@inproceedings{lei-etal-2026-live,
    title = "Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming",
    author = "Lei, Yiming  and
      Fan, Yize  and
      Liu, Zeming  and
      Dong, Jiaji  and
      Qiu, Hui  and
      Leng, Haitao  and
      Liu, Qingjie  and
      Chen, Kehai  and
      Gao, Tingting  and
      Wang, Yunhong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1193/",
    pages = "23813--23850",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advancements in Multimodal Large Language Models (MLLMs) have achieved significant success in understanding static pre-recorded video scenarios (e.g., event-centric or narrative-driven content). However, existing MLLMs are largely trained on datasets restricted to static content due to the scarcity of high-quality interleaved data, causing them to struggle with dynamic interactions. Distinct from pre-recorded videos, live streaming is characterized by high-density, interleaved multimodal turns, where viewer comments (danmaku) are tightly coupled with real-time audio-visual evidence and evolving dialogue context. In such settings, purely textual annotations fail to capture fine-grained visual and temporal dependencies. To bridge this gap, we introduce **Live-Aid**, the first large-scale interleaved live interaction Chinese dataset with **human-annotated**, temporally aligned video responses, spanning over **1,100 hours** and 80,037 dialogue turns across 8,053 video sessions. Building on this, we leverage these high-quality annotations within a novel multi-agent pipeline to construct evaluation tasks targeting core capabilities of live interactions. Extensive evaluations of strong Video-LLMs and Omni-LLMs reveal critical limitations in interleaved multi-turn interactions requiring temporal reasoning, highlighting the value of **Live-Aid** in advancing interleaved multimodal reasoning and dynamic audio-visual dependencies."
}