CVAICLOct 23, 2025

Small Drafts, Big Verdict: Information-Intensive Visual Reasoning via Speculation

arXiv:2510.20812v13 citationsh-index: 1Has Code
Originality Incremental advance
AI Analysis

This addresses a bottleneck in multimodal AI for tasks like visual question answering on complex images, offering an incremental improvement in efficiency and accuracy.

The paper tackles the problem of large vision-language models struggling with information-intensive images that combine dense text and graphics, proposing the Speculative Verdict framework to improve reasoning by using lightweight draft experts and a strong verdict model, achieving consistent gains on benchmarks like InfographicVQA and ChartQAPro.

Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle when reasoning over information-intensive images that densely interleave textual annotations with fine-grained graphical elements. The main challenges lie in precisely localizing critical cues in dense layouts and multi-hop reasoning to integrate dispersed evidence. We propose Speculative Verdict (SV), a training-free framework inspired by speculative decoding that combines multiple lightweight draft experts with a large verdict model. In the draft stage, small VLMs act as draft experts to generate reasoning paths that provide diverse localization candidates; in the verdict stage, a strong VLM synthesizes these paths to produce the final answer, minimizing computational cost while recovering correct answers. To further improve efficiency and accuracy, SV introduces a consensus expert selection mechanism that forwards only high-agreement reasoning paths to the verdict. Empirically, SV achieves consistent gains on challenging information-intensive and high-resolution visual question answering benchmarks, including InfographicVQA, ChartMuseum, ChartQAPro, and HR-Bench 4K. By synthesizing correct insights from multiple partially accurate reasoning paths, SV achieves both error correction and cost-efficiency compared to large proprietary models or training pipelines. Code is available at https://github.com/Tinaliu0123/speculative-verdict

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