The Unwritten Benchmark Dataset Summary The Unwritten Benchmark is a multimodal benchmark for acousto kinematic word inference : given only the sound of a pen scratching on paper and/or the motion of a hand writing, models must infer the underlying word, even though no visible ink trace is present. The dataset was introduced in the paper: The Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning Garima Arya Yadav, Nilay Yilmaz, Yezhou Yang Arizona State University Paper: (https://riri y.github.io/unwritten benchmark/) (CVPR 2026 Findings) The benchmark is designed to probe a capability that current multimodal systems still struggle with: recovering a symbolic outcome from the physical process that created it. Instead of recognizing explicit text or visible handwriting, models must reason from subtle motion and audio cues alone. Supported Tasks and Leaderboards This dataset supports research on: multimodal reasoning audio visual inference handwriting understanding without visible text temporal perception and micro kinematic reasoning cross modal fusion under causal coupling The main evaluation task is: Acousto kinematic word inference…
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