Ising Optimization

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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
Mycelial Search: A Graph-Structured Metaheuristic for Continuous Optimisation
Aug 24, 2026Mohammad Mahdi DehshibiMetaheuristic Optimization Ising Optimization
A Large-Scale Empirical Evaluation of MMAO Under Fair-Budget Continuous and Discrete Benchmarks
Jun 30, 2026Jinliang Xu, Liping MaMulti-Agent System Optimization Metaheuristic Optimization
Jinliang Xu is with the current study team · Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search
Jun 29, 2026Jinliang Xu, Liping MaMulti-Agent System Optimization Metaheuristic Optimization
Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
Local-Minima-Preserving Continuous Relaxation of Ising Problems
Jun 29, 2026Debraj Banerjee, Santanu Mahapatra, Kunal N. ChaudhuryCombinatorial Optimization Ising Optimization
Department of Electrical Engineering, Indian Institute of Science, Bangalore, India · Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, India
MMAO: A Metabolic Multi-Agent Optimizer with Endogenous Resource Allocation for Continuous and Discrete Optimization
Jun 26, 2026Jinliang Xu, Liping MaMulti-Agent System Optimization Metaheuristic Optimization
Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
Stage-dependent integer-binary encoding in factorization-machine black-box optimization
Jun 22, 2026Ryo Ogawa, Mayumi Nakano, Yuya Seki +1Surrogate-Assisted Optimization Black-Box Optimization
Graduate School of Science and Technology, Keio University, Kanagawa 223-8522, Japan · Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC), Keio University, Tokyo 108-8345, Japan · Department of Applied Physics and Physico-Informatics, Keio University, Kanagawa 223-8522, Japan +2
WASHH: An Anchor-Aware Whale-Guided Selection Hyper-Heuristic for Continuous Optimization and SVC Configuration
May 13, 2026Yifu Zhao, Xiaofan Zou, Junhao Wei +9Black-Box Optimization Metaheuristic Optimization
Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China · School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai, 200444, China · Pazhou Lab (Huangpu), Guangzhou, 510555, China +2
Drain-Vortex Optimization: A Population-Based Metaheuristic Inspired by Multi-Drain Free-Vortex Flow
May 9, 2026Mohsen Omidi, Brian VaughanBlack-Box Optimization Metaheuristic Optimization
Technological University Dublin, Grangegorman Lower, Dublin, D07 C972, Ireland
A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications
Apr 18, 2026Ruomin Zhu, Abhishek Kumar Singh, Jérémie Laydevant +5Ising Optimization
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA · USRA Research Institute for Advanced Computer Science, Moffett Field, CA, 94035, USA +1

Sep 2, 2026, cs.LGJ/K move · Enter open · S save

Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings

Frank Hu, Shriram Chennakesavalu, David Graff

Prescient Design, Genentech South San Francisco, CA, USA

Black-Box Optimization Large Language Model-Guided Optimization Combinatorial Optimization Ising Optimization

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Frontier large language models (LLMs) have become attractive priors for optimization due to their large-scale pretraining that enables them to navigate a variety of optimization settings. However, the effectiveness of modern reasoning LLMs in batch optimization settings remains underexplored. Here we investigate the performance of the current generation of frontier LLMs as batch optimizers in both continuous and discrete settings. We find that while LLMs are competitive zero-shot batch optimizers for numerical test functions, their performance is brittle compared to classical non-LLM optimization approaches. However, LLM priors are significantly better in semantically rich settings, indicating that their batch optimization behavior is highly effective when navigating and reasoning over the discrete spaces most similar in structure to their pretraining data.

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