AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces MARCO, an evaluator-grounded reinforcement-learning framework that trains molecular editors on bounded proposal--feedback--revision trajectories and aggregates shaped turn rewards into an undiscounted trajectory return for group-relative policy optimization.
Objective-wise Reconciled Policy Gradient (ORPG), which constructs a separate clipped policy objective for each reward and reconciles the resulting gradients into one policy update, achieves the highest average full-budget accuracy and three-budget hypervolume among the compared methods.
A paired ablation that removes explicit scientific guidance while preserving the repository and executable engineering context shows that scientific knowledge is not uniformly beneficial: well-grounded information can constrain repair and improve average performance and token efficiency, whereas poorly aligned guidance...
It is found that memory can improve UAQ performance in some settings, but such gains are selective rather than universal and remain fragile under dataset shift, suggesting that reliable UAQ memory depends less on storing larger amounts of experience and more on preserving transferable behavioral guidance.
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