AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness. We investigate how this operating space shapes agent behavior and policy learning in computer-aided design (CAD), a professional domain characterized by persistent state, geometric constraints, a...
BOW is introduced, an RL framework that instead trains models to produce self-contained, neutral, and comprehensive descriptions of the plausible next-word space, and human evaluation shows that BOW-Reg produces broader next-word reasoning trajectories, while direct next-word-prediction evaluation shows that these traj...
This work proves a PAC-Bayes bound guaranteeing that a dictionary extracted from successful trajectories has bounded expected description length on future successful behavior, and introduces ReuseRL, which grounds agentic RL in the Minimum Description Length (MDL) principle.
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