Ten Visual Question Answering Failure Cases for Qwen3.5-Base Model

This dataset documents 10 specific failure cases where the Qwen3.5-Base-0.8B vision-language model produced incorrect answers on visual question answering tasks. The examples were sampled from the SimpleVQA benchmark and include the original image, question, expected answer, and the model's actual output.

Use Cases

• Analyze the model's incorrect outputs to identify systematic blind spots in visual reasoning.
• Use the provided images and expected answers to benchmark other VQA models against these specific failure cases.
• Examine the relationship between the questions and the model's actual outputs to understand error patterns.

Strengths

• Focuses on 10 curated failure cases, providing a targeted analysis set.
• Includes multiple data modalities: images, questions, expected answers, and model outputs.
• Examples are sourced from the established SimpleVQA benchmark.

Limitations

• Extremely small sample size of only 10 examples, insufficient for statistical analysis.
• Contains only failure cases, lacking a balanced view with correct model responses for comparison.
• Selection criteria for the failures may introduce bias, not representing the model's overall error distribution.

Provenance

Source

Sampled from the m-a-p/SimpleVQA benchmark.

Collection Method

Selected failure cases of the Qwen3.5-Base-0.8B model.

Time Range

null

Freshness

Last updated March 2026.

Geography

null