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Success in long-horizon tasks hinges more on an agent's iterative persistence than on the quality of its initial solution.
A simple resampling strategy closes the "Thinking-Acting Gap" in agentic VLMs, enabling smaller models to outperform larger ones on multimodal reasoning tasks.
