AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing. As a task progresses, earlier exploration becomes stale, so managing context is more than avoiding overflow: an agent must decide when to compact, what working state to preserve, a...
It is shown that under GRPO-style optimization, a global normalization baseline may deviate from diverse agents'reward distributions, which ultimately leads to gradient-norm instability, which theoretically pinpoints a key reason for training instability when extending group-based RL to multi-agent LLM systems.
JET is introduced, which evolves an executable judge on labeled source trajectories, then freezes and transfers it to guide target-side program evolution, andalyses identify useful reward-prediction logic in the evolved code and show that better final selection alone cannot explain the gains.
AgentBrew is proposed, an offline training framework that learns effective tool-use policies from a single batch of raw interaction trajectories, without task verifiers or iterative on-policy rollouts, and demonstrates that fine-grained offline learning can recover useful supervision from raw trajectories that filterin...
AppDeltaWorld is proposed, a transition-grounded delta code world model that predicts the next GUI as a reachable code update rather than as an unconstrained image or text description, and achieves the highest fidelity on CMGUIBench-500 under Code2World evaluation.
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