This paper introduces a benchmark for schema-guided document extraction, which is a crucial task in enterprise workflows, and evaluates various models' performance on this task, including their accuracy, grounding, and cost-effectiveness.
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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.