11 citations · 20 across the 16 of their papers we have counts for
19 papers
SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation
Yufei Jiao, Jinfa Huang, Zeyue Xue +5
Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existing methods typically apply a…
RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment
Qingchuan Ma, Yuexiao Ma, Xiang Meng +7
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, a…
Duplex-MPE: Benchmarking Multi-Party Interaction in Full-Duplex Dialogue
Chengqian Ma, Wenhao Feng, Weixuan Jin +7
Real-time full-duplex speech models can listen while speaking, enabling natural interaction without rigid turn boundaries. Existing benchmarks evaluate turn-taking, interruption ha…
YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family
Xu Lin, WenJie Nie, Jinlong Peng +4
Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specif…
OmniScope: Modality-Decoupled Token Compression for Omnimodal Large Language Models
Jinsen Su, Yongdong Luo, Yuexiao Ma +3
Existing token compression methods for omnimodal large language models typically rely on one modality to determine what to retain in the other. We show that this assumption often b…
AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization
Wanqi Yang, Yuexiao Ma, Alexander Conzelmann +4
Mixture-of-Experts (MoE) architectures scale model capacity through sparse expert activation, but their deployment remains memory-bound because all expert weights must reside in me…
FinBoardBench: Benchmarking Dynamic Wealth Management and Strategic Financial Reasoning of LLMs via Board Game Simulations
Xuesi Hu, Peng Wang, Jinpeng Miao +7
Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However, existing static financial benchma…
A2RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation
Qingchuan Ma, Yuexiao Ma, Yongkang Xie +3
Abstract reasoning ability reflects the intelligence and generalization capacity of LLMs to extract and apply abstract rules. However, accurately measuring this ability remains cha…
Motion-Aware Caching for Efficient Autoregressive Video Generation
Jing Xu, Yuexiao Ma, Xuzhe Zheng +7
Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential i…
ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization
Kunpeng Liao, Yuexiao Ma, Yisheng Lin +3
Algorithm Visualization (AV) helps students build mental models by animating algorithm execution states. Recent LLM-based systems such as CODE2VIDEO generate AV videos in an end-to…
HEART: Exploiting Head Heterogeneity in Sparse Attention for Video Diffusion
Xuzhe Zheng, Yuexiao Ma, Jing Xu +3
Sparse attention accelerates video diffusion by allowing each attention head to focus on only a small subset of interactions. Existing methods already construct head-specific spars…
SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models
Tianyu Xie, Jinfa Huang, Yuexiao Ma +11
Evaluating omni-modal large language models (OLMs) in multi-party dialogue requires more than answer correctness on pre-segmented inputs. We introduce SocialOmni, an offline diagno…
Flow caching for autoregressive video generation
Yuexiao Ma, Xuzhe Zheng, Jing Xu +9
Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. Howeve…
Polybasic Speculative Decoding Through a Theoretical Perspective
Ruilin Wang, Huixia Li, Yuexiao Ma +4
Inference latency stands as a critical bottleneck in the large-scale deployment of Large Language Models (LLMs). Speculative decoding methods have recently shown promise in acceler…
Training-Free Multimodal Large Language Model Orchestration
Tianyu Xie, Yuexiao Ma, Yuhang Wu +5
Building interactive omni-modal assistants often relies on end-to-end multimodal alignment to fuse heterogeneous modalities, which incurs substantial data and compute costs and lim…
AffineQuant: Affine Transformation Quantization for Large Language Models
Yuexiao Ma, Huixia Li, Xiawu Zheng +6
The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing…
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
Yuexiao Ma, Huixia Li, Xiawu Zheng +6
Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We ar…
OMPQ: Orthogonal Mixed Precision Quantization
Yuexiao Ma, Taisong Jin, Xiawu Zheng +6
To bridge the ever increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest…
An Information Theory-inspired Strategy for Automatic Network Pruning
Xiawu Zheng, Yuexiao Ma, Teng Xi +6
Despite superior performance on many computer vision tasks, deep convolution neural networks are well known to be compressed on devices that have resource constraints. Most existin…