# VideoGAIA：面向智能体视频理解的通用 AI 助手基准测试

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-12 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmsy53c720kijroz0t4p0g7xw
- 原文链接：https://arxiv.org/abs/2608.14718

## AI 摘要

VideoGAIA 是一个面向通用 AI 助手的智能体视频理解基准，将视频理解从单轮问答转向多轮、工具增强的交互过程，要求模型迭代感知视频、调用外部工具并整合多模态证据。该基准包含 271 个由模型与人类共同设计的任务，每个实例均经三位人类专家独立验证。GPT-5.5、Kimi-K3 等前沿 MLLM 在 VideoGAIA 上准确率均低于 60%，凸显其作为评估下一代 MLLM 基准的价值。

## 正文

Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approximately 90% accuracy on the Video-MME leaderboard, suggesting that conventional single-turn video understanding tasks are becoming increasingly saturated and insufficient for assessing the intelligence of advanced MLLMs. Towards this end, we introduce VideoGAIA, an agentic video understanding benchmark for general artificial intelligence (AI) assistants. Moving beyond one-shot video question answering, VideoGAIA formulates video understanding as a multi-turn, tool-augmented interaction process, where models must iteratively perceive videos, invoke external tools, gather complementary information, and integrate multimodal evidence across turns. VideoGAIA contains 271 model-human co-designed tasks covering diverse and complex real-world scenarios. Each video-question-answer instance is independently verified by three human experts to ensure both correctness and appropriate difficulty. All evaluated MLLMs, including frontier models such as GPT-5.5 and Kimi-K3, achieve less than 60% accuracy on VideoGAIA, highlighting its value as a high-quality and timely benchmark for evaluating next-generation MLLMs. We hope that VideoGAIA will facilitate the transition from conventional video understanding toward agentic video understanding.