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Realistic anomalies can be generated for unseen products without needing any target-product samples, revolutionizing how we approach anomaly detection in industrial settings.
MLLMs can achieve 10% gains on multimodal reasoning benchmarks by using ground-truth anchored data curation and scaffold-stripping to avoid cognitive drift during self-evolution.
Forget static datasets – RL-based co-training unlocks +20% real-world VLA performance by interactively leveraging simulation while preserving real-world capabilities.
