Researchers have investigated test-time scaling (TTS) techniques to improve the performance of small open vision-language models (VLMs) on multilingual visual question-answering tasks. Their study on the EXAMS-V benchmark, using models like Qwen2.5-VL-7B-Instruct and Qwen3.5-4B, found that prompt formatting and decoding budget were more critical than complex search or verification methods. By optimizing these factors, their best configuration achieved 84.1% accuracy on the ImageCLEF 2026 test split, securing the top rank on the Visual MCQ leaderboard. AI
IMPACT Optimizing prompt formatting and decoding budgets can significantly enhance small VLMs' reasoning capabilities on complex multilingual tasks.
RANK_REASON The cluster contains an academic paper detailing research findings on improving VLM performance.
- arXiv
- Hugging Face
- ImageCLEF 2026
- Qwen2.5-VL-7B-Instruct
- Qwen3.5-4B
- Spiros Baxevanakis
- alphaXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- ScienceCast
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