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MLLMs can be misled by focusing attacks on phase information, revealing a critical vulnerability that traditional adversarial methods overlook.
Even the best LLMs struggle with Olympiad-level combinatorics, achieving only 65.4% on a benchmark designed to expose their reasoning limitations.
Draft-OPD accelerates inference by over 5x while improving speculative decoding accuracy, transforming how draft models learn from target feedback.
