LaViT：对齐潜在视觉思维以进行多模态推理
LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning
AI总结：
LaViT通过对齐潜在视觉思维提升多模态推理能力，实现视觉感知增强和模型性能突破。
摘要：
当前多模态潜在推理往往依赖外部监督（例如辅助图像），忽略了内在视觉注意力动态。在本工作中，我们识别出一种关键的感知差距：学生模型经常模仿教师的文本输出，但关注于根本上不同的视觉区域，实际上依赖于语言先验而非基于感知的感知。为弥合这一差距，我们提出了LaViT，一个对齐潜在视觉思维而非静态嵌入的框架。LaViT迫使学生自回归地重建教师的视觉语义和注意力轨迹，再进行文本生成，采用课程感官门控机制以防止捷径学习。大量实验表明，LaViT显著增强了视觉感知，实现了在复杂推理任务上高达+16.9%的提升，并使紧凑的3B模型超越了更大的开源变体和专有模型如GPT-4o。
英文摘要：
Current multimodal latent reasoning often relies on external supervision (e.g., auxiliary images), ignoring intrinsic visual attention dynamics. In this work, we identify a critical Perception Gap in distillation: student models frequently mimic a teacher's textual output while attending to fundamentally divergent visual regions, effectively relying on language priors rather than grounded perception. To bridge this, we propose LaViT, a framework that aligns latent visual thoughts rather than static embeddings. LaViT compels the student to autoregressively reconstruct the teacher's visual semantics and attention trajectories prior to text generation, employing a curriculum sensory gating mechanism to prevent shortcut learning. Extensive experiments show that LaViT significantly enhances visual grounding, achieving up to +16.9% gains on complex reasoning tasks and enabling a compact 3B model to outperform larger open-source variants and proprietary models like GPT-4o.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
