視覺推理

It combines perception with task-specific reasoning. Correctly naming an object does not establish that a system can count, compare, or infer relationships reliably.

重點摘要

• Separate perception from inference.
• Test controlled and realistic scenes.
• Check the source values behind explanations.

Break the task into what must be perceived and what must be inferred. A chart question may require reading an axis, identifying a series, and comparing values. If the axis is misread, the final arithmetic can be correct while the answer is wrong. Use controlled examples to test specific relationships, then evaluate realistic images. A diagnostic dataset can isolate skills such as counting or spatial comparison, but results on simplified scenes do not automatically transfer to cluttered photographs, diagrams, or scanned documents. Check sensitivity to image resolution, cropping, and wording. Small text, overlapping objects, and ambiguous references can change the evidence available to the model. Ask for uncertainty when the image cannot support the requested conclusion. Verify answers against the actual visual evidence. A plausible explanation may rely on common expectations rather than what the image shows. For consequential use, preserve the source and any extracted values so a reviewer can reconstruct the comparison independently.

1. Imagine a chart whose vertical axis starts at 90 rather than zero, with bars at 95 and 100.
2. The visible bar heights can make the difference look dramatic, but the numerical difference is 5 units.
3. Read the labels and scale before comparing the values, and distinguish the numerical claim from the visual impression.

This constructed chart exercise tests evidence extraction and interpretation together.