively managing drift and outlier challenges.
SB-RF achieves leading performance in speech enhancement with a one-step generative approach that outperforms traditional multi-step methods.
Most MLLMs still struggle with reflective decision-making and self-evaluation in Chinese mobile GUI environments, hindering their reliability in real-world interactions.
GPT-4's mobile proactivity is so bad (7.4% success) that a fine-tuned Qwen2 model more than doubles its performance, revealing a critical gap in current MLLMs and a path to improvement.
