AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes minimal-edit corrective on-policy distillation, a method that preserves on-policy supervision while avoiding full-response imitation, and improves perception-oriented performance, particularly low-level geometric perception, and transfers to external mathematics and perception benchmarks.
TAR and TAR-Bench serve as the official training and in-domain evaluation resources for AI City Challenge 2026 Track 3 and highlight persistent limitations in temporal precision and causal attribution.
Despite its compact active-parameter budget, Turing-20B-A2B achieves, at the base-model stage, overall general capability exceeding Qwen3-8B Base and approaching Qwen3.5-9B Base, while maintaining strong long-context performance and favorable prefill-latency scaling.
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