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EvoIn is an agent fine-tuning framework that bridges evolution and internalization, and consistently enables agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain.
Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
AgentGym2 is presented, a new evaluation framework with task instances grounded in real-world end-to-end working demands that measures agents'ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.
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