mlsanityData to 14 Oct 2026

HBLLM: Wavelet-Enhanced High-Fidelity 1-Bit Quantization for LLMs

arXiv 2512.00862 ↗First posted 30 Nov 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

We introduce HBLLM, a wavelet-enhanced high-fidelity 1-bit post-training quantization method for Large Language Models (LLMs). By leveraging Haar wavelet transforms to enhance expressive capacity through frequency decomposition, HBLLM significantly improves quantization fidelity while maintaining minimal overhead. This approach features two innovative structure-aware grouping strategies: (1) frequency-aware multi-parameter intra-row grouping and (2) ℓ2-norm-based saliency-driven column selection. For non-salient weights, a shared mean is employed across quantization groups within each frequency band to optimize storage efficiency. Experiments conducted on the OPT and LLaMA models demonstrate that HBLLM achieves state-of-the-art performance in 1-bit quantization, attaining a perplexity of 6.71 on LLaMA2-13B with an average weight storage of only 1.08 bits. Code available at: https://github.com/Yeyke/HBLLM.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 30 Nov 2025.

Builds on

30 of 38 references.

References · 30

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  4. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  6. 2016Pointer Sentinel Mixture Modelscited by 3,096
  7. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  8. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,183
  9. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  10. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  11. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  12. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,298
18 more
  1. 2023SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shotcited by 1,121
  2. 2016The LAMBADA dataset: Word prediction requiring a broad discourse contextcited by 992
  3. 2023A Simple and Effective Pruning Approach for Large Language Modelscited by 800
  4. 2022ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformerscited by 526
  5. 2023QuIP: 2-Bit Quantization of Large Language Models With Guaranteescited by 414
  6. 2024SliceGPT: Compress Large Language Models by Deleting Rows and Columnscited by 385
  7. 2022Distilling Reasoning Capabilities into Smaller Language Modelscited by 158
  8. 2024BiLLM: Pushing the Limit of Post-Training Quantization for LLMscited by 144
  9. 2022Less is More: Task-aware Layer-wise Distillation for Language Model Compressioncited by 93
  10. 2023PB-LLM: Partially Binarized Large Language Modelscited by 83
  11. 2024OneBit: Towards Extremely Low-bit Large Language Modelscited by 79
  12. 2024An empirical study of LLaMA3 quantization: from LLMs to MLLMscited by 64
  13. 2024DB-LLM: Accurate Dual-Binarization for Efficient LLMscited by 37
  14. 2024A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithmscited by 31
  15. 2024ARB-LLM: Alternating Refined Binarizations for Large Language Modelscited by 31
  16. 2024FrameQuant: Flexible Low-Bit Quantization for Transformerscited by 18
  17. 2024Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Modelscited by 14
  18. 2024OAC: Output-adaptive Calibration for Accurate Post-training Quantizationcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Modelscited by 2
  2. 2026NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Modelscited by 1
  3. 2026SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Modelscited by 1
  4. 2026FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs
  2. 2025Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
  3. 2025LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models
  4. 2025Achieving binary weight and activation for LLMs using Post-Training Quantization
  5. 2024SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
  6. 2025Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
  7. 2025LittleBit: Ultra Low-Bit Quantization via Latent Factorization
  8. 2025PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models