ReGuLaR：面向大型视觉语言模型的基于关系的潜在推理
ReGuLaR: Relation-Grounded Latent Reasoning for Large Vision-Language Models
AI总结：
提出ReGuLaR框架，通过训练时的ReGFormer将潜在推理显式地锚定在视觉证据中的对象和关系上，在多种基准上取得最优性能。
摘要：
链式思维推理通过用自然语言表述中间推理步骤，显著提升了大视觉语言模型的推理能力。然而，这种离散的文本理由通常不足以编码连续的视觉证据。最近的工作通过将推理转移到连续潜在空间来解决这一限制。尽管取得了有希望的进展，现有方法仍使潜在推理与视觉证据的组合结构和关系结构联系不足。为填补这一空白，我们引入了ReGuLaR，一种基于关系的潜在推理框架，将潜在状态显式地锚定在这些关键但被忽视的视觉证据上。ReGuLaR在训练时使用ReGFormer使潜在推理聚焦于与问题相关的对象及对象间关系，而在推理时模型无需调用ReGFormer即可推理并生成答案。为支持ReGuLaR的训练，我们构建了RGROUNDING-351K，一个标注了关键对象边界框和对象间关系的真实世界视觉语言数据集。在多种基准上的广泛实验表明，ReGuLaR持续优于现有方法，并取得了最先进的性能。我们在投稿中包含了代码，并将在接收后公开发布代码和训练数据。
英文摘要：
Chain-of-thought (CoT) reasoning has significantly improved the reasoning ability of large vision-language models (LVLMs) by verbalizing intermediate reasoning steps in natural language. However, such discrete textual rationales are often insufficient for encoding continuous visual evidence. Recent work addresses this limitation by moving reasoning into continuous latent space. Despite promising progress, existing methods leave latent reasoning insufficiently connected to the compositional and relational structure of visual evidence. To address this gap, we introduce ReGuLaR, a relation grounded latent reasoning framework that explicitly grounds latent states in these critical yet overlooked visual evidence. ReGuLaR uses a training-time ReGFormer to focus latent reasoning on question-relevant objects and inter-object relations, while at inference time the model reasons and generates answers without invoking the ReGFormer. To support training ReGuLaR, we construct RGROUNDING-351K, a real-world vision-language dataset annotated with key object bounding boxes and inter-object relations. Extensive experiments across diverse benchmarks show that ReGuLaR consistently outperforms existing approaches and achieves state-of-the-art performance. We include our code in the submission and will release the code and training data publicly upon acceptance.
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。
