AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is found that generated harnesses remain substantially behind mature human-engineered references on code and on search and research, while matching or exceeding the selected references on writing and machine-learning experimentation, with large variation in execution cost.
This work introduces ASPIRE, a benchmark for vague-goal-driven self-evolution and shows that vague goals redirect search effort toward goal interpretation, and evaluates the resulting systems on a hidden, expert-authored set of 520 items spanning six goals.
These findings show that recognizing successful actions is insufficient; agents must also transform feedback into executable and transferable policies, and provide a unified framework for diagnosing this process and identifying the bottlenecks that prevent agents from translating interaction experience into reliable self-improvement.
Together, the perplexity analysis indicates improved continuation predictability, while the controlled pre-training experiments suggest that this augmentation can improve model performance without changing the standard pre-training objective.
LP-SFT, a Local-Preserving Supervised Fine-Tuning objective designed to explicitly protect this inherent entropy structure, improves overall performance over vanilla SFT and recent SFT-enhancement baselines, suggesting that local preservation helps mitigate capability degradation without collapsing sampling-accessible diversity.
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