NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

2026-01-29

AI Summary

This work addresses the challenge of automatically translating natural language descriptions of decision problems into executable optimization models by proposing an execution-aware modeling framework based on Autonomous Coding Agents (ACA). The approach ensures code executability through a sandboxed environment and introduces novel coordination mechanisms—including asymmetric verification loops, external memory reuse, minimum Bayes risk decoding, and self-consistency—to significantly enhance modeling robustness and accuracy. The system supports both interactive and fully autonomous operation, achieving state-of-the-art performance across nine standard optimization benchmarks and substantially outperforming existing methods on multiple datasets. These results validate the effectiveness of an architecture that treats ACA as a first-class abstraction for automated optimization modeling.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents. Such methods are often brittle, complex and frequently generating syntactically invalid or non-executable code. NEMO instead centers on remote interaction with autonomous coding agents (ACAs), treated as a first-class abstraction analogous to API-based interaction with LLMs. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications. Because ACAs execute within sandboxed environments, code produced by NEMO is executable by construction, allowing automated validation and repair. Building on this, we introduce novel coordination patterns with and across ACAs, including asymmetric validation loops between independently generated optimizer and simulator implementations (serving as a high-level validation mechanism), external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. We evaluate NEMO on nine established optimization benchmarks. As depicted in Figure 1, it achieves state-of-the-art performance on the majority of tasks, with substantial margins on several datasets, demonstrating the power of execution-aware agentic architectures for automated optimization modeling.

Problem

• Natural-language to optimization
• Executable code generation
• Mathematical optimization
• Autonomous coding agents
• Decision problem modeling

Innovation

• Autonomous Coding Agents
• Executable Optimization
• Execution-Aware Modeling
• Asymmetric Validation
• Minimum Bayes Risk Decoding