AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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By coupling QAD's stable low-bit initialization with OPD's on-policy reasoning recovery, the framework provides a comprehensive sub-3-bit solution that preserves broad capabilities while restoring long-form reasoning.
It is found that expert selection is far more redundant than the field's operating points assume, and how much expert computation fine-grained MoEs can dispense with is revealed, and when dynamic allocation earns its complexity is established, informing both practical deployment and future pruning methods.
Experiments show that PRQuant substantially reduces down-projection reconstruction error, with scaling and residual compensation providing the main numerical gains while permutation enables a hardware-friendly contiguous layout.
BiSCo-LLM is presented, a codebook-free binary spherical coding framework for extreme low-bit LLM weight compression and its reported storage budget includes binary codes, neural decoders, protected-channel payloads, LoRA adapters, and metadata.
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