Large Language Model-Guided Optimization

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Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows
May 12, 2026Martins OtunMolecular Optimization Large Language Model-Guided Optimization
1Algonix AI Ltd., Scotland, United Kingdom
Task-Adaptive Embedding Refinement via Test-time LLM Guidance
May 12, 2026Ariel Gera, Shir Ashury-Tahan, Gal Bloch +2Zero-Shot Text Classification Large Language Model-Guided Optimization
IBM Research
Formalize, Don't Optimize: The Heuristic Trap in LLM-Generated Combinatorial Solvers
May 12, 2026Haoyu Wang, Yuliang Song, Tao Li +5Large Language Model-Guided Optimization LLM-Based Program Synthesis
University of Pennsylvania · University of Toronto · Google DeepMind +1
Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
May 12, 2026Peipei Xu, SiYuan Ma, Yaohua Liu +4Large Language Model-Guided Optimization Constrained Optimization
University of Shanghai for Science and Technology · The Hong Kong University of Science and Technology
Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models
May 12, 2026Jinbiao Chen, Shuang Jin, Guoyun Zhang +3Memory-Augmented Language Models Large Language Model-Guided Optimization
Department of Industrial Systems Engineering and Management, National University of Singapore · Department of Data and Systems Engineering, The University of Hong Kong · Institute of Operations Research and Analytics, National University of Singapore +1
AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
May 12, 2026Taicheng Guo, Nitesh V. Chawla, Olaf Wiest +1Multi-Fidelity Optimization Large Language Model-Guided Optimization
University of Notre Dame
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
May 11, 2026Mingxu Zhang, Yuhan Li, Lujundong Li +3Molecular Optimization LLM Interpretability
The Hong Kong University of Science and Technology (Guangzhou) · Nanjing University of Aeronautics and Astronautics · The 63rd Research Institute, National University of Defense Technology, Nanjing
Budget-Efficient Automatic Algorithm Design via Code Graph
May 11, 2026Maxime Bouscary, Manxi Wu, Saurabh AminAutomated Algorithm Discovery Large Language Model-Guided Optimization
Operations Research Center Massachusetts Institute of Technology · Department of Civil and Environmental Engineering University of California, Berkeley · Laboratory for Information & Decision Systems Massachusetts Institute of Technology
LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs
May 11, 2026Zhinan Hou, Xingchen Li, Yankai Zhang +2Branch and Bound Mixed-Integer Linear Programming
Department of Automation, BNRist, Tsinghua University, Beijing, China.
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
May 11, 2026Qingchuan Zhang, He Cao, Hao Li +6Molecular Optimization Large Language Model-Guided Optimization
University of Science and Technology of China · International Digital Economy Academy · Peking University +5
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
May 10, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuEvolutionary Optimization Large Language Model-Guided Optimization
The University of Hong Kong, China · Stellaris AI Limited, China
LEVI: Stronger Search Architectures Can Substitute for Larger LLMs in Evolutionary Search
May 10, 2026Temoor TanveerEvolutionary Optimization Large Language Model-Guided Optimization
Independent Researcher
Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
May 10, 2026Víctor GallegoGPU Kernel Optimization Benchmark Design
Komorebi AI Technologies, Madrid, Spain
ARES-LSHADE: Autoresearch-Enhanced LSHADE with Memetic Polish for the GNBG Benchmark
May 9, 2026Abdullah Naeem, Md Wasi Ul kabir, Manish Bhatt +3Differential Evolution Evolutionary Optimization
University of New Orleans · Amazon†
Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs
May 9, 2026Xiaozhe Li, Xinyu Fang, Shengyuan Ding +5RL Benchmarks Reinforcement Learning
Tongji University1 · Shanghai AI Lab2 · Zhejiang University3 +3
OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces
May 9, 2026Xiaozhe Li, Jixuan Chen, Xinyu Fang +4Large Language Model-Guided Optimization LLM Agent Self-Improvement
Tongji University1 · Shanghai AI Lab2 · University of California San Diego5 +2
CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
May 8, 2026Shiyang Li, Zijian Zhang, Guangyan Sun +5LLM Evaluation GPU Kernel Optimization
University of Minnesota · Northeastern University
LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
May 8, 2026Tong Zheng, Haolin Liu, Chengsong Huang +10LLM Inference Efficiency Automated Algorithm Discovery
