循环回溯以前进：递归变换器用于高效灵活的大型多模态模型
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
AI总结：
RecursiveVLM通过递归细化机制提升多模态模型效率，实现参数复用和性能提升。
摘要：
大型多模态模型（LMMs）在视觉-语言任务中取得了显著成功，但其庞大的参数数量在训练和推理过程中往往被低效利用。在本工作中，我们采用“循环回溯以前进”的理念：通过递归细化重用模型参数，提取更强的多模态表示，而无需增加模型规模。我们提出了RecursiveVLM，一种专为LMMs设计的递归Transformer架构。两项关键创新使有效循环成为可能：（i）递归连接器通过融合中间层隐藏状态并应用模态特定投影，在递归步骤中对齐特征，尊重视觉和语言标记不同的统计结构；（ii）单调递归损失监督每一步并保证性能随递归深度单调提高。这种设计将递归转化为按需细化机制：在资源受限设备上通过少量循环即可获得强结果，在更多计算资源可用时逐步改进输出。实验显示，与标准Transformer相比，性能提升+3%，与 vanilla 递归基线相比提升+7%，证明了战略性循环是通往高效、部署适应性LMMs的强大路径。
英文摘要：
Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference. In this work, we embrace the idea of looping back to move forward: reusing model parameters through recursive refinement to extract stronger multimodal representations without increasing model size. We propose RecursiveVLM, a recursive Transformer architecture tailored for LMMs. Two key innovations enable effective looping: (i) a Recursive Connector that aligns features across recursion steps by fusing intermediate-layer hidden states and applying modality-specific projections, respecting the distinct statistical structures of vision and language tokens; (ii) a Monotonic Recursion Loss that supervises every step and guarantees performance improves monotonically with recursion depth. This design transforms recursion into an on-demand refinement mechanism: delivering strong results with few loops on resource-constrained devices and progressively improving outputs when more computation resources are available. Experiments show consistent gains of +3% over standard Transformers and +7% over vanilla recursive baselines, demonstrating that strategic looping is a powerful path toward efficient, deployment-adaptive LMMs.
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。
