Forest Before Trees: Latent Superposition for Efficient Visual Reasoning
Yubo Wang, Juntian Zhang, Yichen Wu, Yankai Lin, Nils Lukas, Yuhan Liu
2026-01-13
Summary
This paper introduces a new approach called Laser to help AI models that can 'see' and 'think' – specifically, large vision-language models – reason more effectively about images. It focuses on improving how these models process visual information and make deductions.
What's the problem?
Current AI models use a method called 'Chain-of-Thought' where they explain their reasoning step-by-step with words. However, this loses important details from the images because images are converted into text, which can't capture everything. Other attempts to solve this by directly processing the image information often get stuck too early in the reasoning process, focusing on small details before understanding the bigger picture.
What's the solution?
Laser tackles this by using a technique called 'Dynamic Windowed Alignment Learning'. Instead of immediately pinpointing an answer, Laser keeps a range of possibilities in mind while looking at the image. It's like looking at the forest before focusing on individual trees. This allows the model to maintain a broader understanding of the image and then narrow down to the important details. The model also keeps track of its reasoning process in a way that humans can understand, and it's designed to learn effectively without getting stuck.
Why it matters?
Laser is a significant step forward because it achieves better results than existing methods on several visual reasoning tasks, improving accuracy by a noticeable margin. Importantly, it does this much faster and more efficiently, using far fewer steps to reach a conclusion. This means it can handle complex images and reasoning problems more effectively and could be applied to real-world scenarios where quick and accurate visual understanding is crucial.
Abstract
While Chain-of-Thought empowers Large Vision-Language Models with multi-step reasoning, explicit textual rationales suffer from an information bandwidth bottleneck, where continuous visual details are discarded during discrete tokenization. Recent latent reasoning methods attempt to address this challenge, but often fall prey to premature semantic collapse due to rigid autoregressive objectives. In this paper, we propose Laser, a novel paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning (DWAL). Instead of forcing a point-wise prediction, Laser aligns the latent state with a dynamic validity window of future semantics. This mechanism enforces a "Forest-before-Trees" cognitive hierarchy, enabling the model to maintain a probabilistic superposition of global features before narrowing down to local details. Crucially, Laser maintains interpretability via decodable trajectories while stabilizing unconstrained learning via Self-Refined Superposition. Extensive experiments on 6 benchmarks demonstrate that Laser achieves state-of-the-art performance among latent reasoning methods, surpassing the strong baseline Monet by 5.03% on average. Notably, it achieves these gains with extreme efficiency, reducing inference tokens by more than 97%, while demonstrating robust generalization to out-of-distribution domains.