7 citations · 9 across the 15 of their papers we have counts for
17 papers
SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models
Yunhan Wang, Haodong Wang, Zhiming Liu +7
World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, pr…
Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination
Xinling Xie, Haodong Wang, Jiazhi Mi +8
World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and actions. This capability comes…
OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
Yikun Miao, Fangqi Zhu, Quanxin Shou +6
Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling. While recent efforts levera…
xDailyBench: Benchmarking LLMs on Professional Consultation for Real-Life Problems
Yongchang Peng, Qingshui Gu, Liya Zhu +31
Large language models (LLMs) are increasingly used for everyday assistance, yet existing benchmarks only partially reflect the requests users naturally make in practice. Real-world…
StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows
Liya Zhu, Xin Ma, Tao Liu +35
Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on re…
Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents
Zining Huang, Haoran Que, Hong Zeng +8
When a coding agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following benchmarks cannot tell the difference: they concentrate rules in t…
MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization
Yangjia Hu, Haodong Wang, Zicong Hong +8
4-bit quantization significantly reduces the memory footprint and accelerates the inference of large language models (LLMs). However, its limited bit-width representation struggles…
Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
Qianli Liu, Kaibin Guo, Zicong Hong +5
Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency depends on the communication and computation latencies of the GPUs, w…
TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization
Haodong Wang, Junjie Liu, Zicong Hong +4
4-bit quantization reduces the memory footprint and latency of large language model inference, but its aggressive precision reduction can severely degrade accuracy. Prior methods a…
KVDrive: A Holistic Multi-Tier KV Cache Management System for Long-Context LLM Inference
Jian Lin, Jiazhi Mi, Zicong Hong +5
Supporting long-context LLMs is challenging due to the substantial memory demands of the key-value (KV) cache. Existing offloading systems store the full cache in host memory and s…
PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
Zile Wang, Qianli Liu, Kaibin Guo +4
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opp…
MVGGT: Multimodal Visual Geometry Grounded Transformer for Multiview 3D Referring Expression Segmentation
Changli Wu, Haodong Wang, Jiayi Ji +5
Most existing 3D referring expression segmentation (3DRES) methods rely on dense, high-quality point clouds, while real-world agents such as robots and mobile phones operate with o…
DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities
Jing Yang Lee, Hamed Bonab, Nasser Zalmout +6
Large Language Models (LLMs) are increasingly employed in multi-turn conversational tasks, yet their pre-training data predominantly consists of continuous prose, creating a potent…
Aligning Large Language Models with Implicit Preferences from User-Generated Content
Zhaoxuan Tan, Zheng Li, Tianyi Liu +10
Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses. However, existing prefe…
DMoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
Haodong Wang, Qihua Zhou, Zicong Hong +1
The mixture of experts (MoE) model is a sparse variant of large language models (LLMs), designed to hold a better balance between intelligent capability and computational overhead.…
IHEval: Evaluating Language Models on Following the Instruction Hierarchy
Zhihan Zhang, Shiyang Li, Zixuan Zhang +11
The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent an…
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Yilun Jin, Zheng Li, Chenwei Zhang +19
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are com…