P$^3$IBench：评测视频助手的主动预判与干预能力
From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios
一篇新 benchmark 论文，测了 12 个视频模型能不能主动预判风险并出手帮忙，结论是时机把握普遍不行，还附带了他们自己训的 4B 模型。
arXiv 论文提出 P$^3$IBench，评测真实流式场景下视频助手的三个能力：日常感知、风险预测和干预。研究测试了 12 个开源和闭源视频语言模型，包括流式系统和改编的非流式系统。结果显示现有模型难以做出校准良好的响应时机决策。作者构建主动式训练数据并训练了 P$^3$I-Assistant-4B，在部分任务上有提升，但各能力间收益不均衡。
From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios
Recent multimodal large language models have advanced video understanding, but existing benchmarks mainly evaluate whether models can interpret observed visual content. In real-world scenarios, assistants must additionally anticipate what assistance is needed and decide when to provide it. To address this gap, we introduce \textsc{P$^3$IBench}, a benchmark for proactive video understanding in real-life streaming scenarios. It evaluates three complementary capabilities: daily perception, risk prediction and intervention. We benchmark 12 representative open-source and proprietary video-language models, including streaming and adapted non-streaming systems. Our evaluation shows that current models struggle to make well-calibrated response decisions. Finally, we construct additional proactive training data and train \textsc{P$^3$I-Assistant-4B}, achieving improvements on several tasks, though gains remain uneven across capabilities. Together, these findings show that effective proactive assistance requires not only visual understanding, but also precise response timing and appropriate restraint.
