aiqbench analysis
NEMO advances the field by treating autonomous coding agents as first-class abstractions with sandboxed execution guarantees, effectively solving common brittleness issues in generating executable optimization code. By integrating asymmetric validation loops and minimum Bayes risk decoding, this system achieves state-of-the-art performance on complex decision problems without relying solely on brittle specialized LLMs. The work demonstrates a critical shift toward robust, execution-aware agentic architectures that are essential for reliable automated mathematical modeling.
NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents
arXiv:2601.21372v3 Announce Type: replace Abstract: We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous coding agents (ACAs). Existing approaches rely on specialized large language models (LLMs) or bespoke task-specific agents that are often brittle and frequently generate syntactically invalid or non-executable code. NEMO instead treats ACAs as a first-class abstraction analogous to API-based interaction with LLMs; their sandboxed execution guarantees code is executable by construction and supports automated validation and repair. We introduce novel coordination patterns including asymmetric validation loops between independently generated optimizer and simulator implementations, external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. Across nine established optimization benchmarks, NEMO 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.
Abstract:We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations using autonomous coding agents (ACAs). Existing approaches rely on specialized large language models (LLMs) or bespoke task-specific agents that are often brittle and frequently generate syntactically invalid or non-executable code. NEMO instead treats ACAs as a first-class abstraction analogous to API-based interaction with LLMs; their sandboxed execution guarantees code is executable by construction and supports automated validation and repair. We introduce novel coordination patterns including asymmetric validation loops between independently generated optimizer and simulator implementations, external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. Across nine established optimization benchmarks, NEMO 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.
Artificial Intelligence (cs.AI)
arXiv:2601.21372 [cs.AI]
(or arXiv:2601.21372v3 [cs.AI] for this version)
Full text under the publisher's open licence · 151 words
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