Despite the rapid progress of video generation models, the role of data in influencing motion is poorly understood. We present Motive (MOTIon attribution for Video gEneration), a motion-centric, gradient-based data attribution framework that scales to modern, large, high-quality video datasets and models. We use this to study which fine-tuning clips improve or degrade temporal dynamics. Motive isolates temporal dynamics from static appearance via motion-weighted loss masks, yielding efficient and scalable motion-specific influence computation. On text-to-video models, Motive identifies clips that strongly affect motion and guides data curation that improves temporal consistency and physical plausibility. With Motive-selected high-influence data, our method improves both motion smoothness and dynamic degree on VBench, achieving a 74.1% human preference win rate compared with the pretrained base model. To our knowledge, this is the first framework to attribute motion rather than visual appearance in video generative models and to use it to curate fine-tuning data.

尽管视频生成模型取得了快速进展，但人们对数据在影响运动中的作用却知之甚少。我们提出了 Motive（视频生成的 MOTIon 归因），这是一种以运动为中心、基于梯度的数据归因框架，可扩展到现代、大型、高质量的视频数据集和模型。我们用它来研究哪些微调剪辑可以改善或降低时间动态。 Motive 通过运动加权损失掩模将时间动态与静态外观隔离，从而产生高效且可扩展的运动特定影响计算。在文本到视频模型上，Motive 可以识别强烈影响运动的剪辑，并指导数据管理，从而提高时间一致性和物理合理性。利用Motive选择的高影响力数据，我们的方法提高了VBench上的运动平滑度和动态程度，与预训练的基础模型相比，实现了74.1%的人类偏好获胜率。据我们所知，这是第一个在视频生成模型中归因于运动而不是视觉外观并使用它来管理微调数据的框架。

The evolution of recommender systems has shifted preference storage from rating matrices and dense embeddings to semantic memory in the agentic era. Yet existing agents rely on isolated memory, overlooking crucial collaborative signals. Bridging this gap is hindered by the dual challenges of distilling vast graph contexts without overwhelming reasoning agents with cognitive load, and evolving the collaborative memory efficiently without incurring prohibitive computational costs. To address this, we propose MemRec, a framework that architecturally decouples reasoning from memory management to enable efficient collaborative augmentation. MemRec introduces a dedicated, cost-effective LM_Mem to manage a dynamic collaborative memory graph, serving synthesized, high-signal context to a downstream LLM_Rec. The framework operates via a practical pipeline featuring efficient retrieval and cost-effective asynchronous graph propagation that evolves memory in the background. Extensive experiments on four benchmarks demonstrate that MemRec achieves state-of-the-art performance. Furthermore, architectural analysis confirms its flexibility, establishing a new Pareto frontier that balances reasoning quality, cost, and privacy through support for diverse deployments, including local open-source models. Code:https://github.com/rutgerswiselab/memrec and Homepage: https://memrec.weixinchen.com

推荐系统的发展已经将偏好存储从评级矩阵和密集嵌入转变为代理时代的语义记忆。然而现有的智能体依赖于孤立的记忆，忽视了关键的协作信号。弥合这一差距受到双重挑战的阻碍：提取大量图形上下文而不用认知负荷压倒推理代理，以及有效地发展协作记忆而不产生过高的计算成本。为了解决这个问题，我们提出了 MemRec，这是一个在架构上将推理与内存管理解耦的框架，以实现高效的协作增强。 MemRec 引入了专用的、经济高效的 LM_Mem 来管理动态协作内存图，为下游 LLM_Rec 提供合成的高信号上下文。该框架通过一个实用的管道进行操作，该管道具有高效的检索和经济高效的异步图形传播，可在后台演化内存。对四个基准的大量实验表明 MemRec 实现了最先进的性能。此外，架构分析证实了其灵活性，建立了一个新的帕累托前沿，通过支持包括本地开源模型在内的多种部署来平衡推理质量、成本和隐私。代码：https://github.com/rutgerswiselab/memrec 和主页：https://memrec.weixinchen.com