Trajectory prediction is a crucial task in modeling human behavior, especially in fields as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, lack of explainability, and generalization issues that limit their practical adoption. In this paper, we introduce TrajEvo, a framework that leverages Large Language Models (LLMs) to automatically design trajectory prediction heuristics. TrajEvo employs an evolutionary algorithm to generate and refine prediction heuristics from past trajectory data. We introduce a Cross-Generation Elite Sampling to promote population diversity and a Statistics Feedback Loop allowing the LLM to analyze alternative predictions. Our evaluations show TrajEvo outperforms previous heuristic methods on the ETH-UCY datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to the unseen SDD dataset. TrajEvo represents a first step toward automated design of fast, explainable, and generalizable trajectory prediction heuristics. We make our source code publicly available to foster future research at https://github.com/ai4co/trajevo.
BuildEvo: Designing Building Energy Consumption Forecasting Heuristics via LLM-driven Evolution
Department of the Built Environment, National University of Singapore（新加坡国立大学建筑环境系）；Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology（韩国科学技术院工业与系统工程系）
Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models
Sun Yat-Sen University（中山大学）；Zhejiang University（浙江大学）；BraneMatrix AI；Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Renmin University of China（中国人民大学）
A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
The Institute of Intelligent Computing, University of Electronic Science and Technology of China（电子科技大学智能计算学院）；College of Systems Engineering, National University of Defense Technology（国防科技大学系统工程学院）；Academy of Military Medical Sciences（军事医学科学院）
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
Bielefeld University（比勒菲尔德大学）；Radical Numerics；KAIST（韩国科学技术院）；Peking University（北京大学）；University of Turin（都灵大学）；Stanford University（斯坦福大学）；RIKEN（日本研究机构）；Omelet；University of Vienna（维也纳大学）
School of EECS；Dept. of Biochemistry \& Biophysics , Oregon State University , USA；Center for RNA Biology；Computational Biology , University of Rochester Medical Center , USA
