Large Language Model-Guided Optimization

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66 papers in the last four weeks, up 214% on the four weeks before. 0.6% of all new papers.

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CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels
May 6, 2026Xing Ma, Yangjie Zhou, Wu Sun +6GPU Kernel Optimization GPU Acceleration
Shanghai Jiao Tong University · National University of Singapore · Shanghai Qi Zhi Institute
Elicitation Matters: How Prompts and Query Protocols Shape LLM Surrogates under Sparse Observations
May 6, 2026Ge Lei, Samuel J. CooperSurrogate Modeling Bayesian Optimization
Dyson School of Design Engineering Imperial College London Exhibition Road, London SW7 2AZ
CodeEvolve: LLM-Driven Evolutionary Optimization with Runtime-Enriched Target Selection for Multi-Language Code Enhancement
May 6, 2026Ajay Krishna Borra, Wenzhuo Yang, Samarth Arora +9Evolutionary Optimization Large Language Model-Guided Optimization
Salesforce
Automated Large-scale CVRP Solver Design via LLM-assisted Flexible MCTS
May 5, 2026Tong Guo, Caishun Chen, Yew Soon OngAutomated Algorithm Discovery Large Language Model-Guided Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore
Programmatic Context Augmentation for LLM-based Symbolic Regression
May 4, 2026Hao Liu, Xiao-Wen Yang, Atharva Sehgal +4Large Language Model-Guided Optimization Large Language Model-Guided Scientific Discovery
Caltech · Nanjing University · University of Texas at Austin +1
ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
May 4, 2026Guangrui XieMulti-Agent LLM Systems Large Language Model-Guided Optimization
Enhanced LLM Reasoning by Optimizing Reward Functions with Search-Driven Reinforcement Learning
May 3, 2026Arash Ahmadi, Sarah Sharif, Yaser +1Reinforcement Learning Large Language Model-Guided Optimization
Mike · School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019 USA · Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory, University of Oklahoma, Norman, OK, USA
Discover Fast Power Allocation Solution for Multi-Target Tracking via AlphaEvolve Evolution
May 3, 2026Zhenkang Hou, Wenqiang Pu, Junkun Yan +2Large Language Model-Guided Optimization Wireless Resource Allocation
National Laboratory of Radar Signal Processing, Xidian University, Xian, China · Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
May 2, 2026Zhiwen Ruan, Yichao Du, Jianjie Zheng +6LLM Fine-Tuning Large Language Model-Guided Optimization
Southern University of Science and Technology · Alibaba Group · Shanghai University of Finance and Economics +2
Learning to Act and Cooperate for Distributed Black-Box Consensus Optimization
May 1, 2026Zi-Bo Qin, Feng-Feng Wei, Tai-You Chen +1Multi-Agent System Optimization Multi-Agent Coordination
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
May 1, 2026Chenyu Huang, Jianghao Lin, Zhengyang Tang +4Inventory Control Large Language Model-Guided Optimization
1Shanghai University of Finance and Economics · 2Shanghai Jiao Tong University · 3The Chinese University of Hong Kong, Shenzhen +2
Design Structure Matrix Modularization with Large Language Models
Apr 30, 2026Shuo Jiang, Jianxi LuoLarge Language Model-Guided Optimization Combinatorial Optimization
Department of Systems Engineering City University of Hong Kong, Hong Kong
Language Models Refine Mechanical Linkage Designs Through Symbolic Reflection and Modular Optimisation
Apr 30, 2026João Pedro Gandarela, Thiago Rios, Stefan Menzel +1Mechanism Design Large Language Model-Guided Optimization
Idiap Research Institute, Switzerland · École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Honda Research Institute Europe, Germany +2
Relation Reasoning with LLMs in Expensive Optimization
Apr 30, 2026Ye Lu, Bingdong Li, Aimin Zhou +1Surrogate-Assisted Optimization Black-Box Optimization
Shanghai Institute of Al for Education, East China Normal University, Shanghai 200062, China
LLM-Guided Runtime Parameter Optimization for Energy-Efficient Model Inference
Apr 29, 2026Katelyn Crumpacker, Dimitrios NikolopoulosLLM Inference Efficiency Energy-Efficient ML
Virginia Polytechnic and State University
MappingEvolve: LLM-Driven Code Evolution for Technology Mapping
Apr 29, 2026Rongliang Fu, Yi Liu, Qiang Xu +1Evolutionary Optimization Large Language Model-Guided Optimization
Chinese University of Hong Kong Hong Kong, China
Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization
Apr 28, 2026Alexander Blasberg, Vasilis Kypriotis, Dimitrios SkarlatosLarge Language Model-Guided Optimization
Carnegie Mellon University
SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution
Apr 27, 2026Sichun Luo, Yi Huang, Haochen Luo +7Automated Algorithm Discovery Evolutionary Optimization
1The University of Hong Kong · 2JIUTIAN Research, China Mobile · 3City University of Hong Kong
Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning
Apr 27, 2026Dong Liu, Yanxuan Yu, Ying Nian WuCoT Reasoning Large Language Model-Guided Optimization
University of California, Los Angeles · Columbia University
A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
Apr 27, 2026Bin Chen, Shouliang Zhu, Beidan Liu +4Automated Algorithm Discovery Automated Heuristic Design
The Institute of Intelligent Computing, University of Electronic Science and Technology of China, Chengdu 611731, China · College of Systems Engineering, National University of Defense Technology, Changsha 410073, China · Academy of Military Medical Sciences, Beijing 100071, China
C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs
Apr 24, 2026Rui Gao, Youngseung Jeon, Swastik Roy +2RL for Language Models Molecular Optimization
University of California, Los Angeles · Amazon
Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
Apr 24, 2026Wang Rui, Lu DiannanLarge Language Model-Guided Optimization
Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models
Apr 23, 2026Tanmay Gautam, Alireza Bahramali, Sandeep AtluriLarge Language Model-Guided Optimization Program Synthesis
Responsible AI, Microsoft
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
Apr 23, 2026Xinyu Zhang, Boxuan Zhang, Yuchen Wan +5LLM Evaluation Benchmark Design
School of Computer Science and Technology, Xi’an Jiaotong University · 2Ministry of Education Key Laboratory of Intelligent Networks and Network Security, China · 4Lenovo Research +1
Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving
Apr 22, 2026Xinyu Zhang, Yuchen Wan, Boxuan Zhang +4Large Language Model-Guided Optimization Agent Memory Management
School of Computer Science and Technology, Xi’an Jiaotong University · 2Ministry of Education Key Laboratory of Intelligent Networks and Network Security, China · 3Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, China
AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
Apr 21, 2026Xidong Wu, Yue Zhuan, Ruoqiao Wei +7Multi-Agent LLM Systems Multi-Agent System Optimization
Google Mountain View, California, USA
What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search
Apr 21, 2026Xinhao Zhang, Xi Chen, François Portet +1Evolutionary Optimization Large Language Model-Guided Optimization
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France
Evaluation-driven Scaling for Scientific Discovery
Apr 21, 2026Haotian Ye, Haowei Lin, Jingyi Tang +22Large Language Model-Guided Optimization Inference-Time Scaling
Wizard Intelligence Learning Lab, Stanford University · Peking University, Tsinghua University
Streamliners for Answer Set Programming
Apr 21, 2026Florentina Voboril, Martin Gebser, Stefan Szeider +1Logic Programming Large Language Model-Guided Optimization
TU Wien Vienna, Austria · University of Klagenfurt Klagenfurt, Austria
ContraPrompt: Contrastive Prompt Optimization via Dyadic Reasoning Trace Analysis
Apr 20, 2026Rishav Rishav, Pushpak Pujari, Pushpendre RastogiPairwise Comparison CoT Reasoning
Vizops AI

