Forest Before Trees: Latent Superposition for Efficient Visual Reasoning
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.
69663b7f87c71000b5a910a3
Yubo Wang
claimed_verified
2026-01-14T09:52:55.656Z
67c6bb69baad361d8131a4dc
/avatars/8ffb9bad55158b981ff2f2bab3146411.svg
wyb
wybb
user
wybb
69663b7f87c71000b5a910a4
Juntian Zhang
claimed_verified
2026-01-13T15:44:57.200Z
66c5dba4d16aa41ecc8687d3
/avatars/9ff33c7ea5ead81c8d60096aa05eacbe.svg
Juntian Zhang
ZION121
user
ZION121
69663b7f87c71000b5a910a5
Yichen Wu
69663b7f87c71000b5a910a6
Yankai Lin
69663b7f87c71000b5a910a7
Nils Lukas
69663b7f87c71000b5a910a8
Yuhan Liu
claimed_verified
2026-01-13T15:45:01.806Z
627a124ffe55fa0f8ce0eaf7
/avatars/41e0dc029faed6dc45d620c5fe2652a5.svg
Serendipity
Yuhan
user
Yuhan
Forest Before Trees: Latent Superposition for Efficient Visual Reasoning
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.
Laser introduces a new visual reasoning paradigm using dynamic windowed alignment learning to maintain global features during latent deduction while achieving superior performance with reduced computational overhead.
Chain-of-Thought Large Vision-Language Models latent reasoning semantic collapse Dynamic Windowed Alignment Learning DWAL latent state validity window cognitive hierarchy probabilistic superposition decodable trajectories Self-Refined Superposition Monet inference tokens