Verifier-Native Control and Trajectory Evaluation for Operations Research Repair Agents

Operations Research (OR) repair is iterative: a candidate edit is solved, checked, diagnosed, and revised. We study how LLM agents use this feedback while preserving operational validity. **Repair-Sufficient State (RSS)** compiles solver and domain-checker observations into typed failure state; **Counterfactual Verifier Loop (CVL)** organizes probes and repairs around verifier transitions; and **OR-INSPECT-BENCH** evaluates fixed-trace diagnosis and live repair. Across 19 directly measured model snapshots, the full pipeline improves strict recovery over one-shot prompting. Matched controls show that RSS improves on raw multi-turn feedback across three anchors. In a fresh same-window Qwen comparison, adding reference-free output normalization to RSS raises strict all-error recovery from 30.6% to 42.8%; the full CVL-r2 configuration reaches 45.8%, a further 3.1-point estimate. A Qwen factorial also finds that canonical-emission correction raises strict recovery by 14.4 and 20.6 points with probes enabled and disabled. Across six paired model anchors, additional control gains vary by model. OR-INSPECT-BENCH combines fixed-trace diagnosis, live repair, and composition holdouts. Together, the results quantify contributions from typed verifier state and output handling, and characterize when added control improves recovery.