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The article proposes AdaThinking-E, a reinforcement learning framework that uses one-token entropy regulation to enable adaptive thinking in multimodal large language models, improving accuracy on complex tasks and efficiency on simple ones.
A practical guide explaining how Claude Opus 4.7 differs from 4.6, covering the new xhigh effort level, adaptive thinking replacing fixed token budgets, and a 1M context window, with recommendations on how to adjust prompting and delegation strategies to avoid inflated token costs.