Large-model deployment is bound by memory, weight bandwidth, and checkpoint volume, and sub-2-bit quantization has long been treated as the last mile. arXiv 2607.08643 (Shao et al., 2026-07-09) proposes BiSCo-LLM, a new path that doesn't rely on a VQ codebook: map local weight blocks to a unit hypersphere, binarize into a pure bitstream symbol as the main payload, then use a Residual BSQ stage to encode the reconstruction error left by the baseline spherical code, as an explicit rate-distortion channel without a codebook. On top of this, a category-wise replacement of Transformer modules is followed by a category-wise recovery distillation, pulling back the local reconstruction error and overall behavior misalignment. The whole system also adds an 8-bit protection channel to stabilize sensitive channels, paired with a neural decoder and LoRA adapter counted into storage. The significance is that LLM weights can be compressed to sub-2-bit while preserving the capacity advantage of VQ-type methods and shedding the explicit codebook storage and lookup overhead, which is very practical for low-bandwidth checkpoint transfer and memory-constrained on-device inference scenarios.