most citedGeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation

6 citations · 6 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method

Boao Yu, Zimo Chen, Junreng Rao +4

Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointl…

cs.CV2026

Map the Possibilities: Spatial Belief Fields for Language-Goal Aerial Navigation

Haotian Xu, Yue Hu, Zhengqiu Zhu +3

Language-goal aerial navigation requires an agent to local- ize a potentially unobserved target from relational instruc- tions and partial observations, and translate this inferenc…

cs.CV2026

ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering

Wenhao Lu, Zhengqiu Zhu, Xiaofeng Wang +7

Aerial Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (UAVs) to actively perceive the environment and answer natural language questions. Existing outdoor EQA s…

cs.AI2026

A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees

Bin Chen, Shouliang Zhu, Beidan Liu +4

Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large La…

cs.AI2025

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…

cs.CV2025

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…

cs.RO2025★ 6 cited

GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation

Haotian Xu, Yue Hu, Chen Gao +4

Language-goal aerial navigation requires UAVs to localize targets in the complex outdoors, such as urban blocks based on textual instructions. The indoor methods are often hard to…

cs.AI2025

AutoSearch: Unlocking the Reasoning Potential of Large Models for Web-based Source Search

Zhengqiu Zhu, Yatai Ji, Jiaheng Huang +3

Web-based management systems have been widely used in risk control and industrial safety. However, effectively integrating source search capabilities into these systems, to enable…

cs.AI2025

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…