Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

TL;DR

Laser introduces a dynamic alignment approach for visual reasoning that maintains global features before local details, achieving state-of-the-art results efficiently across multiple benchmarks.

Contribution

The paper presents Laser, a novel latent reasoning paradigm with Dynamic Windowed Alignment Learning that improves interpretability and efficiency in visual deduction tasks.

Findings

Laser surpasses Monet by 5.03% on average across 6 benchmarks.

Reduces inference tokens by over 97%, enhancing efficiency.

Demonstrates robust out-of-distribution generalization.

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.