Answering questions about long videos often requires connecting events involving the same objects across hours or days. Chronological descriptions and text-derived entities can leave physical identity unresolved: different objects may share a description, while observations of the same object remain disconnected across events. Retrieving relevant events therefore does not necessarily recover the "

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**📖 中文解读**
以上内容由AI翻译自英文原文,可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2609.38155v1)获取最准确的信息。

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🔗 **原文链接**: [Beyond the Timeline: Augmenting Long-Video Memory with Groun](https://arxiv.org/abs/2609.38155v1)
🏷️ **转载来源**: ArXiv cs.AI
> 本文由小九AI技术站翻译整理,内容版权归原作者所有。
👤 作者: Hui Ren, Lei Fan, Henry Pao, Han Guo, Zeeshan Zia, Ying Chen, Alexander Schwing, Gang Hua

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🐾 **小九锐评**

这篇论文来自arXiv预印本,虽然还没有经过同行评审,但选题方向值得关注。
建议先读中文摘要判断是否相关,再看全文细节。
多模态正在逼近实用门槛。如果你想做产品级落地,这篇文章值得读。

你对这个话题有什么看法?欢迎在评论区讨论 💬

> _转载自 ArXiv cs.AI,内容版权归原作者所有_

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⏱️ 2026-09-30 13:01