OVO-S-Bench：多模态大语言模型中流式空间智能的分层基准

摘要

多模态智能体在机器人、增强现实和自动驾驶领域，需要从连续以自我为中心的信息流中推断位置和布局，往往需要借助当前视野之外的证据。现有基准测试要么在完整视频上进行离线评估，要么聚焦于事件而非空间结构。我们提出了OVO-S-Bench，这是一个完全人工标注的流式空间智能基准测试，包含来自348个源视频的1680个问题。标注工作由12名经过训练的标注人员完成，每人同时担任盲审交叉复核员，总共耗时约804人小时进行多轮质量保证。每个问题都带有查询时间戳和证据区间，在评估时，模型只能看到查询之前的前缀信息。问题涵盖四个抽象层级：即时自我中心感知、时空上下文追踪、空间模拟与推理，以及以环境为中心的地图构建。在38个专有和开源多模态大语言模型中，Gemini-3.1-Pro以59.2分落后人类专家的86.6分达27个百分点，其中以环境为中心的地图构建是主要瓶颈。值得注意的是，经过流式处理和空间微调的多模态大语言模型表现甚至不如其基础模型。此外，我们发现思维链推理在缺乏信息流支撑时会放大空间错误。通过揭示这些局限性，OVO-S-Bench为下一代流式空间多模态大语言模型建立了一个高要求的测试平台。

English

Multimodal agents in robotics, AR, and autonomous driving must reason about places and layouts from continuous egocentric streams, often using evidence outside the current view. Existing benchmarks either evaluate offline over full videos or target events rather than spatial structure. We introduce OVO-S-Bench, a fully human-annotated benchmark for streaming spatial intelligence, comprising 1,680 questions over 348 source videos. Annotation involves 12 trained annotators, each also serving as a blind cross-reviewer, across roughly 804 person-hours of multi-round quality assurance. Each question carries a query timestamp and an evidence interval, and at evaluation, the model sees only the prefix preceding the query. Questions span four levels of increasing abstraction: instantaneous egocentric perception, spatiotemporal context tracking, spatial simulation and reasoning, and allocentric mapping. Across 38 proprietary and open-source MLLMs, Gemini-3.1-Pro trails human experts by 27 points, 59.2 vs. 86.6, with allocentric mapping as the dominant bottleneck. Notably, streaming and spatially fine-tuned MLLMs underperform their own backbones. We further find that chain-of-thought reasoning amplifies spatial errors when ungrounded in the stream. By exposing these limitations, OVO-S-Bench establishes a demanding testbed for next-generation streaming spatial MLLMs.