AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Vision Language Models (VLMs) excel on visual benchmarks but fail systematically on tasks requiring abstract reasoning. Existing benchmarks document this failure but cannot say \emph{why} it happens or which cognitive capability is missing. We close this gap by adopting the Relational Match-to-Sample (RMTS) paradigm fr...
VIF-Bench is introduced, a benchmark of 1,241 tasks designed to assess the edge of model capabilities in this joint setting in which multiple references must be composed under multiple and heterogeneous visual-instruction images, and uncover three findings.
This work proposes AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding agent iteratively rewrites the harness code, and discovers AutoRef-Harness, which improves the open-weight FLUX.
This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.
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