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.

翻译：尽管思维链技术使大型视觉-语言模型具备了多步推理能力，但显式文本推理路径受限于信息带宽瓶颈——连续视觉细节在离散标记化过程中被丢弃。近期潜在推理方法试图解决该挑战，却常因僵化的自回归目标而陷入语义过早坍缩。本文提出Laser范式，通过动态窗口对齐学习（DWAL）重构视觉演绎过程：不同于强制逐点预测，Laser将潜在状态与未来语义的动态有效性窗口对齐。该机制构建了"先见森林后见树木"的认知层级，使模型在聚焦局部细节前维持全局特征的即时机率叠加态。关键的是，Laser通过可解码轨迹保持可解释性，同时借助自精炼叠加机制稳定无约束学习。在6个基准测试上的实验表明，Laser在潜在推理方法中达到最优性能，平均超越强基线方法Monet达5.03%。值得注意的是，它在实现突破性效率提升（推理令牌减少97%以上）的同时，展现出对分布外领域的稳健泛化能力。