2 citations · 6 across the 21 of their papers we have counts for
21 papers
Atomic Task Graph: A Unified Framework for Agentic Planning and Execution
Yue Zhang, Sihan Chen, Ziwen Huang +3
LLM-based agents have shown strong potential for solving complex multi-step tasks, yet existing performance improvements often rely on either scaling to larger backbone models or t…
Test-time Sparsity for Extreme Fast Action Diffusion
Kangye Ji, Yuan Meng, Jianbo Zhou +3
Action diffusion excels at high-fidelity action generation but incurs heavy computational costs owing to its iterative denoising nature. Despite current technologies showing promis…
VP-VAE: Rethinking Vector Quantization via Adaptive Vector Perturbation
Linwei Zhai, Han Ding, Mingzhi Lin +5
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental to modern generative modeling, yet they often suffer from training instability and "codebook collapse" due to th…
WiSparse: Boosting LLM Inference Efficiency with Weight-Aware Mixed Activation Sparsity
Lei Chen, Yuan Meng, Xiaoyu Zhan +2
Large Language Models (LLMs) offer strong capabilities but incur high inference costs due to dense computation and memory access. Training-free activation sparsity is a promising a…
Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Kangye Ji, Jianbo Zhou, Yuan Meng +3
Diffusion Policy has dominated action generation due to its strong capabilities for modeling multi-modal action distributions, but its multi-step denoising processes make it imprac…
TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration
Ye Li, Jiahe Feng, Yuan Meng +10
Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. Existing diffusion model…
FREE: Uncertainty-Aware Autoregression for Parallel Diffusion Transformers
Xinwan Wen, Bowen Li, Jiajun Luo +2
Diffusion Transformers (DiTs) achieve state-of-the-art generation quality but require long sequential denoising trajectories, leading to high inference latency. Recent speculative…
Quantization Meets OOD: Generalizable Quantization-aware Training from a Flatness Perspective
Jiacheng Jiang, Yuan Meng, Chen Tang +4
Current quantization-aware training (QAT) methods primarily focus on enhancing the performance of quantized models on in-distribution (I.D) data, while overlooking the potential pe…
Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning
Yijun Liu, Yuwei Liu, Yuan Meng +8
Vision-centric hierarchical embodied models have demonstrated strong potential. However, existing methods lack spatial awareness capabilities, limiting their effectiveness in bridg…
Block-wise Adaptive Caching for Accelerating Diffusion Policy
Kangye Ji, Yuan Meng, Hanyun Cui +5
Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control. Despite huge redund…
SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration
Ye Li, Yuan Meng, Zewen Sun +7
Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities. However, their high computational cost and low execution frequency hi…
JAQ: Joint Efficient Architecture Design and Low-Bit Quantization with Hardware-Software Co-Exploration
Mingzi Wang, Yuan Meng, Chen Tang +9
The co-design of neural network architectures, quantization precisions, and hardware accelerators offers a promising approach to achieving an optimal balance between performance an…
GAQAT: gradient-adaptive quantization-aware training for domain generalization
Jiacheng Jiang, Yuan Meng, Chen Tang +4
Research on loss surface geometry, such as Sharpness-Aware Minimization (SAM), shows that flatter minima improve generalization. Recent studies further reveal that flatter minima c…
EMS: Adaptive Evict-then-Merge Strategy for Head-wise KV Cache Compression Based on Global-Local Importance
Yingxin Li, Ye Li, Yuan Meng +4
As large language models (LLMs) continue to advance, the demand for higher quality and faster processing of long contexts across various applications is growing. KV cache is widely…
SizeGS: Size-aware Compression of 3D Gaussian Splatting via Mixed Integer Programming
Shuzhao Xie, Jiahang Liu, Weixiang Zhang +7
Recent advances in 3D Gaussian Splatting (3DGS) have greatly improved 3D reconstruction. However, its substantial data size poses a significant challenge for transmission and stora…
RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization
Nanyang Du, Chen Tang, Yuxiao Jiang +2
Performing unsupervised domain adaptation on resource-constrained edge devices is challenging. Existing research typically adopts architecture optimization (e.g., designing slimmab…
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference
Ye Li, Chen Tang, Yuan Meng +5
We introduce PRANCE, a Vision Transformer compression framework that jointly optimizes the activated channels and reduces tokens, based on the characteristics of inputs. Specifical…
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Yijun Liu, Yuan Meng, Fang Wu +7
Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world application…
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Lei Chen, Yuan Meng, Chen Tang +5
Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality an…
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models
Haojun Sun, Chen Tang, Zhi Wang +4
Diffusion models have emerged as preeminent contenders in the realm of generative models. Distinguished by their distinctive sequential generative processes, characterized by hundr…
Investigating the Impact of Quantization on Adversarial Robustness
Qun Li, Yuan Meng, Chen Tang +2
Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for…