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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SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling
Sep 14, 2026Junquan Gu, Shibo Cui, Xiangfeng Luo +1LLM Self-Correction Ensemble Learning
AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
Sep 14, 2026Ji Liu, Saptarshi Majumder, Yiqing Huang +14GPU Kernel Optimization Large Language Model-Guided Optimization
Advanced Micro Devices, Inc.
RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems
Sep 14, 2026Ziyue Yang, Yuting Jiang, Lei Qu +1LLM Inference Large Language Model-Guided Optimization
Shanghai Xingyunzhili Artificial Intelligence Institute, Shanghai, China · Microsoft Research, Redmond, WA, USA
GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization
Sep 14, 2026Rahimeh Neamatian Monemi, Shahin Gelareh, Lubin Cui +1Large Language Model-Guided Optimization Digital Twins
Sharkey Predictim Globe · Département R&T, IUT de Béthune, Université d’Artois, F-62000 Béthune, France · Federal University of Rio de Janeiro, COPPE-PESC, P.O. Box 68511, Rio de Janeiro, RJ 21941-972, Brazil +1
Efficiency Hallucination: Formalizing and Measuring Behavioral Calibration in LLM-Based Code Optimization
Sep 13, 2026Sarah Wilson, Gail Kaiser, Patrick MusauSelective Prediction Large Language Model-Guided Optimization
Google · Columbia University · Vanderbilt University
Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models
Sep 12, 2026Rui Zhu, Minglong Cao, Chenyu Zhou +2RL for Language Models Large Language Model-Guided Optimization
Shanghai Jiao Tong University, Shanghai, China · Fudan University, Shanghai, China
RouteRepair: Instance-Level Failure Diagnosis and Targeted Repair in LLM-Based Automated Heuristic Design for Routing Optimization
Sep 12, 2026Binghao Ji, Di Huang, Jiahui Fang +1Automated Program Repair Automated Heuristic Design
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China, 211189.
Language-Augmented Semantic Priors for B-Spline Surface Fitting
Sep 12, 2026Yunzhong Lou, Yusheng Luo, Jiahao Li +2Large Language Model-Guided Optimization Parametric CAD Modeling
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China
Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction
Sep 11, 2026Kateryna Karpo, Artem ChernodubLLM Prompting Large Language Model-Guided Optimization
Ukrainian Catholic University · YouScan · Zendesk
R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds
Sep 11, 2026Xinghui Tao, Zehao Ye, Guangming Wang +23D Point Cloud Segmentation Large Language Model-Guided Optimization
Construction Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom · Department of Engineering, Durham University, Stockton Road, Durham, DH1 3LE, United Kingdom
Dynamic language model representations for multi-objective reaction optimisation
Sep 10, 2026Joshua W. Sin, David Ming Segura, Bojana Ranković +8Large Language Model-Guided Optimization Molecular Representation Learning
Process Chemistry & Catalysis, Synthetic Molecules Technical Development, F. Hoffmann-La Roche AG, Basel, Switzerland · Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland · National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland +3
$Φ$ -Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?
Sep 9, 2026Leilei Ding, Shumin Wang, Yuting Huang +10LLM Evaluation Large Language Model-Guided Optimization
University of Science and Technology of China · StepFun · Peking University +3
SkillAdam: Stable and Efficient Skill Evolution for Agents
Sep 8, 2026Gaoyuan Li, Meihao Fan, Yizhe Liu +7Large Language Model-Guided Optimization LLM Agent Self-Improvement
Renmin University of China · Tencent
Automated Design of Inventory Policy with Large Language Models: An Exploratory Study
Sep 8, 2026Fenghua Yang, Preet Baxi, Yi Zhang +4Inventory Control Large Language Model-Guided Optimization
University of Michigan, Ann Arbor, MI 48109, USA · Stanford University, Stanford, CA 94305, USA · Queen’s University, Kingston, ON K7L 3N6, Canada +2
Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach
Sep 7, 2026Nguyen Thach, Hau Chan, David Parkes +1Automated Algorithm Discovery Game Theory
University of Nebraska-Lincoln Lincoln, Nebraska, USA · Harvard University Cambridge, Massachusetts, USA
MaxKernel: Agentic Kernel Generation for TPUs
Sep 7, 2026Shangkun Wang, Nina Cai, Charles Hoong +7Large Language Model-Guided Optimization Multi-Agent Coding
1Google · 2Google DeepMind
La Agente 'Optima: Towards Agentic Self-Driving Laboratories
Sep 7, 2026Marcel Müller, Jiaru Bai, Willi Gottstein +14Bayesian Optimization Large Language Model-Guided Optimization
Department of Chemistry, University of Toronto, 80 St. George St., Toronto, ON M5S 3H6, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON2026 M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +11
Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
Sep 4, 2026Sihan Ge, Yichen Lin, Chenyu Zhou +3Large Language Model-Guided Optimization LLM Agent Evaluation
Cardinal Operations · Shanghai Jiao Tong University, Shanghai, China
ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize
Sep 3, 2026Lihao Liu, Peng Tang, Kunwar Yashraj Singh +1Evolutionary Optimization Large Language Model-Guided Optimization
AWS Agentic AI
Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
Sep 2, 2026Frank Hu, Shriram Chennakesavalu, David GraffBlack-Box Optimization Large Language Model-Guided Optimization
Prescient Design, Genentech South San Francisco, CA, USA
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
Sep 1, 2026Yuhan Chen, Zhihua Tian, Mahavir Dabas +7Large Language Model-Guided Optimization Action-Conditioned World Models
1Virginia Tech · 2Amazon · 3Independent Researcher
AgentFactory: Towards Automated Agentic System Design and Optimization
Sep 1, 2026Enci Zhang, Haofeng Wang, Yuesheng Zhu +2Large Language Model-Guided Optimization Agentic Workflows
Peking University Shenzhen Graduate School, Shenzhen, China
SOVER: Formal Certification of Optimization Reformulations via LLM-Assisted SMT Verification
Sep 1, 2026Swapnil Bhattacharyya, Mayank BaranwalLarge Language Model-Guided Optimization
TCS Research, Mumbai · Indian Institute of Technology Bombay
Beneath the Diff: Diagnosing and Mitigating Algorithmic Mode Collapse in Code-Level Autonomous Research Loops
Aug 31, 2026Bowei He, Weixu Zhang, Yili Jin +1Reward Hacking Automated Algorithm Discovery
MBZUAI · McGill University · MirrorSpace Technology +1
Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction
Aug 30, 2026Haoting Zhang, Haoxian Chen, Jiayuan Sheng +4Large Language Model-Guided Optimization Digital Twins
Haoting Zhang, Jiayuan Sheng, Haoxian Chen, Donglin Zhan, Zeyu Zheng, David D. Yao, and Wenpin Tang
LLMs Can Design Near-Optimal OR Algorithms
Aug 27, 2026Jackie BaekInventory Control Automated Algorithm Discovery
Stern School of Business, New York University
A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design
Aug 18, 2026Huanshu Zhang, Kegeng Tang, Lei Kang +3Neural Surrogate Modeling Large Language Model-Guided Optimization
The Pennsylvania State University, Department of Electrical Engineering, University Park, PA 16802, USA · University of Tennessee at Chattanooga, Department of Computer Science and Engineering, Chattanooga, TN 37403, USA
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
Aug 16, 2026Zhongwei Yu, Yan Song, Xue Yan +9Bayesian Optimization Large Language Model-Guided Optimization
The Hong Kong University of Science and Technology (GZ) · University College London · AI Lab, The Yangtze River Delta +3
Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling
Aug 16, 2026Junbo Jacob Lian, Huiling Chen, Hanzhang Qin +1Large Language Model-Guided Optimization Learning to Optimize
Institute of Operations Research and Analytics National University of Singapore, Singapore · Wenzhou Buyi Pharmacy, Wenzhou, China · College of Computer Science and Artificial Intelligence Wenzhou University, Wenzhou, China
LLM-Guided Graph Generation for Structure-Based Local Improvement Methods
Aug 13, 2026Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan SzeiderAutomated Heuristic Design Metaheuristic Optimization
Algorithms and Complexity Group, TU Wien, Austria

