7 papers
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments
Haotian Xu, Yue Hu, Zhengqiu Zhu +7
Cross-view spatial reasoning is essential for embodied AI, underpinning spatial understanding, mental simulation and planning in complex environments. Existing benchmarks primarily…
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao +4
Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather t…
Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events
Mengzhu Liu, Zhengqiu Zhu, Chuan Ai +8
During sudden disaster events, accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this probl…
Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology
Yatai Ji, Zhengqiu Zhu, Yong Zhao +7
Aerial Visual Object Search (AVOS) tasks in urban environments require Unmanned Aerial Vehicles (UAVs) to autonomously search for and identify target objects using visual and textu…
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory
Weichen Zhang, Chen Gao, Shiquan Yu +6
Aerial vision-and-language navigation (VLN), requiring drones to interpret natural language instructions and navigate complex urban environments, emerges as a critical embodied AI…
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
Yong Zhao, Kai Xu, Zhengqiu Zhu +7
Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings-spanning environment, action, and perception-largely unex…