AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The value of curated, capability-balanced supervision for adapting general language models to educational tasks spanning problem solving, curriculum understanding, and instructional support is demonstrated.
DataFlex-RL, an evaluation platform for comparing choices under a common GRPO recipe, is introduced, finding that changing the data policy measurably changes the training process but does not produce a reproducible improvement over uniform training.
WorkSurface-Bench is introduced, a benchmark for evaluating the capability of heterogeneous knowledge sources as surface routing in enterprise agents, and shows that correct surface selection is necessary but insufficient for task completion.
OmniaBench provides a broad and diagnostic benchmark for characterizing the capability boundaries of general agents across diverse scenarios with explicit state spaces, and introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors to support fine-grained evaluation and analysis.
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