2 citations · 3 across the 21 of their papers we have counts for
24 papers
LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression
Zishan Shao, Lixun Zhang, Kangning Cui +10
SVD-based low-rank compression has become a fast-growing direction for reducing the memory and computational cost of large language models (LLMs). However, meaningful comparison ac…
From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
Qinsi Wang, Jing Shi, Huazheng Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, i…
DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions
Zishan Shao, Lixun Zhang, Kangning Cui +10
Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general structure, if any, is used at decode time r…
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
Yudong Liu, Yuan Li, Zijia Tang +12
Dual-system Vision-Language-Action (VLA) models achieve state-of-the-art robotic manipulation but are bottlenecked by the VLM backbone, which must execute at every control step whi…
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Yuzhe Fu, Hancheng Ye, Cong Guo +7
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces…
MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems
Yifei Wang, Hancheng Ye, Yechen Xu +8
Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic workloads induce a temporal shi…
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Qinsi Wang, Hancheng Ye, Jinhee Kim +12
Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise,…
LLaViDA: A Large Language Vision Driving Assistant for Explicit Reasoning and Enhanced Trajectory Planning
Yudong Liu, Spencer Hallyburton, Jiwoo Kim +8
Trajectory planning is a fundamental yet challenging component of autonomous driving. End-to-end planners frequently falter under adverse weather, unpredictable human behavior, or…
Focus: A Streaming Concentration Architecture for Efficient Vision-Language Models
Chiyue Wei, Cong Guo, Junyao Zhang +8
Vision-Language Models (VLMs) have demonstrated strong performance on tasks such as video captioning and visual question answering. However, their growing scale and video-level inp…
KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems
Hancheng Ye, Zhengqi Gao, Mingyuan Ma +8
Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, the…
AsyncVoice Agent: Real-Time Explanation for LLM Planning and Reasoning
Yueqian Lin, Zhengmian Hu, Jayakumar Subramanian +4
Effective human-AI collaboration on complex reasoning tasks requires that users understand and interact with the model's process, not just receive an output. However, the monolithi…
Voice Evaluation of Reasoning Ability: Diagnosing the Modality-Induced Performance Gap
Yueqian Lin, Zhengmian Hu, Qinsi Wang +6
We present Voice Evaluation of Reasoning Ability (VERA), a benchmark for evaluating reasoning ability in voice-interactive systems under real-time conversational constraints. VERA…
Vision-Zero: Scalable VLM Self-Improvement via Strategic Gamified Self-Play
Qinsi Wang, Bo Liu, Tianyi Zhou +6
Although reinforcement learning (RL) has emerged as a promising approach for improving vision-language models (VLMs) and multimodal large language models (MLLMs), current methods r…
FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models
Zishan Shao, Yixiao Wang, Qinsi Wang +6
Singular Value Decomposition (SVD) has recently seen a surge of interest as a simple yet powerful tool for large language models (LLMs) compression, with a growing number of works…
Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signals
Qinsi Wang, Jinghan Ke, Hancheng Ye +6
Current Reinforcement Fine-tuning (RFT) paradigms for Large Language Models (LLMs) suffer from sample inefficiency due to the redundant exposure of identical queries under uniform…
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
Ming-Yu Chung, Jiashuo Fan, Hancheng Ye +5
Model Reprogramming (MR) is a resource-efficient framework that adapts large pre-trained models to new tasks with minimal additional parameters and data, offering a promising solut…
CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
Qinsi Wang, Hancheng Ye, Ming-Yu Chung +6
Vision-Language Models (VLMs) excel across diverse tasks but suffer from high inference costs in time and memory. Token sparsity mitigates inefficiencies in token usage, while neur…
Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
Yudong Liu, Jingwei Sun, Yueqian Lin +6
Vision language models (VLMs) demonstrate strong capabilities in jointly processing visual and textual data. However, they often incur substantial computational overhead due to red…
HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
Yueqian Lin, Jingyang Zhang, Qinsi Wang +5
Comprehending extended audiovisual experiences remains challenging for computational systems, particularly temporal integration and cross-modal associations fundamental to human ep…
H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
Martin Kuo, Jianyi Zhang, Aolin Ding +6
Large Reasoning Models (LRMs) have recently extended their powerful reasoning capabilities to safety checks-using chain-of-thought reasoning to decide whether a request should be a…
Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
Qinsi Wang, Jinghan Ke, Masayoshi Tomizuka +3
We provide a new LLM-compression solution via SVD, unlocking new possibilities for LLM compression beyond quantization and pruning. We point out that the optimal use of SVD lies in…
CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activation
Qinsi Wang, Saeed Vahidian, Hancheng Ye +3
Large language models (LLMs) with billions of parameters have sparked a new wave of exciting AI applications. However, their high computational costs and memory demands during infe…
MathNAS: If Blocks Have a Role in Mathematical Architecture Design
Wang Qinsi, Ke Jinghan, Liang Zhi +1
Neural Architecture Search (NAS) has emerged as a favoured method for unearthing effective neural architectures. Recent development of large models has intensified the demand for f…
Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation
Yuqi Xiang, Feitong Chen, Qinsi Wang +5
The capability to transfer mastered skills to accomplish a range of similar yet novel tasks is crucial for intelligent robots. In this work, we introduce ,…