MBench：一个关于视频世界模型记忆能力的全面基准

摘要

基于视频的世界模型的最新进展展示了合成高保真视觉序列的前所未有的能力。然而，在视觉上合理的视频生成与世界模型的功能需求之间仍存在根本性差距，尤其是在长时间范围内维持稳定且合理的内部状态方面。现有基准主要强调视觉质量、运动一致性和文本-视频对齐，但很大程度上忽略了记忆——即世界模型在长期时间跨度和复杂交互中保持一致性的核心能力。为弥补这一差距，我们提出了MBench，这是一个专门用于量化和评估视频世界模型记忆能力的综合性基准。我们将视频世界模型的记忆能力系统地分解为三个层次化且互补的核心维度：实体一致性、环境一致性和因果一致性，并进一步细化为12个可量化的子维度，以全面表征长期记忆。我们的基准建立在经过严格筛选的真实拍摄的长视频之上，并通过基于规则的量化指标和视觉语言模型进行评估，从而实现客观且全面的一致性评估。对主流最先进的视频世界模型进行的广泛评估，揭示了现有方法在长期状态保持方面的关键系统性局限，为该领域的发展提供了标准化的基准和明确的研究方向。

English

Recent advancements in video-based world models have demonstrated an unprecedented ability to synthesize high-fidelity visual sequences. However, a fundamental gap persists between visually plausible video generation and the functional requirements of a world model, particularly in maintaining a stable and reasonable internal state over extended temporal horizons. While existing benchmarks primarily emphasize visual quality, motion coherence, and text-video alignment, they largely overlook memory, the core capability of a world model to preserve consistency across long-term horizons and complex interactions. To address this gap, we present MBench, a comprehensive benchmark dedicated to quantifying and evaluating the memory capability of video world models. We systematically decompose the memory capability of video world models into three hierarchical and complementary core dimensions: entity consistency, environment consistency, and causal consistency, which are further refined into 12 quantifiable sub-dimensions for comprehensive characterization of long-term memory. Our benchmark is built upon rigorously curated real-captured long videos, and evaluated by rule-based quantitative matrices and VLM to enable objective and comprehensive consistency assessment. Extensive evaluations of mainstream state-of-the-art video world models reveal critical systemic limitations of existing methods in long-term state retention, providing a standardized benchmark and clear research direction to advance the field.