摘要：现实世界场景中可靠的视觉安全理解，不仅需要对象识别，还需要在认知不确定性下进行因果推理。尽管大视觉语言模型（LVLM）在标准基准上展现出令人印象深刻的语义对齐能力，但当模型面对动态且部分可观测的第一人称体验时，往往难以区分表层相关性与真正的取证逻辑。现有评测主要由第三人称监控视频和二元分类指标主导，未能揭示这一认知鸿沟。为此，我们提出 EgoSafe-Bench，这是一个专门用于探测以自我中心视角呈现的安全场景中取证推理能力的基准。该基准包含 12,000 个独立评测样本：每个样本由 3,000 段视频片段之一与一条受我们提出的分层推理评估（Hierarchical Reasoning Evaluation，HRE）协议约束的问答链配对生成。不同于标准基准，HRE 要求模型遵循严格的推理轨迹，从初始特征锚定，到盲区推断，再到意图推理；这一机制借此强制保证逻辑一致性，并惩罚依赖捷径的预测。对最先进 LVLM（如 Qwen3-VL、Gemini、VideoLLaMA 3）进行的大规模评测揭示了显著的感知—推理解耦现象：模型通常能获得较高的描述性得分，却在因果推理和逻辑闭环方面表现出明显脆弱性。本研究同时提供了一个具有挑战性的数据集和一套系统化评估框架，以推动逻辑鲁棒的视频理解系统发展。

Abstract:Reliable visual safety understanding in real-world scenarios demands more than just object recognition; it requires causal reasoning under epistemic uncertainty. While Large Vision-Language Models (LVLMs) demonstrate impressive semantic alignment on standard benchmarks, they often struggle to distinguish between superficial correlation and genuine forensic logic when grounded in the dynamic, partially observable nature of first-person experiences. Existing evaluations, dominated by third-person surveillance footage and binary classification metrics, fail to expose this cognitive gap. To address this, we introduce EgoSafe-Bench, a benchmark specifically designed to probe forensic reasoning in egocentric safety scenarios. It comprises 12,000 unique evaluation samples, generated by pairing each of the 3,000 video clips with a QA chain governed by our proposed Hierarchical Reasoning Evaluation (HRE) protocol. Unlike standard benchmarks, HRE mandates a rigorous reasoning trajectory from initial feature anchoring to blind-spot deduction and intent inference, thereby enforcing logical consistency and penalizing shortcut-based predictions. Extensive evaluations of state-of-the-art LVLMs (e.g., Qwen3-VL, Gemini, VideoLLaMA 3) reveal a significant perception-reasoning decoupling: models often achieve high descriptive scores but exhibit notable fragility in causal reasoning and logical closure. Our work provides both a challenging dataset and a systematic evaluation framework to foster the development of logically robust video understanding systems.