ADClaw: Evidence-Grounded Active Inspection for Industrial Anomaly Detection and Reasoning

Industrial anomaly detection is essential for automated industrial inspection. Although multimodal large language models offer strong visual and semantic reasoning capabilities, existing methods often rely on a single observation or use tool outputs as transient feedback. This limits their ability to retain evidence across inspection steps and connect intermediate findings to final diagnoses. We introduce ADClaw, an evidence-grounded agent that formulates industrial anomaly diagnosis as active evidence construction and hypothesis verification. Its Structured Normality Prior integrates object–part semantics, explicit normality rules, and multi-scale normal visual memory to identify inspection targets and select suitable normal references. Guided by this prior, a persistent, programmable Inspection Workbench organizes heterogeneous tool outputs into a structured evidence state, allowing intermediate findings to be traced, composed, and reused throughout inspection. Hypothesis-Guided Evidence Reasoning assesses the sufficiency and consistency of this accumulated evidence to decide whether to acquire further observations, revise the current hypothesis, or finalize the diagnosis. These components work together in a hypothesize–inspect–reason–revise loop, turning discrete tool calls into a stateful inspection process in which each action addresses an explicit evidence requirement. Evaluations on 2D and 3D industrial anomaly detection benchmarks and MMAD demonstrate competitive detection and localization performance while enabling evidence-grounded defect understanding and traceable diagnostic explanations linking intermediate findings to final decisions.