AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces SPatial coHErent risk control for REplay (SPHERE), a general replay-allocation method applicable across a broad range of learning settings and demonstrates that SPHERE improves accuracy and reduces forgetting across noisy-label vision tasks, continual language-model instruction tuning, and code-gen...
Pretrained macro-placement policies can reduce repeated optimization across circuits, but deployment often exposes them to unfamiliar designs when the original training data are unavailable. Repeatedly fine-tuning a single serving model can overwrite earlier improvements, while simply saving checkpoints does not determ...
This work introduces Selection-Aware Semantic Stress Testing (\SASST{}), which learns a task reweighting from pre-execution features on discovery tasks and evaluates the same paired comparison on separate confirmation tasks.
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