May 6, 2026, cs.LGJ/K move · Enter open · S save

CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels

Xing Ma, Yangjie Zhou, Wu Sun, Zihan Liu+5

Shanghai Jiao Tong University · National University of Singapore · Shanghai Qi Zhi Institute

GPU Kernel Optimization GPU Acceleration Self-Attention Large Language Model-Guided Optimization LLM-Based Program Synthesis Transformer Attention+1

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Efficient CUDA implementations of attention mechanisms are critical to modern deep learning systems, yet supporting diverse and evolving attention variants remains challenging. Existing frameworks and compilers trade performance for flexibility, while expert-written kernels achieve high efficiency but are difficult to adapt. Recent work explores large language models (LLMs) for GPU kernel generation, but prior studies report unstable correctness and significant performance gaps for complex operators such as attention. We present CuBridge, an LLM-based framework that adapts expert-written attention kernels through a structured lift-transfer-lower workflow. CuBridge starts from expert-written CUDA attention kernels and lifts them into an executable intermediate representation that makes execution orchestration explicit while abstracting low-level CUDA syntax. Given a user-provided PyTorch specification, CuBridge generates and verifies a target IR program, then reconstructs optimized CUDA code via reference-guided lowering. Across diverse attention variants and GPU platforms, CuBridge consistently produces correct kernels and substantially outperforms general frameworks, compiler-based approaches, and prior LLM-based methods.

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