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ResNets and transformers are equally powerful in altering linking numbers, challenging conventional views on network expressivity.
Embedding magnitudes, often ignored in contrastive models, actually encode critical semantic information that can enhance retrieval tasks.
DeepSeek LLMs are surprisingly vulnerable to prompt injection attacks that combine semantic and character-level mutations, outperforming single-strategy attacks by 12.5% in misuse success rate.
