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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.
Memory and generalization in neural networks can be disentangled and quantified, revealing that optimal generalization is achievable with minimal memorization through targeted interventions.
GRAIN boosts LLM accuracy by over 16% while cutting latency, revolutionizing how we handle real-world graph reasoning challenges.
