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Harbin Institute of Technology
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LLM search agents are often just verifying pre-existing knowledge, not truly searching, and a new benchmark shows their performance plummets when up-to-date information is required.
Speech, not just images, can unlock significant gains in multilingual machine translation, especially when paired with a self-evolution mechanism for MLLMs.
Unlock SOTA performance in long-horizon search tasks with REDSearcher, a framework that slashes the cost of training by strategically synthesizing complex tasks and boosting core LLM capabilities *before* reinforcement learning.
