AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Heteroscedastic Student-t action regression (HT-Policies) is introduced, which learns input-dependent residual scales and reduces the influence of heavy tails and offers an efficient direct-regression alternative for a range of architectures and tasks.
The model achieves the best performance among similarly sized models on 19 of the 38 benchmarks and substantially outperforms strong competitors, including Qwen3.6-A3B and Cosmos 3.5 MoT-2B, and demonstrates strong performance on embodied agentic tasks requiring multi-turn interaction and long-horizon reasoning.
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