Natural Language to Optimization Modeling

Follow this topic Ask about this topic

Momentum

11 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 60

All topics

YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate
Jun 22, 2026Suyash MishraLarge Language Model-Guided Optimization Robust Optimization
AI Researcher
StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling
Jun 13, 2026Jiajun Li, Yu Ding, Shisi Guan +2Reinforcement Learning Large Language Model-Guided Optimization
School of Computer Science and Engineering, Southeast University · Northwest A&F University
Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment
Jun 12, 2026Junyeop Bang, Byongho Lee, Dohyun An +1Multi-Robot Task Allocation Quantum Approximate Optimization Algorithm
Department of Smart Convergence Korea University Republic of Korea · School of Electrical Engineering Korea University Republic of Korea
Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing Problems
Jun 3, 2026Xizi Luo, Changhong He, Dongdong Geng +2Vehicle Routing Natural Language to Optimization Modeling
Beihang University, Beijing, China · Baidu Inc., Beijing, China
Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models
Jun 2, 2026Eleanor Brosius, Yuji Takubo, Daniele Gammelli +2Trajectory Optimization Large Language Model-Guided Optimization
Stanford University 496 Lomita Mall, Stanford, CA, 94305
OptSkills: Learning Generalizable Optimization Skills from Problem Archetypes via Cluster-Based Distillation
May 28, 2026Haochen Yang, Ke Zhao, Mengyuan Ma +3Large Language Model-Guided Optimization OOD Generalization
Shanghai Institute of AI for Education and School of Computer Science and Technology, East China Normal University, Shanghai, China · Ant Group, Hangzhou, China · Key Laboratory of Mathematics and Engineering Applications (MoE) and School of Mathematical Sciences, East China Normal University, Shanghai, China +2
Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
May 28, 2026Pedro Orvalho, Marta Kwiatkowska, Guillem Alenyà +1Large Language Model-Guided Optimization LLM-Based Program Synthesis
Artificial Intelligence Research Institute (IIIA) Consejo Superior de Investigaciones Científicas (CSIC) Barcelona, Catalonia, Spain · Department of Computer Science University of Oxford Oxford, UK · Institut de Robòtica i Informàtica Industrial (IRI-CSIC-UPC) Barcelona, Catalonia, Spain
Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
May 28, 2026Haoyang Liu, Jie Wang, Boxuan Niu +8Natural Language to Optimization Modeling
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China · Noah’s Ark Lab, Huawei Technologies · Tianjin University
OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents
May 27, 2026Chenyu Zhou, Xinyun Lu, Jiangyue Zhao +3LLM Agent Evaluation AI Agent Benchmarks
Shanghai Jiao Tong University · Shanghai University of Finance and Economics · Stanford University
Generating Robust Portfolios of Optimization Models using Large Language Models
May 26, 2026Eleni Straitouri, Cheol Woo Kim, Milind TambeLarge Language Model-Guided Optimization Language Model Robustness
Max Planck Institute for Software Systems, Kaiserslautern, Germany · Harvard University, Cambridge, United States
Constraint acquisition needs better benchmarks
May 25, 2026Rafał Stachowiak, Tomasz P. PawlakBenchmark Construction Benchmark Design
Institute of Computing Science, Poznan Univeristy of Technology, Piotrowo 2, Poznań, Poland
VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification
May 24, 2026Sumaya Abdul Rahman, Seckhen Ariel Andrade Cuellar, Ghani Raissov +1Large Language Model-Guided Optimization Formal Verification
Texas A&M University at Qatar, Doha, Qatar · Carnegie Mellon University in Qatar, Doha, Qatar · Qatar Computing Research Institute, Doha, Qatar
Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization
May 20, 2026Zhiqi Gao, Albert Ge, Alexander Berenbeim +2LLM Grounding LLM Fine-Tuning
University of Wisconsin-Madison · United States Military Academy
TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization
May 20, 2026Isabella A. Stewart, Hongrui Chen, Faez AhmedTopology Optimization Multi-Agent System Optimization
Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge, MA, 02139 USA · Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA, 02139 USA
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
May 18, 2026Tinghan Ye, Arnaud Deza, Ved Mohan +2Large Language Model-Guided Optimization Natural Language to Optimization Modeling
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA · Department of Operations and Decision Systems, Université Laval, Québec, Canada
TriVAL: A Tri-Validation Framework for Faithful Automatic Optimization Modeling
May 12, 2026Ziyang Fang, JinXi Wang, Jinghui Zhong +1Large Language Model-Guided Optimization Combinatorial Optimization
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore 138632 · College of Computing and Data Science, Nanyang Technological University, Singapore 639798
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
MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling
May 12, 2026Zhong Li, Qi Huang, Yuxuan Zhu +6Multimodal Optimization Synthetic Benchmark Generation
Great Bay University · Leiden University · Rensselaer Polytechnic Institute +1
OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
May 12, 2026Zhong Li, Zihan Guo, Xiaohan Lu +5LLM Auditing Hallucination in Language Models
♣Great Bay University · ▲Peking University · ♡Jilin University +4
ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
May 4, 2026Guangrui XieMulti-Agent LLM Systems Large Language Model-Guided Optimization
Strategy-Aware Optimization Modeling with Reasoning LLMs
May 4, 2026Ruiqing Zhao, Fengzhi Li, Yuan Zuo +5RL for Code Generation LLM-Based Program Synthesis
School of Computer Science and Engineering, Beihang University, Beijing, China · JIUTIAN Research, Beijing, China · MIIT Key Laboratory of Data and Decision Intelligence, Beihang University, Beijing, China
EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
May 4, 2026Xiyuan Zhou, Ruixi Zou, Xinlei Wang +4Multi-Agent LLM Systems Software Engineering Agents
Nanyang Technological University · The Chinese University of Hong Kong, Shenzhen · INSAIT, Sofia University “St. Kliment Ohridski” +2
From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Apr 28, 2026Jianghao Lin, Zi Ling, Chenyu Zhou +4Multi-Agent Debate Multi-Agent LLMs
Antai College of Economics and Management, Shanghai Jiao Tong University · University of Chicago Booth School of Business · School of Economics and Management, Tsinghua University +1
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
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
Apr 18, 2026Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov +4LLM Post-Training RL Fine-Tuning
1X · University of Oxford
ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization
Feb 17, 2026Junbo Jacob Lian, Yujun Sun, Huiling Chen +3LLM-Based Program Synthesis LLM Reliability
Institute of Operations Research and Analytics, National University of Singapore · McCormick School of Engineering, Northwestern University · Wenzhou Buyi Pharmacy Chain Co., Ltd. +2
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
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
Jul 29, 2024Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg +3Mixed-Integer Linear Programming Large Language Model-Guided Optimization
a School of Management Science and Engineering, Stanford University, Stanford, California 94305 · b Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305
Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions
Date pendingShrenil Shaun Sharma, Avi SharmaLarge Language Model-Guided Optimization Combinatorial Optimization
Independent Researcher San Francisco, CA, USA · Department of Electrical Engineering and Computer Sciences University of California, Berkeley
QuantumQUBO Agent: Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
Date pendingNiloy Kumar Mondal, Md Rizwan ParvezMulti-Agent LLM Systems Large Language Model-Guided Optimization
Bangladesh University of Engineering and Technology · Qatar Computing Research Institute, HBKU, Doha, Qatar.

Jun 22, 2026, cs.AIJ/K move · Enter open · S save

YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate

Suyash Mishra

AI Researcher

Large Language Model-Guided Optimization Robust Optimization Regret Minimization Uncertainty Propagation Multi-Objective Optimization Natural Language to Optimization Modeling+1

Open paper AskPDF arXiv

Language models turn a worded situation into a numeric plan, and the dominant pipelines (NL4Opt, OptiMUS, ORLM, OR-LLM-Agent) commit to a single objective and point-valued coefficients, then solve once. For decisions that allocate real budget, effort, or clinical attention, that confidence is the failure mode: every objectified number is an assumption, and a plan optimal only if the guesses are exactly right is fragile -- mimicry of computation. YUKTI changes the target of autoformulation. Its representation is a typed-proposition graph whose relationships carry shape priors, coefficient uncertainty, and provenance. YUKTI routes each stage to an exact, nonlinear, or evolutionary solver; couples stages by a distributional Pareto hand-off; and introduces Assumption-Robust Pareto Frontiers (ARPF), resampling assumptions (including structural epsilon-contamination) to score how often each action survives (rho). We prove a bound making rho an exact factor of decision regret, add auditable traceability, and synthesize a benchmark-faithful data foundation when none exists (SRJANA). We validate three ways: under controlled misspecification the robust compromise cuts mean and tail regret by over 90% versus a naive point plan; on a regulated commercial decision we optimize inside a lawful action space and price the downside in euros; and on a real public dataset of 41,188 decisions an out-of-sample backtest beats the logged status quo by 34% and a naive point rule by 4% while reducing the optimizer's curse. The solvers are standard; we claim no benchmark-SOTA win. A head-to-head shows an LLM given the correct numbers, and single-objective optimization, both incur about 47x the held-out regret of YUKTI -- an LLM is a formulator, not a solver. Under long-range causal coupling, the forward hand-off becomes unsound, locating where it must become a backward-induction causal policy.

Momentum

Related topics

Latest papers 60