2 citations · 3 across the 12 of their papers we have counts for
14 papers
SkillFocus: Evolving Agent Skills via Capability Decomposition
Ning Wang, Zhiren Gong, Bingdong Li +2
Agent skill evolution seeks to improve reusable procedural guidance for large language model (LLM) agents through iterative revision. Existing methods base each revision mainly on…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Ke Zhao, Zixiang Di, Hong Qian +9
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented…
A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization
Yaoming Yang, Shuai Wang, Bingdong Li +2
Dynamic multi-objective optimization requires continuous tracking of moving Pareto fronts. Existing methods struggle with irregular mutations and data sparsity, primarily facing th…
IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization
Shuai Wang, Yaoming Yang, Bingdong Li +2
Learning Path Recommendation (LPR) aims to generate personalized sequences of learning items that maximize long-term learning effect while respecting pedagogical principles and ope…
Evolutionary Reinforcement Learning based AI tutor for Socratic Interdisciplinary Instruction
Mei Jiang, Haihai Shen, Zhuo Luo +4
Cultivating higher-order cognitive abilities -- such as knowledge integration, critical thinking, and creativity -- in modern STEM education necessitates a pedagogical shift from p…
SID: Benchmarking Guided Instruction Capabilities in STEM Education with a Socratic Interdisciplinary Dialogues Dataset
Mei Jiang, Houping Yue, Bingdong Li +4
Fostering students' abilities for knowledge integration and transfer in complex problem-solving scenarios is a core objective of modern education, and interdisciplinary STEM is a k…
ELMES: An Automated Framework for Evaluating Large Language Models in Educational Scenarios
Shou'ang Wei, Xinyun Wang, Shuzhen Bi +9
The emergence of Large Language Models (LLMs) presents transformative opportunities for education, generating numerous novel application scenarios. However, significant challenges…
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network
Bingdong Li, Mei Jiang, Hong Qian +3
Evolutionary Reinforcement Learning (ERL), training the Reinforcement Learning (RL) policies with Evolutionary Algorithms (EAs), have demonstrated enhanced exploration capabilities…
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
Bingdong Li, Zixiang Di, Yanting Yang +5
In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algo…
ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems
Hong Qian, Shuo Liu, Mingjia Li +3
Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs. CDMs play a fundamental role in online education systems since they sign…
A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
Hao Hao, Xiaoqun Zhang, Bingdong Li +1
Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function…
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
Bingdong Li, Zixiang Di, Yongfan Lu +5
Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling compl…
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Yongfan Lu, Zixiang Di, Bingdong Li +5
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to tran…
Pointer Networks Trained Better via Evolutionary Algorithms
Muyao Zhong, Shengcai Liu, Bingdong Li +3
Pointer Network (PtrNet) is a specific neural network for solving Combinatorial Optimization Problems (COPs). While PtrNets offer real-time feed-forward inference for complex COPs…