AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. However, existing methods primarily optimize how the harness is updated while largely fixing which training scenarios generate the feedback that drives t...
The Prefix-Aware Internal Reward (PAIR), a two-stage model with a frozen hidden-state probe estimating belief-consistency and a lightweight attention-based head correcting it toward grounded correctness, is proposed, enabling dense step-level reward signals for GRPO training without external model calls, ground-truth d...
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
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