Logical anomalies in industrial inspection are rule violations: an object count is wrong, an item is misplaced, a component type does not match, or a required relation is broken. Existing anomaly understanding systems usually stop at diagnosis, answering whether an image is anomalous, where the anomaly is, or why it violates a rule. Reworkable inspection workflows require the next question: What should be fixed? We introduce an industry-oriented benchmark and training pipeline for rule-grounded corrective actions. The task asks a model to produce an operator-facing action that supports one of three downstream decisions: no action, rework, or reject/manual review. This setting exposes a Diagnosis-Action Gap: identifying an abnormal object does not imply knowing the valid, minimal action that restores normality. We construct from MVTec LOCO and from VID-AD, adding correction-oriented annotations that connect normal rules, visual evidence, repairability, corrective actions, and verification targets. The datasets support multiple-choice correction selection, structured JSON action prediction, and open-ended correction generation. We further train ReworkVLM, a Qwen3-VL-based model optimized with , which combines semantic correctness, trace-answer alignment, and minimal-edit rewards. Experiments on both benchmarks show that correction-oriented evaluation reveals failures hidden by diagnosis alone, including wrong action direction, unnecessary repair, and non-minimal edits, while ReworkVLM improves structured corrective success and open-ended correction quality over general and anomaly-specific VLM baselines.