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SoloQ提出一种免校准的量化框架，将权重和激活映射到归一化旋转基底，实现数据无关的4比特量化，在扩散语言模型上保持精度并最多降低2.61倍峰值内存、加速2.24倍推理。
The paper introduces Magnitude Profile (MP) scoring, a calibration-free method for pruning attention heads in transformers, achieving better perplexity on models like OPT-6.7B and RoBERTa-large compared to existing methods, with zero forward passes or calibration data.
D-Quant is a flexible KV cache quantization framework using driftable entropy coding to convert variable-length representations into fixed-size bitstreams, enabling efficient parallel dequantization in attention kernels and achieving up to 7x memory reduction and 3.5x throughput improvement while preserving near BF16 performance.
LILA is a calibration-free structured pruning method for large language models that uses latent spectral geometry to score neuron importance, achieving competitive performance without calibration data.
ReRound is a post-training quantization method that uses a conditional diffusion model to guide rounding of near-midpoint weights, improving small LLM accuracy at 3-bit and 4-bit quantization without inference overhead.