19 papers
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction
Leyang Chen, Junyi Wu, Zhiteng Li +1
Streaming 3D reconstruction from long monocular video sequences requires maintaining a key-value (KV) cache that grows linearly with sequence length, creating a severe memory bottl…
SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization
Chengzhu Bao, Xianglong Yan, Zhiteng Li +3
NVFP4 has recently emerged as an efficient 4-bit microscaling format for large language models (LLMs), offering superior numerical fidelity with native hardware support. However, e…
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
Yixin Tang, Jiawei Guo, Junxian Li +6
Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, they indiscriminately denoise…
DQuant: Accurate Low-bit Post-Training Weight Quantization for LLMs
Xianglong Yan, ChengZhu Bao, Zhiteng Li +5
Large language models (LLMs) deliver strong performance, but their high compute and memory costs make deployment difficult in resource-constrained scenarios. Weight-only post-train…
VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
Guangshuo Qin, Zhiteng Li, Zheng Chen +3
Mixture-of-Experts(MoE) Vision-Language Models (VLMs) offer remarkable performance but incur prohibitive memory and computational costs, making compression essential. Post-Training…
PT-LLM: Post-Training Ternarization for Large Language Models
Xianglong Yan, Chengzhu Bao, Zhiteng Li +6
Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attenti…
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
Tianao Zhang, Zhiteng Li, Xianglong Yan +3
Diffusion large language models (dLLMs), which offer bidirectional context and flexible masked-denoising generation, are emerging as a compelling alternative to autoregressive (AR)…
FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing
Junyi Wu, Zhiteng Li, Haotong Qin +2
Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized i…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Xianglong Yan, Zhiteng Li, Tianao Zhang +4
Large language models (LLMs) have demonstrated remarkable performance, but their long-context reasoning remains constrained by the excessive memory required for the Key-Value (KV)…
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Zhiteng Li, Hanxuan Li, Junyi Wu +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…
QuantCache: Adaptive Importance-Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation
Junyi Wu, Zhiteng Li, Zheng Hui +3
Recently, Diffusion Transformers (DiTs) have emerged as a dominant architecture in video generation, surpassing U-Net-based models in terms of performance. However, the enhanced ca…
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li +5
Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration ability. However, accuracy degrad…
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen +5
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and compu…
Progressive Binarization with Semi-Structured Pruning for LLMs
Xianglong Yan, Tianao Zhang, Zhiteng Li +2
Large language models (LLMs) have achieved remarkable progress in natural language processing, but their high computational and memory costs hinder deployment on resource-constrain…
AdaSVD: Adaptive Singular Value Decomposition for Large Language Models
Zhiteng Li, Mingyuan Xia, Jingyuan Zhang +5
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP) tasks, yet their substantial memory requirements present significant challenges f…
ARB-LLM: Alternating Refined Binarizations for Large Language Models
Zhiteng Li, Xianglong Yan, Tianao Zhang +7
Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. B…
BinaryHPE: 3D Human Pose and Shape Estimation via Binarization
Zhiteng Li, Yulun Zhang, Jing Lin +5
3D human pose and shape estimation (HPE) aims to reconstruct the 3D human body, face, and hands from a single image. Although powerful deep learning models have achieved accurate e…