A new benchmark, Game2Real-Bench, tests whether visual reasoning that vision-language models (VLMs) demonstrate on toy games transfers to real-world visual reasoning, according to the benchmark's authors. The work argues that existing VLM evaluations rely mainly on real-world data, which preserves natural complexity, or on procedurally generated tasks, and it frames toy games as a testbed for visual evidence, spatial structure, constraints and state changes. The benchmark's central question is whether toy-game performance reveals and transfers to real-world visual reasoning.
Visual reasoning is an important capability for vision-language models (VLMs) to understand visual evidence, spatial structure, constraints, and state changes. Existing evaluations mainly rely on real-world data or procedurally generated tasks. Real-world data preserve natural complexity, while proc
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