AI 的视觉短板，靠「自己出题考自己」补上了
大模型看单张图时，能认出「有个杯子」，但一问「杯子和桌角哪个更靠左」就卡壳——它缺的不是看清细节，而是把分散的视觉证据连起来的能力。研究者发现，与其花大价钱人工标注这种推理数据，不如让 AI 自己出题：把一张图里相关的区域放大，生成需要比较、串联、推理的问题，再用多个模型交叉验证答案质量，不合格的题直接扔掉。用这套自动生成的题库训练后，模型在空间感知、长上下文理解、智能体任务上全面变强，而且没针对任何下游任务定制。它不是你明天能用上的功能，但它指出了一个方向：AI 的推理短板，可能不需要更聪明的架构，而是需要更聪明的训练数据。
📄 原文摘要(英文)
Multimodal perception forms the foundation of multimodal intelligence. In particular, spatial perception and multimodal long-context understanding are two pivotal tasks that underpin embodied AI and multimodal agents. However, synthesizing targeted, high-quality training data for these tasks remains difficult and costly. Our key insight is that these tasks share an underlying capability, which we call connecting evidence: identifying relevant visual facts, binding them to the correct entities, recovering their relations, and composing them into an answer. Crucially, targeted training data are far easier to synthesize for this capability than for these tasks themselves. To strengthen this capability, we propose EvidenceConnect, an easy-to-implement data synthesis framework that uses zoomed views of related regions within a single image to generate questions requiring comparison, aggregation, or chaining of visual evidence. To ensure quality, a reference answer is retained only when independent VLMs agree on it, and region-ablation tests discard questions whose answer survives the removal of selected regions. We then train base VLMs on the resulting data with RLVR, obtaining Zooming without Zooming v2 (ZwZ2). Although the synthesized data are not tailored to any downstream benchmark, ZwZ2 improves fine-grained and spatial perception, with further gains on embodied spatial reasoning, multimodal long-context understanding, multimodal agent tasks, and general visual reasoning. These results establish EvidenceConnect as an effective framework for supervising evidence connection and suggest that strengthening this shared capability leads to more generalizable multimodal perception.