AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TEX, a collaborative test-time scaling framework based on execution-driven cross-validation that parallelizes agents to generate tests and patches, cross-executing each test against every patch to form a feedback matrix, enabling objective validation without verbose trace sharing.
This work proposes SwitchPFN, which learns a shared projection and regime codebook from the training sequences, making local dynamic operators and transition features directly comparable across samples, and achieves the highest mean accuracy among the evaluated methods.
It is found that process-level verification does not consistently improve performance and frequently exhibits high variance, highlighting the difficulty of reliably evaluating partial multi-agent trajectories.
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