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NEMO system uses autonomous agents for optimization modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed NEMO, a system that uses autonomous coding agents to translate natural language descriptions of decision problems into executable mathematical optimization models. Unlike previous methods that often produced invalid code, NEMO's agents operate in a sandboxed environment, ensuring code executability and enabling automated validation and repair. The system incorporates novel coordination patterns, including asymmetric validation loops and external memory for experience reuse, achieving state-of-the-art performance on nine optimization benchmarks. AI

IMPACT Autonomous agents can now translate natural language into executable optimization models, potentially streamlining complex problem-solving.

RANK_REASON The cluster contains an academic paper detailing a new system and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

NEMO system uses autonomous agents for optimization modeling

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The cluster contains an academic paper detailing a new system and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yang Song, Anoushka Vyas, Zirui Wei, Sina Khoshfetrat Pakazad, Henrik Ohlsson, Graham Neubig ·

    NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents

    arXiv:2601.21372v2 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 spe…

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