EGVLR: Evidence-Grounded Vision–Language Reinforcement for Anomaly Reasoning

005 Multimodal large language models (MLLMs) have recently 005006 shown promise for industrial anomaly understanding, where models are 006007 expected not only to determine whether an object is defective, but also to 007008 localize visual evidence and explain defect-related decisions. However, ex- 008009 isting MLLMs often rely on language priors or shortcut reasoning when 009010 facing fine-grained industrial defects, resulting in inconsistent outputs 010011 such as correct answers with invalid locations, hallucinated defect boxes 011012 on normal samples, or rationales that contradict the final prediction. To 012013 address these issues, we propose EGVLR, an evidence-grounded vision– 013014 language reinforcement framework for anomaly reasoning. EGVLR fol- 014015 lows a unified Evidence-Driven Diagnostic Protocol (EDDP), where each 015016 response explicitly contains visual evidence, diagnostic logic, spatial loca- 016017 tion, and the final answer. The framework consists of four stages: Progres- 017018 sive Visual-Evidential Fine-Tuning (PVE-FT), Knowledge-Grounded In- 018019 struction Tuning (KG-IT), Geometry-Semantic Decoupled Preference 019020 Optimization (GS-DPO), and Box-Guided Segmentation Rendering (BGSR). 020021 PVE-FT first aligns the model with localized visual evidence through 021022 synthetic anomalies, 3 × 3 grid grounding, local verification decoys, and 022023 null-hypothesis calibration. KG-IT then internalizes industrial-domain 023024 QA behavior while preserving the same output schema. GS-DPO fur- 024025 ther optimizes answer correctness, geometric localization, BGE-based 025026 rationale semantics, and answer–location–rationale consistency. Finally, 026027 BGSR converts predicted bounding boxes into dense masks using an off- 027028 the-shelf segmentation backend. Experiments on MMAD demonstrate 028029 that EGVLR improves evidence-grounded industrial anomaly reasoning, 029030 especially on defect localization, reasoning-dependent QA, and spatial 030031 false-positive control. 031