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School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Current language agents are still far from matching human expert performance when faced with real-world professional tasks requiring complex reasoning, authoritative source retrieval, and domain-specific knowledge, as revealed by the new \$OneMillion-Bench benchmark.
By strategically warming up residual connections layer-by-layer, ProRes unlocks faster and more stable pretraining for language models.
Autonomous vehicles can now navigate complex environments more efficiently and smoothly thanks to a novel integration of improved A*, Fuzzy DWA, and Fuzzy PID control, outperforming traditional methods in real-world tests.
