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• Existing LMM-based segmentation approaches face limitations in precisely localizing anomalies due to weak alignment between high-level semantic embeddings and pixel-level spatial features.limitation 70%
• AG-VAS introduces three learnable semantic anchor tokens: [SEG], [NOR], and [ANO] to establish a unified anchor-guided segmentation paradigm.method 70%
• The [SEG] anchor token translates abstract anomaly semantics into explicit, spatially grounded visual entities.method 70%
• The Semantic-Pixel Alignment Module (SPAM) aligns language-level semantic embeddings with high-resolution visual features.technical 70%
• The Anchor-Guided Mask Decoder (AGMD) performs anchor-conditioned mask prediction for precise anomaly localization.technical 70%
• The Anomaly-Instruct20K dataset organizes anomaly knowledge into structured descriptions of appearance, shape, and spatial attributes.method 70%
• AG-VAS achieves consistent state-of-the-art performance in the zero-shot setting on six industrial and medical benchmarks.result 70%

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