12 citations · 49 across the 33 of their papers we have counts for
34 papers
An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation
Wenhao Li, Fangyi Liu, Bo Du
Ultrasound image segmentation is essential for delineating anatomical structures and lesions, providing the foundation for accurate diagnosis. While the Segment Anything Model (SAM…
Differentiable Adaptive Kalman Filtering via Optimal Transport
Yangguang He, Wenhao Li, Minzhe Li +3
Learning-based filtering has demonstrated strong performance in non-linear dynamical systems, particularly when the statistics of noise are unknown. However, in real-world deployme…
TextAtari: 100K Frames Game Playing with Language Agents
Wenhao Li, Wenwu Li, Chuyun Shen +8
We present TextAtari, a benchmark for evaluating language agents on very long-horizon decision-making tasks spanning up to 100,000 steps. By translating the visual state representa…
Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows
Wenhao Li, Bo Jin, Mingyi Hong +2
This position paper argues that optimization problem solving can transition from expert-dependent to evolutionary agentic workflows. Traditional optimization practices rely on huma…
Reinforced Reasoning for Embodied Planning
Di Wu, Jiaxin Fan, Junzhe Zang +4
Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and natural language goals. While recent vision-language models (VLMs)…
Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding
Shiyue Wang, Haozheng Xu, Yuhan Zhang +4
Multi-Agent Path Finding (MAPF) is a fundamental problem in artificial intelligence and robotics, requiring the computation of collision-free paths for multiple agents navigating f…
Interpretable Hybrid-Rule Temporal Point Processes
Yunyang Cao, Juekai Lin, Hongye Wang +2
Temporal Point Processes (TPPs) are widely used for modeling event sequences in various medical domains, such as disease onset prediction, progression analysis, and clinical decisi…
Scalable Reinforcement Learning for Virtual Machine Scheduling
Junjie Sheng, Jiehao Wu, Haochuan Cui +6
Recent advancements in reinforcement learning (RL) have shown promise for optimizing virtual machine scheduling (VMS) in small-scale clusters. The utilization of RL to large-scale…
A Survey of Automatic Prompt Engineering: An Optimization Perspective
Wenwu Li, Xiangfeng Wang, Wenhao Li +1
The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than we…
Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review
Di Wu, Xian Wei, Guang Chen +4
Embodied multi-agent systems (EMAS) have attracted growing attention for their potential to address complex, real-world challenges in areas such as logistics and robotics. Recent a…
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
Wenhui Ma, Wenhao Li, Bo Jin +2
Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. However, existing simulators oft…
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
Yangguang He, Wenhao Li, Minzhe Li +3
State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been…
Verbalized Bayesian Persuasion
Wenhao Li, Yue Lin, Xiangfeng Wang +3
Information design (ID) explores how a sender influence the optimal behavior of receivers to achieve specific objectives. While ID originates from everyday human communication, exi…
GraphThought: Graph Combinatorial Optimization with Thought Generation
Zixiao Huang, Lifeng Guo, Wenhao Li +6
Graph combinatorial optimization (GCO) problems are central to domains like logistics and bioinformatics. While traditional solvers dominate, large language models (LLMs) offer new…
Interactive 3D Medical Image Segmentation with SAM 2
Chuyun Shen, Wenhao Li, Yuhang Shi +1
Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. Howev…
Carbon Market Simulation with Adaptive Mechanism Design
Han Wang, Wenhao Li, Hongyuan Zha +1
A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility, i.e., reducing carbon emissions to tackle climate chan…
Complementary Information Mutual Learning for Multimodality Medical Image Segmentation
Chuyun Shen, Wenhao Li, Haoqing Chen +5
Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the…
Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
Junjie Sheng, Zixiao Huang, Chuyun Shen +5
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in t…
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
Chuyun Shen, Wenhao Li, Ya Zhang +1
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high…
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
Junjie Sheng, Wenhao Li, Bo Jin +3
Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) als…
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
Wenhao Li, Dan Qiao, Baoxiang Wang +3
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Autom…
Information Design in Multi-Agent Reinforcement Learning
Yue Lin, Wenhao Li, Hongyuan Zha +1
Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. The setting is somewhat idealized because, in actual tasks, other agents in…
LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
Yun Hua, Shang Gao, Wenhao Li +5
In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how othe…
Diverse Policy Optimization for Structured Action Space
Wenhao Li, Baoxiang Wang, Shanchao Yang +1
Enhancing the diversity of policies is beneficial for robustness, exploration, and transfer in reinforcement learning (RL). In this paper, we aim to seek diverse policies in an und…