Sep 14, 2026, cs.LGJ/K move · Enter open · S save

SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling

Junquan Gu, Shibo Cui, Xiangfeng Luo, Hang Yu

LLM Self-Correction Ensemble Learning Large Language Model-Guided Optimization LLM Reliability Automated Machine Learning

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Automated machine learning (AutoML) is reshaping data-driven science and industrial practice, and as large language models are introduced into AutoML, pipeline reliability becomes as important as automation efficiency. However, existing AutoML still struggles to realize instant feedback and adaptive optimization during execution, so once a run drifts into a suboptimal or failed state, it lacks a process-level correction mechanism. The fundamental pathology lies in its one-way pipeline: intermediate failures are typically terminated or bypassed, while fixed paradigms often strengthen model generation but leave ensemble decisions static, weakening both execution reliability and the controlled use of structural diversity. This indicates that LLM-driven AutoML needs a closed-loop ability for trial-correction-improvement together with evidence-based use of model diversity. To this end, we propose SAGE-Loop, a reliable closed-loop, self-adaptive, LLM-driven AutoML framework that performs multi-round generation and validation for trial-and-repair, and adaptively selects ensemble strategies in both supervised and unsupervised tasks, thereby unifying how to generate with how to use models. Across 20 public datasets, SAGE-Loop consistently improves performance and stability on classification, regression, and clustering tasks. Additional results further show its ability to recover from execution failures and maintain robust pipeline behavior.

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