Enhancing Zero-Shot Visual Reasoning with Qwen2-VL on CLEVR

Abstract

Vision-language models now perform well on many image-text tasks, but they are still less reliable when a question requires multi-step reasoning. This paper examines that gap through a small but controlled reproduction experiment using Qwen2-VL on CLEVR. The study begins with environment verification and a small VQA v2 smoke test, and then moves to fixed CLEVR validation slices scored with exact-match accuracy, answer normalization, and simple error categories. On the first 100-sample slice, the zero-shot baseline reaches 96.0% accuracy. To test whether simple inference-time changes help, the study compares three prompt styles on the same slice. All three produce the same 96.0% result. A decoding ablation on a larger 200-sample slice shows that greedy decoding remains at 95.0% across response-length limits of 16, 32, and 64, while a sampled setting falls slightly to 94.5%. A final 500-sample confirmation run with the best simple setting, namely a constrained short-answer prompt and greedy decoding with a 16-token limit, reaches 94.4%. Overall, the results show that the baseline is fairly stable under lightweight prompt and decoding changes. However, these low-cost adjustments do not solve the model's main weakness, which still appears in spatial and compositional reasoning.