11 citations · 26 across the 14 of their papers we have counts for
5 papers · 1 filter
Online Automated Algorithm Design with Large Language Models
Zhiyao Zhang, Yichen Li, Xingyu Wu +2
Large language models (LLMs) enable automated algorithm design (AAD) through reasoning and code synthesis. However, most existing LLM-based AAD methods separate algorithm design fr…
How to Guide LLM Generation: Dual-Surrogate Guided Search for Automated Heuristic Design
Yuhan Wang, Chaoda Peng, Xingyu Wu +2
Large language models (LLMs) have made automated heuristic design (AHD) increasingly practical by generating executable heuristic code from task descriptions and evaluator feedback…
Semantics-Aware Bilevel Co-Evolution: Towards Automated Multicomponent Algorithm Design
Zhiyao Zhang, Shenghao Wu, Xingyu Wu +1
LLM-assisted evolutionary search (LES) has emerged as a promising paradigm for automated algorithm design. However, existing methods usually suffer from two inherent limitations wh…
Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution
Haiyun Qiu, Xingyu Wu, Kay Chen Tan
Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing CMM methods are hindered by a…
Fine-Grained Model Merging via Modular Expert Recombination
Haiyun Qiu, Xingyu Wu, Liang Feng +1
Model merging constructs versatile models by integrating task-specific models without requiring labeled data or expensive joint retraining. Although recent methods improve adaptabi…