MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.
IEEE Transactions on Information theory37(1), 145–151 (2002)
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Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.
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Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.