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This paper creates a benchmark for evaluating AI systems in real-world customer delivery scenarios, where agents must clarify ambiguous requirements, work in live systems, and uncover unstated constraints. Practitioners might care because it highlights the challenges of delivering effective AI systems in complex, real-world environments.
This paper evaluates the ability of Deep Search agents to conduct end-to-end research in real-world open Web environments, where they must autonomously search for, access, and integrate relevant evidence. Practitioners may care about this research to develop agents that can effectively search and retrieve information in complex, real-world settings.
This paper introduces a method to generate high-quality long-horizon trajectories for terminal agents, allowing them to complete complex tasks in a realistic and autonomous way. Practitioners in AI/ML research and development can benefit from this approach to improve the performance and reliability of autonomous systems.
This paper introduces TurboVLA, a new vision-language-action model that reduces computation and memory overhead by directly exchanging information between visual observations and language instructions, allowing for faster and more efficient robotic manipulation. Practitioners might care about this approach for building more efficient and effective VLA models.