v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning

Understand

When thinking with images, humans rarely rely on a single glance: they revisit visual evidence while reasoning.

• In contrast, most Multimodal Language Models encode an image once to key-value cache and then reason purely in text, making it hard to re-ground intermediate steps.
• We empirically confirm this: as reasoning chains lengthen, models progressively lose focus on relevant regions.
• We introduce v1, a lightweight extension for active visual referencing via point-and-copy: the model selects relevant image patches and copies their embeddings back into the reasoning stream.