AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Two test-time monitoring strategies are proposed: reasoning expression monitoring and hidden states monitoring, that reduce token usage by 62.7-93.6%, substantially improving efficiency and reliability while largely preserving accuracy.
Quantile-Guided Density Estimation (QGDE), which approximates this distribution with multiple quantile trends and uses local density weighting to produce token-level estimates and suggests that released tokenizer vocabularies provide a useful signal for fine-grained corpus estimation beyond coarse composition inference.
This work proposes Sampled-BPE, a lightweight token-level auditing pipeline that sample a small subset and train BPE tokenizer to surface polluted tokens, and releases a hierarchical Chinese web token dataset with 660k+ token records, organized as trees to support review and tracing of pollution.
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