AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes PACE, a model-native VLM policy that jointly predicts what to execute and for how long under a hard decision budget, and evaluates PACE on four long-horizon environments spanning fully and partially observable settings and visual and textual modalities.
Fair Multi-Level Preference Optimization (Fair-MPO or $\Phi$-MPO), a new preference optimization framework for agentic learning, is proposed and a Fair Multi-Level Objective that addresses imbalance in agentic learning is introduced.
Despite-encoder vision-language models expose a similarity interface that enables zero-shot retrieval but fails compositional constraints, this work proposes factored inference, which separates evidence extraction from constraint execution, and introduces LCSE (Logic-Constrained Score Editing), a training-free method t...
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