IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection

Small defects in unfamiliar product categories can be difficult to distinguish from benign appearance variations in a global view. Additional reasoning over the same view may leave this ambiguity unresolved. We introduce IndusAgent, a Qwen3-VL-8B inspection policy trained through supervised fine-tuning and tool-augmented reinforcement learning for cross-category industrial anomaly detection. The policy selects among six tools for cropping, enhancement, measurement, within-image comparison, texture analysis, and external visual opinions from Qwen3-VL-Max, then integrates returned observations into a structured diagnosis. We construct Indus-CoT with 3,000 trajectories from 26 Real-IAD categories, generated with actual tool feedback and screened against annotations after teacher generation. GRPO optimizes an accuracy-gated objective that conditions localization, type, and tool rewards, including call costs, on final diagnostic correctness while retaining an independent format reward. Under the common protocol, IndusAgent exceeds direct Max inference in mean balanced accuracy across five benchmarks (83.5% versus 79.4%). Across three training seeds, the gated policy achieves higher five-benchmark mean balanced accuracy than the weight-matched ungated control (83.5% versus 80.5%). Evaluation uses disjoint product categories and a fixed defect vocabulary. Inference requires no paired normal exemplar but can access the external prior API.