3 papers
cs.AI2026
AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
Junhao Qiu, Qinglong Hu, Xialiang Tong +3
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined cont…
cs.AI2026
Beyond Average Performance: Dynamic Instance Clustering and Specialized Algorithm Design in LLM-Assisted Evolutionary Search
Qinglong Hu, Qingfu Zhang, Fei Liu +3
Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods primarily optimize for aver…
cs.LG2025
Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies
Qinglong Hu, Xialiang Tong, Mingxuan Yuan +3
Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to unders…