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CUA-Sandbox is introduced, which separates private state capsules from shared runtimes through state-scoped execution and transactional lifecycle operations, including resets and branches, while retaining the original software interfaces and task evaluators.
Socratic-Geo is proposed, a fully autonomous framework that dynamically couples data synthesis with model learning through multi-agent interaction, and establishes new state-of-the-art for open-source models, sur-passing strong baselines.
Experiments show that SearchEyes achieves state-of-the-art performance among open-source multimodal search agents, with SearchEyes-27B improving over the strongest open-source baseline by 6.2 points on average.
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