# 自回归视频生成中的记忆机制综述：过去如何框定未来

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-23 08:00
- AIHOT 分数：34
- AIHOT 链接：https://aihot.news/items/cmuewn5g2033vro3khjk98601
- 原文链接：https://arxiv.org/abs/2609.28466

## AI 摘要

一篇综述系统梳理了自回归（AR）视频生成中的记忆机制，将记忆定义为跨 AR 步骤持续保留、在原始证据离开上下文后仍能影响后续生成的历史信息。文章从形式、功能、操作、学习与评估五个视角组织文献，并指出可组合且资源感知的记忆架构、可信状态更新、自 rollout 学习与标准化评估等开放挑战。

## 正文

Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded context windows, storage, and computational limits. Consequently, critical historical information, e.g., entity identities, dynamic states, and intervention-induced causal changes, often leaves the active context long before its relevance diminishes.

Overcoming this limitation and maintaining temporal persistence constitutes a fundamental memory problem. We present a systematic and comprehensive review of memory mechanisms in AR video generation. We formulate memory operationally as persistent historical information maintained across outer AR steps, capable of influencing future generation even after the originating evidence is no longer locally accessible. Building upon this unified framework, we organize the literature through five complementary perspectives: (I) Forms, the representational carriers of history; (II) Functions, the specific semantic and physical information requiring preservation; (III) Operations, the lifecycle of writing, reading, updating, managing, and integrating memory; (IV) Learning, the optimization of memory behaviors under closed-loop rollouts; and (V) Evaluation, the paradigms for diagnosing genuine memory capabilities.

We conclude by synthesizing open challenges, including composable and resource-aware memory architectures, trustworthy state updating, self-rollout learning, and standardized evaluation. By bridging representations, mechanisms, and learning paradigms, this paper establishes a structured foundation for developing reliable, memory-conditioned video generation systems.