13 citations · 16 across the 14 of their papers we have counts for
9 papers · 1 filter
TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control
Yuwei Du, Jun Zhang, Jie Feng +3
Traffic simulation is important for transportation optimization and policy making. While existing simulators such as SUMO and MATSim offer fully-featured platforms and utilities, u…
CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation
Yuwei Du, Jie Feng, Jian Yuan +1
Human mobility simulation plays a crucial role in various real-world applications. Recently, to address the limitations of traditional data-driven approaches, researchers have expl…
OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data
Jinwei Zeng, Yu Liu, Guozhen Zhang +4
Accurately estimating high-resolution carbon emissions is crucial for effective emission governance and mitigation planning. While conventional methods for precise carbon accountin…
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
Yiwen Song, Qianyue Hao, Qingmin Liao +2
Model ensemble is a useful approach in reinforcement learning (RL) for training effective agents. Despite wide success of RL, training effective agents remains difficult due to the…
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models
Qianyue Hao, Yiwen Song, Qingmin Liao +2
Policy exploration is critical in reinforcement learning (RL), where existing approaches include greedy, Gaussian process, etc. However, these approaches utilize preset stochastic…
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning
Qianyue Hao, Sibo Li, Jian Yuan +1
Despite rapid advancements in large language models (LLMs), the token-level autoregressive nature constrains their complex reasoning capabilities. To enhance LLM reasoning, inferen…
DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions
Jinhui Yi, Huan Yan, Haotian Wang +2
Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of de…
Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services
Jinhui Yi, Huan Yan, Haotian Wang +2
Accurately estimating package delivery time is essential to the logistics industry, which enables reasonable work allocation and on-time service guarantee. This becomes even more n…
Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content
Bingbing Fan, Lin Chen, Songwei Li +4
Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on soci…