3 citations · 6 across the 11 of their papers we have counts for
13 papers
Semantics-Guided Automatic Tensorization for Multiobjective Evolutionary Algorithms: A Multi-Agent Framework
Zhenyu Liang, Beichen Huang, Bowen Zheng +1
Multiobjective evolutionary algorithms (MOEAs) naturally expose population-level parallelism, but many mature implementations encode their computation in sequential program structu…
Persistent Recursive Worlds Enable Autonomous Software Evolution
Beichen Huang, Zhenyu Liang, Bowen Zheng +1
Complex software systems develop over timescales that exceed the lifespan of any individual coding agent. Most agentic software systems preserve continuity through persistent sessi…
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…
Beyond Speedups: Hardware-Aware Evaluation of Evolutionary Algorithms on GPUs
Xinmeng Yu, Tao Jiang, Ran Cheng +2
Evolutionary algorithms (EAs) are increasingly executed on graphics processing units (GPUs) to exploit population-level parallelism. This shift changes the resource model under whi…
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…
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.…
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…
EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration
Beichen Huang, Ran Cheng, Kay Chen Tan
We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent co…
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…
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
Yuxin Ma, Zherui Zhang, Ran Cheng +2
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-o…
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
Bowen Zheng, Ran Cheng, Kay Chen Tan
Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutio…
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…