AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces a Graph Neural Network (GNN)-based difficulty estimator that jointly captures topological and semantic aspects of violations, enabling the system to distinguish between cases suitable for automatic repair and those requiring human input.
GyroNovo, a framework with two main contributions that align missing-fragment recovery with decoder behavior while explicitly incorporating the mass relationships that underlie peptide fragmentation, is introduced.
This survey systematizes recent progress in tree-search-based reasoning, viewing inference as instance-specific optimization rather than decoding, and introduces a Unified Design Space spanning search topology, evaluation signals, and control dynamics to unify a fragmented literature.
Empirical results on RouterBench and SWE-Bench demonstrate that WR-Offline surpasses existing baselines in performance under a fixed budget and adheres more closely to budget constraints, and WR-Online achieves comparable performance to the baselines, while using substantially less exploration data.
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