Visual Reasoning

Visual reasoning asks how AI can move from recognizing objects to using image and video evidence to infer relations, transformations, rules, causes, and actions. Approaches include object-centric and scene-graph representations, symbolic and neuro-symbolic programs, explicit visual chains of thought, latent reasoning, iterative image inspection, and reinforcement-learned tool use. Benchmarks cover abstract puzzles, geometry, charts and diagrams, multi-image and video tasks, physical prediction, and fine-grained evidence grounding, often testing whether answers genuinely rely on visual input. Models benefit from structured data and additional inference-time computation, but remain brittle on long reasoning chains, viewpoint changes, and grounding; a key open question is whether failures arise from perception, representation, or reasoning itself.