Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but lack inference acceleration. This paper explores the middle ground of statistically-lossless compression, examining three complementary aspects of what losslessness means for quantized LLMs. First, task-lossless compression preserves zero-shot benchmark accuracy within natural sampling variance and is achievable at aggressive bitwidths. Second, we formalize the stricter notion of distribution-lossless compression, requiring the quantized model's next-token distribution to be practically indistinguishable from the original, and propose the Expected Acceptance Rate (EAR), the maximum token-agreement probability under optimal coupling, as a directly interpretable fidelity metric. For example, EAR >= 0.99 means 99% agreement. Third, we prove a gamma-squared variance law showing that symmetric quantization inflates noise variance by gamma^2 relative to asymmetric quantization, making asymmetric quantization a prerequisite for distribution-lossless fidelity but not for task-level preservation. Through SLQ, a layer-wise non-uniform method with asymmetric quantization and wide bitwidth search, we obtain task-lossless compression at well below 4 bits per parameter, as low as 3.3 bits depending on the model, distribution-lossless compression at 5-6 bits per parameter on average, and inference speedups of 1.7-3.7x compared to FP16 using optimized kernels. Source code is available at [https://github.com/IST-DASLab/SLQ](https://github.com/IST-DASLab/SLQ).
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
University of Washington（华盛顿大学）；HSE University（高等经济大学）；Yandex（扬德克斯）；Skoltech（斯科尔科沃科学技术研究院）；IST Austria（奥地利科学技术研究所）；ETH Zurich（苏黎世联邦理工学院）；NeuralMagic（神经魔法公司）
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Zhejiang Lab（之江实验室）；School of Integrated Circuits, Peking University（北京大学集成电路学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机学院）；State Key Laboratory of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
Southwestern University of Finance and Economics（西南财经大学）；Artificial Intelligence and Digital Finance Key Laboratory of Sichuan Province（四川省人工智能与数字金融重点实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与艺术大学（广州））；Sun Yat-sen University（中山大学）；Nanjing University（南京大学）
OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond
Tsinghua University（清华大学）；Meituan LongCat Team（美团LongCat团队）；The University of Hong Kong（香港大学）；The University of Edinburgh（爱丁堡大学）；UCAS（中国科学技术大学）；The Hong Kong Polytechnic University（香港理工大学）
ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
School of Electronic Science and Engineering, Nanjing University, China.（南京大学电子科学与工程学院）；National Key Laboratory for Novel Software Technology, Nanjing University, China.（南京大学新型软件技术国家重点实验室）
ENEC: A Lossless AI Model Compression Method Enabling Fast Inference on Ascend NPUs
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Sensetime Research（商汤科技研究院）；Tsinghua University（清华大学）；Syracuse University（雪城大学）；Northeastern University（东北大学）；Florida International University（佛罗里达国际大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
权值修剪和权值量化是DNN模型压缩的两个重要方面。这些技术的先前工作主要基于化学。最近的一项工作使用先进的优化技术ADMM（Alternating Direction Methods of Multipliers）开发了DNN权重修剪的系统框架，实现了最先进的权重修剪结果之一。在这项工作中，我们首先扩展这样的一杆ADMM为基础的框架，以保证解决方案的可行性，并提供快速收敛速度，以及推广到权重量化。我们进一步开发了一个多步骤的渐进式DNN权重修剪和量化框架，具有双重好处：（i）由于ADMM正则化的特殊属性，实现了进一步的权重修剪/量化，以及（ii）减少了每个步骤中的搜索空间。大量的实验结果表明，与以前的工作相比，优越的性能。一些亮点：（i）我们分别在LeNet-5、AlexNet和ResNet-50模型上实现了246倍、36倍和8倍的权重修剪，（几乎）零精度损失;（ii）在AlexNet中甚至实现了显著的61倍权重修剪（ImageNet）与先前的工作相比，实际准确性仅略有下降;（iii）我们是第一批为ResNet和MobileNet模型得出显着权重修剪结果的人之一;（iv）我们为MNIST和CIFAR-10分别导出了第一个无损的、完全二进制化的（所有层）LeNet-5和VGG-16;以及（v）我们为ImageNet导出了第一个完全二进制化的（所有层）ResNet，具有合理的准确性损失…
A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits
