VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

arXiv:2607.20474 · cs.AI · Submitted 2026-05-24 · Read on arXiv

cs.AI

Submitted: 2026-05-24

Updated: 2026-09-22

Comments: Accepted and published at ICML 2026. Code available at https://github.com/suabar/VeriSimple

Code: https://github.com/suabar/VeriSimple

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Verification — 135× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Linear Programming — 8× in this paper
• Objective Function — 7× in this paper · explained in 2 episodes
• Verifier — 6× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Commentary — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Agentic System — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Mixed-Integer Linear Programming — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Grounding — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• LLM-ARC: Enhancing LLMs with an Automated Reasoning Critic
• ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling
• Validating LLM-Generated Programs with Metamorphic Prompt Testing

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