UMD · UVA · WUSTL +3
ADKO: Agentic Decentralized Knowledge Optimization
May 8, 2026Lucas Nerone Rillo, Zhanhong Jiang, Nastaran Saadati +4Multi-Agent System Optimization AI Agents for Scientific Discovery
Iowa State University · New York University
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
May 8, 2026Nicolas Menet, Andreas Krause, Abbas RahimiThompson Sampling Black-Box Optimization
IBM Research – Zurich · Department of Computer Science, ETH Zürich
HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
May 8, 2026Yuping Yan, Jirui Han, Fei Ming +2Multi-Agent LLM Systems Multi-Agent System Optimization
School of Engineering Westlake University
Kernel Foundry: A Diagnosis-driven Evolutionary Kernel Optimizer with Multi-Experts
May 8, 2026Zixuan Huang, Da Chen, Kecheng Huang +5GPU Kernel Optimization GPU Acceleration
1The Chinese University of Hong Kong · 2Noah’s Ark Lab, Huawei
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
May 7, 2026Minghao Yan, Bo Peng, Benjamin Coleman +11Test-Time Adaptation Reinforcement Learning
1Google · University of Wisconsin–Madison · 3Google DeepMind
Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
May 7, 2026Shivaram Subramanian, Zhengliang Xue, Markus Ettl +2Large Language Model-Guided Optimization Discrete Choice Modeling
IBM T.J. Watson Research Center Yorktown Heights, New York, 10598, U.S.A.
A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis
May 7, 2026Teo SusnjakLarge Language Model-Guided Optimization Language Model Calibration
School of Mathematical and Computational Sciences Massey University, Auckland, New Zealand
AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization
May 7, 2026Zijiu Yang, Zixiang Zhang, Shunpu Tang +2Multi-Agent LLM Systems Large Language Model-Guided Optimization
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
CoupleEvo: Evolving Heuristics for Coupled Optimization Problems Using Large Language Models
May 7, 2026Thomas Bömer, Bastian Amberg, Max Disselnmeyer +1Automated Heuristic Design Evolutionary Optimization
Karlsruhe Institute of Technology Karlsruhe, Germany
Back to the Beginning of Heuristic Design: Bridging Code and Knowledge with LLMs
May 7, 2026Nguyen Viet Tuan Kiet, Bui Dinh Pham, Dao Van Tung +2Automated Heuristic Design Large Language Model-Guided Optimization
Hanoi University of Science and Technology, Hanoi, Vietnam · University of Sydney, Sydney, Australia
LLM-Driven Design Space Exploration of FPGA-based Accelerators
May 7, 2026Vinamra Sharma, Xingjian Fu, Jude Haris +1Large Language Model-Guided Optimization Electronic Design Automation
School of Computing Science, University of Glasgow, Scotland, UK
Agentic Discovery of Exchange-Correlation Density Functionals
May 6, 2026Titouan Duston, Jiashu Liang, Yuanheng Wang +6Quantum Chemistry Large Language Model-Guided Optimization
ByteDance Seed · Princeton University · Work done at ByteDance Seed

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Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows

Martins Otun

1Algonix AI Ltd., Scotland, United Kingdom

Molecular Optimization Large Language Model-Guided Optimization Agentic Workflows Large Language Model-Guided Scientific Discovery

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Cold-chain storage limits access to insulin for hundreds of millions of people; a thermally protective patch polymer could help, but the design space is too large for exhaustive experiment. Starting from that problem, we narrow to an agentic workflow: a large language model (LLM) calls physics-based tools through the Model Context Protocol (MCP), searching the discrete PSMILES space under a budget of OpenMM Packmol-matrix evaluations. The LLM acts as an implicit acquisition function conditioned on a persistent "discovery world": hypotheses, literature claims, and simulation outcomes updated each iteration. Under matched oracle budgets, the best autonomous campaign reaches an insulin-polymer interaction energy of -2263 kJ/mol, outperforming reinforcement-learning baselines by 68% and Bayesian optimization by 19%. Three independent campaigns converge on one structural motif (dense hydrogen-bond donors and acceptors per repeat unit) while physics checks reject infeasible packings and name-structure mismatches before they steer the next step. The science stage is CPU-bound and runs on commodity hardware. More broadly, the same architecture and workflow designed here applies to other protein-stabilization tasks whenever a tractable screening oracle is available.

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