9 papers
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi +1
Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily focuses on evolutionary optimization, i.…
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
Jian Yao, Xiongcai Luo, Ran Cheng +1
Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequ…
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…
Learning to Evolve for Optimization via Stability-Inducing Neural Unrolling
Jiaxin Gao, Yaohua Liu, Ran Cheng +1
Evolutionary algorithms serve as a powerful paradigm for tackling optimization challenges, yet their reliance on manually engineered heuristics inherently limits their adaptability…
Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning
Tao Jiang, Kebin Sun, Zhenyu Liang +3
Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despit…
VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarks
Jian Yao, Ran Cheng, Kay Chen Tan
Recent advances in reinforcement learning (RL) have led to substantial improvements in the mathematical reasoning abilities of LLMs, as measured by standard benchmarks. Yet these g…