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Contrastive learning's entanglement problem may be solved: BayesNCL disentangles representations by selectively filtering task-irrelevant features, leading to a 142% boost in semantic consistency.
MLLMs struggle to plan coherent interleaved text-and-image generation, often missing opportunities for tool use, revealing a critical gap in their ability to unify factuality with creativity.
Legally mandated data deletion requests can be weaponized to stealthily cripple GNN performance, even if the model appears robust during initial training.
