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Review regressions before expanding the test." ], "measure": "Held-out quality, peak memory, training time, and full compute cost to the target quality.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.38811", "title": "DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation", "summary": "The method replaces full fine-tuning or prompt-based use of a large Segment Anything model with a frozen ViT-B backbone carrying per-class Conv-LoRA expert banks and separate mask decoders, trained on synthetic data with only 4.4% of parameters updated. 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