2 citations · 2 across the 11 of their papers we have counts for
13 papers
DEFlow: Deep Equilibrium Flow Maximum-Entropy Reinforcement Learning
Ruslan Ageev, Huaze Tang, Runqing Miao +2
Expressive flow policies can represent multiple high-value actions, but sampling them with few integration steps while retaining tractable action densities remains challenging. We…
CB-Agent: Concept-Based In-Context Learning for Sequential Action Selection
Qingyue Zhang, Huaze Tang, Minyu Zhang +3
In-context learning (ICL) allows large language models to use demonstrations without updating their parameters. For interactive agents, demonstrations are trajectories of reasoning…
Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation
Yan Tang, Tingyu Cao, Yuanbo Tang +2
Large language model agents can plan, invoke tools, and modify external states, yet most systems still take an explicit user instruction as a fixed starting point. Proactive servic…
Revealing Safety-Critical Scenarios for UTM via Transformer
Huaze Tang, Bill Zeng, Chao Wang +3
Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely. UTM systems are safety-critical which canno…
Toward Temporal Realism in City-Scale Crisis Response Simulation using LLM Agents
Anping Zhang, Yang Tan, Yuanbo Tang +4
Human collective participation is rarely steady in time: it is bursty, with short episodes of intense activity separated by long quiet intervals. In crisis response and community m…
RUMAD: Reinforcement-Unifying Multi-Agent Debate
Chao Wang, Han Lin, Huaze Tang +2
Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus…
ProAgentBench: Evaluating LLM Agents for Proactive Assistance with Real-World Data
Yuanbo Tang, Huaze Tang, Tingyu Cao +6
Proactive agents that anticipate user intentions without explicit prompts represent a significant evolution in human-AI interaction, promising to reduce cognitive load and streamli…
Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors
Pengcheng Hao, Huaze Tang, Ercan Engin Kuruoglu +1
Bayesian deep learning (BDL) provides a principled framework for reliable uncertainty quantification by combining deep neural networks with Bayesian inference. A central challenge…
Reinforced Domain Selection for Continuous Domain Adaptation
Hanbing Liu, Huaze Tang, Yanru Wu +2
Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain.…
Bidirectional Soft Actor-Critic: Leveraging Forward and Reverse KL Divergence for Efficient Reinforcement Learning
Yixian Zhang, Huaze Tang, Changxu Wei +1
The Soft Actor-Critic (SAC) algorithm, a state-of-the-art method in maximum entropy reinforcement learning, traditionally relies on minimizing reverse Kullback-Leibler (KL) diverge…
Policy Newton Algorithm in Reproducing Kernel Hilbert Space
Yixian Zhang, Huaze Tang, Chao Wang +1
Reinforcement learning (RL) policies represented in Reproducing Kernel Hilbert Spaces (RKHS) offer powerful representational capabilities. While second-order optimization methods l…
Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for Resource Management in Space-Air-Ground Integrated Networks
Hengxi Zhang, Huaze Tang, Wenbo Ding +1
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous devices including low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users (GU…
Lightweight Neural Network with Knowledge Distillation for CSI Feedback
Yiming Cui, Jiajia Guo, Zheng Cao +5
Deep learning has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher comp…