AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Privileged Hindsight Distillation (PHD) is introduced, which uses later-emerging scientific evidence during training to improve proposal-time criterion assessment and the population mapping, while exposing only proposal-visible information at inference.
This work makes IoA detectable by generating diagnostic multiple-choice questions whose divergent answers across participants provide direct behavioral evidence of hidden disagreement, and pairs IoA-Prober-8B with LLM agents improves downstream task performance on BigCodeBench-Hard and HiddenBench.
State2State is proposed, an environment-derived mid-training method that converts explored environment states into training objectives, challenging agents to reach a specified target state by deriving tasks from environment exploration and verifying success through rule-based state matching.
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