AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TTR-Compressor is evaluated as the compressor for block-wise sparse attention and the visual token merger for multimodal large language models and improves RULER performance over attention-based compressors by a large margin.
Experiments show that UI-Mate-27B sets a new open-weight state of the art on general computer-use benchmarks, substantially improving long-horizon reliability, and makes three contributions to an environment-grounded training stack with in-context demonstration learning.
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