OR-ARK: AN AUTONOMOUS MODELING LARGE LANGUAGE MODEL DRIVEN BY ACADEMIC AND RE- FLECTIVE KNOWLEDGE

Large language models (LLMs) offer a promising approach to automating optimization modeling, yet their performance on complex problems remains constrained by the scarcity and limited diversity of high-quality training data. Existing approaches largely rely on predefined seed problems, synthetic data, or proprietary industrial data, leaving the rich optimization knowledge in academic literature underexplored. We introduce OR-ARK, a fully public-data-driven framework that extracts optimization problems, mathematical formulations, and reasoning processes from over 60,000 publicly available academic operations research papers and validates the resulting instances through executable solver code. To improve reasoning reliability, we further construct error–reflection–correction (ERC) trajectories that explicitly train the model to identify and correct plausible intermediate modeling errors. Using the resulting data, we progressively train an 8B model through supervised fine-tuning and reinforcement learning. By internalizing error correction into the reasoning process, OR-ARK generates executable optimization code in a single pass, avoiding the iterative solver-feedback-based correction required by many agent-based approaches. Experiments across five optimization-modeling benchmarks show that OR-ARK achieves state-of-the-art performance among comparable-scale models on challenging benchmarks such as IndustryOR while remaining competitive on the others, highlighting the effectiveness of academic optimization knowledge and explicit self-correction for complex optimization modeling.