Traceability Matters: Towards Faithful Optimization Modeling via Evidence-Chain Supervision

Operations Research (OR) modeling, which translates natural-language problem descriptions into solver-executable mathematical formulations, has traditionally relied on domain experts, making it labor-intensive and difficult to scale. Recent work uses large language models (LLMs) to automate this process, but primarily optimizes for code executability and final-answer accuracy, overlooking whether each modeling decision is faithful to the problem statement. Through a failure analysis of state-of-the-art frameworks, we find that nearly 90% of failures arise from such unfaithfulness: modeling elements bound to the wrong entities or conditions (*mapping misalignment*) and stated conditions missing from the formulation (*information omission*). Human experts guard against both errors by repeatedly checking every modeling decision against the source problem text and confirming that no stated condition is left out, an ability we term *traceability awareness*. Inspired by this observation, we propose **TAGO** (**T**raceability-**A**ware **G**uided **O**ptimization), a framework that trains reasoning LLMs to ground each modeling step in explicit textual evidence. TAGO first constructs StepTrace-OR, a dataset that links every modeling step to verbatim evidence from the problem statement, and then trains models with evidence-aligned supervised fine-tuning followed by curriculum reinforcement learning, using a composite reward that targets binding validity and key-evidence recovery. Across four backbones and five OR benchmarks, TAGO consistently outperforms state-of-the-art baselines on both Pass@1 and Pass@8, improving average Pass@1 by up to **8.14** points over the strongest baseline. Further analyses also show that TAGO substantially reduces misalignment and omission errors and produces more credible evidence chains. Our code is available at [https://anonymous.4open.science/r/TAGO_code-245D/](https://anonymous.4open.science/r/TAGO_code-245D/).