11 citations · 26 across the 14 of their papers we have counts for
16 papers
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…
LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery
Xingyu Wu, Kui Yu, Jibin Wu +1
This paper critically re-evaluates LLMs' role in causal discovery and argues against their direct involvement in determining causal relationships. We demonstrate that LLMs' autoreg…
Diversity-Aware Policy Optimization for Large Language Model Reasoning
Jian Yao, Ran Cheng, Xingyu Wu +2
The reasoning capabilities of large language models (LLMs) have advanced rapidly, particularly following the release of DeepSeek R1, which has inspired a surge of research into dat…
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Yu Zhou, Xingyu Wu, Jibin Wu +2
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in…
Design Principle Transfer in Neural Architecture Search via Large Language Models
Xun Zhou, Xingyu Wu, Liang Feng +2
Transferable neural architecture search (TNAS) has been introduced to design efficient neural architectures for multiple tasks, to enhance the practical applicability of NAS in rea…
Learning to Transfer for Evolutionary Multitasking
Sheng-Hao Wu, Yuxiao Huang, Xingyu Wu +3
Evolutionary multitasking (EMT) is an emerging approach for solving multitask optimization problems (MTOPs) and has garnered considerable research interest. The implicit EMT is a s…
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
Xingyu Wu, Yan Zhong, Jibin Wu +3
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical…
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Jibin Wu +2
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals th…
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Beichen Huang, Xingyu Wu, Yu Zhou +4
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diver…
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Yuxiao Huang, Wenjie Zhang, Liang Feng +2
Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitat…
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Xingyu Wu, Sheng-hao Wu, Jibin Wu +2
Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artif…
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
Xingyu Wu, Yan Zhong, Jibin Wu +2
Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution. Mainstream al…