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Zhengqiu Zhu

4 accepted papers

2026

$A_2$DEPT: Large Language Model–Driven Automated Algorithm Design via Evolutionary Program Trees

ICML 2026poster

Designing heuristics for combinatorial optimization problems (COPs) is a fundamental yet challenging task that traditionally requires extensive domain expertise. Recently, Large Language Model (LLM)-based Automated Heuristic Design (AHD) has shown promise in autonomously generating heuristic compone…

Cited by 0SourceScholar
2026

Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology

AAAI 2026technical

Aerial Visual Object Search (AVOS) tasks in urban environments require Unmanned Aerial Vehicles (UAVs) to autonomously search for and identify target objects based on visual inputs without external guidance. Existing approaches struggle in complex urban environments due to redundant semantic process

Cited by 0SourcePDFScholar
2025

CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space

EMNLP 2025

Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings—spanning environment, action, and perception—largely unexplored. To bridge this gap, we introduce CityEQA, a new task where an embodied agent answers open-vocabulary questions t

2025

PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events

EMNLP 2025

Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuraci