Title: Small Drafts, Big Verdict: Information-Intensive Visual Reasoning via Speculation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: multimodal reasoning, visual question answering, vision-language model, information-intensive images, speculative decoding

Abstract: Large Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet they struggle with 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 both 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 partially accurate reasoning paths, SV achieves both error correction and cost-efficiency compared to large proprietary models or training pipelines.

TL;DR: Speculative Verdict is a training-free framework that combines lightweight draft experts with a strong verdict model to achieve accurate and efficient reasoning over information-intensive images.