AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A system level acceleration strategy that reduces computation in both perception and action generation and compress diffusion sampling into a compact 2-step schedule through efficiency oriented training while preserving action precision is proposed.
The Triplet-to-Track System (TTS), a closed-loop long-horizon imitation learning system that uses human videos to reduce reliance on robot-collected data, achieves a 74.8\% average success rate and supports object-level and compositional generalization.
Results show that a fixed-weight, self-evolving harness can revise, recover, and accumulate verified approaches while producing structured trajectories for future supervised and reinforcement learning.
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