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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Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval
Apr 20, 2026Junyoung Kim, Anton Korikov, Jiazhou Liang +5Large Language Model-Guided Optimization Active Learning
Sungkyunkwan University, Republic of Korea · University of Toronto, Canada
Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
Apr 20, 2026Jiahao Huang, Peilan Xu, Xiaoya Nan +1Evolutionary Optimization Large Language Model-Guided Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China · Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China
Compiling Deterministic Structure into SLM Harnesses
Apr 19, 2026Zan Kai Chong, Hiroyuki Ohsaki, Bryan NgLarge Language Model-Guided Optimization Small Language Models
School of Science and Technology Kwansei Gakuin University Japan · School of Engineering & Computer Science Victoria University of Wellington New Zealand
LLM-Guided Strategy Synthesis for Scalable Equality Saturation
Apr 19, 2026Chenyun Yin, Youwei Xiao, Yuze Luo +2Large Language Model-Guided Optimization Program Synthesis
Peking University Beijing, China · School of Integrated Circuits Peking University Beijing, China
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
Apr 19, 2026Meghana Kshirsagar, Allen Nie, Ching-An Cheng +5Inference-Time Search Large Language Model-Guided Optimization
AI for Good, Microsoft Redmond · Google DeepMind · Google Research +3
AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation
Apr 17, 2026Weihua Du, Jingming Zhuo, Yixin Dong +9Test-Time Adaptation GPU Kernel Optimization
1Carnegie Mellon University · University of Washington · 3Arm Ltd.
LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
Apr 17, 2026Masakazu Yoshimura, Zitang Sun, Yuiko Sakuma +3Large Language Model-Guided NAS Large Language Model-Guided Optimization
Sony Group Corporation, Tokyo, Japan
Bilevel Optimization of Agent Skills via Monte Carlo Tree Search
Apr 17, 2026Chenyi Huang, Haoting Zhang, Jingxu Xu +2Large Language Model-Guided Optimization LLM Agent Skill Learning
Department of Mathematics National University of Singapore Singapore · Department of Industrial Engineering and Operations Research University of California at Berkeley CA, USA · Department of Decision, Operation and Technology The Chinese University of Hong Kong CHINA
HYPERHEURIST: A Simulated Annealing-Based Control Framework for LLM-Driven Code Generation in Optimized Hardware Design
Apr 17, 2026Shiva Ahir, Prajna Bhat, Alex DoboliLarge Language Model-Guided Optimization Code Generation
Department of ECE
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Apr 16, 2026Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5GPU Kernel Optimization GPU Acceleration
Intel Corporation
Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement
Apr 16, 2026Wenji Fang, Yao Lu, Shang Liu +5Large Language Model-Guided Optimization Electronic Design Automation
Hong Kong University of Science and Technology (HKUST)
Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems
Apr 16, 2026Xing Zhang, Guanghui Wang, Yanwei Cui +4Prompt Sensitivity Large Language Model-Guided Optimization
AWS Generative AI Innovation Center · HSBC Holdings Plc., HSBC Technology Center, China.
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
Apr 10, 2026Zhen Liu, Yuhan Liu, Jinjun Wang +3Large Language Model-Guided NAS Large Language Model-Guided Optimization
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. · Zhongguancun Academy, Beijing, China. · MiLM Plus, Xiaomi Inc. +1
LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization
Apr 9, 2026Tao Li, Kaiyuan Hou, Tuan Vinh +4Molecular Optimization Large Language Model-Guided Optimization
Department of Computer Science, Emory University · School of Computer Science, Carnegie Mellon University · MRC Brain Network Dynamics Unit, University of Oxford +3
Document Optimization for Black-Box Retrieval via Reinforcement Learning
Apr 6, 2026Omri Uzan, Ron Polonsky, Douwe Kiela +1RL for IR Large Language Model-Guided Optimization
Stanford University · ContextualAI
CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery
Apr 2, 2026Ao Qu, Han Zheng, Zijian Zhou +14Large Language Model-Guided Optimization Multi-Agent Collaboration
MIT · Singapore-MIT Alliance for Research and Technology · NUS +7
Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection
Mar 23, 2026Ekta U. Samani, Christopher G. AtkesonManufacturing Large Language Model-Guided Optimization
Robotics Institute at Carnegie Mellon University
KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
Mar 12, 2026Nina Wiedemann, Quentin Leboutet, Michael Paulitsch +2GPU Kernel Optimization GPU Acceleration
Intel Corporation
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Feb 13, 2026Guoan Wang, Shihao Yang, Feng LiuEvolutionary Optimization EEG Decoding
Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Road, Piscataway, NJ 08854, USA
Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback
Feb 13, 2026Sein Kim, Sangwu Park, Hongseok Kang +6Large Language Model-Guided Optimization LLM-Based Program Synthesis
KAIST · Microsoft
ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search
Feb 10, 2026Alexey Kravatskiy, Valentin Khrulkov, Ivan OseledetsAutomated Algorithm Discovery Evolutionary Optimization
1MIRIAI, Russia · 2FusionBrain Lab, Russia · 3AXXX, Russia +1
Test-Time Detoxification without Training or Learning Anything
Jan 14, 2026Baturay Saglam, Dionysis KalogeriasLarge Language Model-Guided Optimization LLM Safety
Department of Electrical and Computer Engineering, Yale University, New Haven, CT.
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Dec 29, 2025Gang Liao, Hongsen Qin, Ying Wang +36GPU Kernel Optimization GPU Acceleration
KernelEvolve Team, Meta Platforms
Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models
Dec 19, 2025Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler +1Quantum Circuit Compilation LLM Fine-Tuning
Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, 55128 Mainz, Germany · Institute of Physics, Johannes Gutenberg University, Staudingerweg 7, 55128 Mainz, Germany · Department of Computer Science, Saarland University, Saarland Informatics Campus, E1 1, 66123 Saarbrücken, Germany
Evolving Excellence: Automated Optimization of LLM-based Agents
Dec 9, 2025Paul Brookes, Vardan Voskanyan, Rafail Giavrimis +18Large Language Model-Guided Optimization LLM Agent Workflow Optimization
TurinTech AI, London, UK · University of Surrey, Guildford, United Kingdom · University of Leeds, Leeds, UK +4
Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization
Dec 8, 2025Haidong Kang, Jun Du, Guo YuMixed-Precision Quantization Large Language Model-Guided Optimization
School of Computer and Communication Engineering, Northeastern University · School of Software Engineering, Beijing Jiaotong University
CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning
Dec 2, 2025Songqiao Su, Xiaoya Li, Albert Wang +3Reinforcement Learning GPU Kernel Optimization
DeepReinforce Team
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Sep 10, 2025Yisong Zhang, Ran Cheng, Guoxing Yi +1Evolutionary Optimization Large Language Model-Guided Optimization
Access Paths for Efficient Ordering with Large Language Models
Aug 30, 2025Fuheng Zhao, Jiayue Chen, Yiming Pan +7Large Language Model-Guided Optimization
1Snowflake Inc. · University of Chicago · 3UC Los Angeles +1
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
Jul 18, 2025Xiaoya Li, Albert Wang, Guoyin Wang +2Reinforcement Learning GPU Kernel Optimization
DeepReinforce Team

