Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal
  Reasoning in MLLMs
Authors: name: Huanyu Zhang, name: Wenshan Wu, name: Chengzu Li, name: Ning Shang, name: Yan Xia, name: Yangyu Huang, name: Yifan Zhang, name: Li Dong, name: Zhang Zhang, name: Liang Wang, name: Tieniu Tan, name: Furu Wei
Organization: Microsoft

While Multimodal Large Language Models (MLLMs) excel at visual understanding,
they often struggle in complex scenarios that require visual planning and
imagination. Inspired by how humans use sketching as a form of visual thinking
to develop and communicate ideas, we introduce Latent Sketchpad, a framework
that equips MLLMs with an internal visual scratchpad. The internal visual
representations of MLLMs have traditionally been confined to perceptual
understanding. We repurpose them to support generative visual thought without
compromising reasoning ability. Building on frontier MLLMs, our approach
integrates visual generation directly into their native autoregressive
reasoning process. It allows the model to interleave textual reasoning with the
generation of visual latents. These latents guide the internal thought process
and can be translated into sketch images for interpretability. To realize this,
we introduce two components: a Context-Aware Vision Head autoregressively
produces visual representations, and a pretrained Sketch Decoder renders these
into human-interpretable images. We evaluate the framework on our new dataset
MazePlanning. Experiments across various MLLMs show that Latent Sketchpad
delivers comparable or even superior reasoning performance to their backbone.
It further generalizes across distinct frontier MLLMs, including Gemma3 and
Qwen2.5-VL. By extending model's textual reasoning to visual thinking, our
framework opens new opportunities for richer human-computer interaction and
broader applications. More details and resources are available on our project
page: https://latent-sketchpad.github.io/.