most citedRUMAD: Reinforcement-Unifying Multi-Agent Debate

2 citations · 2 across the 3 of their papers we have counts for

collaborators

8 papers

cs.AI2026

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…

cs.SI2026

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…

cs.AI20262 cited

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…

cs.HC2026

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…

cs.LG2026

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…

cs.LG2025

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.…