Apr 20, 2026, cs.IRJ/K move · Enter open · S save

Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval

Junyoung Kim, Anton Korikov, Jiazhou Liang, Justin Cui+4

Sungkyunkwan University, Republic of Korea · University of Toronto, Canada

Large Language Model-Guided Optimization Active Learning Dense Retrieval

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While Large Language Models (LLMs) exhibit exceptional zero-shot relevance modeling, their high computational cost necessitates framing passage retrieval as a budget-constrained global optimization problem. Existing approaches passively rely on first-stage dense retrievers, which leads to two limitations: (1) failing to retrieve relevant passages in semantically distinct clusters, and (2) failing to propagate relevance signals to the broader corpus. To address these limitations, we propose Bayesian Active Learning with Gaussian Processes guided by LLM relevance scoring (BAGEL), a novel framework that propagates sparse LLM relevance signals across the embedding space to guide global exploration. BAGEL models the multimodal relevance distribution across the entire embedding space with a query-specific Gaussian Process (GP) based on LLM relevance scores. Subsequently, it iteratively selects passages for scoring by strategically balancing the exploitation of high-confidence regions with the exploration of uncertain areas. Extensive experiments across four benchmark datasets and two LLM backbones demonstrate that BAGEL effectively explores and captures complex relevance distributions and outperforms LLM reranking methods under the same LLM budget on all four datasets.

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