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Small VLMs boosted by test-time scaling on multilingual visual tasks · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

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.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Small VLMs boosted by test-time scaling on multilingual visual tasks · 2 sources tracked

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The cluster contains an academic paper detailing research findings on improving VLM performance.
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2 independent sources
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paper, model release
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Spiros Baxevanakis, Peng-Jian Yang ·

    Test-Time Scaling for Small VLMs on Multilingual Visual MCQ

    arXiv:2607.09438v1 Announce Type: cross Abstract: Test-time scaling (TTS) reliably improves reasoning in large language models, but whether it transfers to small open vision-language models remains unclear. We examine this on EXAMS-V, a multilingual visual multiple-choice benchma…

  2. arXiv cs.AI TIER_1 English(EN) · Peng-Jian Yang ·

    Test-Time Scaling for Small VLMs on Multilingual Visual MCQ

    Test-time scaling (TTS) reliably improves reasoning in large language models, but whether it transfers to small open vision-language models remains unclear. We examine this on EXAMS-V, a multilingual visual multiple-choice benchmark, comparing self-consistency, describe-then-reas…

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