@inproceedings{luo-etal-2026-museg,
    title = "{MUSEG}: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding",
    author = "Luo, Fuwen  and
      Lou, Shengfeng  and
      Chen, Chi  and
      Wang, Ziyue  and
      Li, Chenliang  and
      Shen, Weizhou  and
      Guo, Jiyue  and
      Li, Peng  and
      Yan, Ming  and
      Zhang, Ji  and
      Huang, Fei  and
      Liu, Yang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1644/",
    pages = "35549--35561",
    ISBN = "979-8-89176-390-6",
    abstract = "Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, current MLLMs still struggle with fine-grained temporal reasoning. While reinforcement learning (RL) has been explored to address this issue recently, existing RL approaches remain limited in performance on time-sensitive tasks. In this work, we propose **MUSEG**, a novel RL-based method that enhances temporal understanding by introducing timestamp-aware multi-segment grounding. MUSEG enables MLLMs to align queries with multiple relevant video segments, promoting more comprehensive temporal reasoning. To facilitate effective learning, we design a customized RL training recipe with phased rewards that progressively guides the model toward temporally grounded reasoning. Extensive experiments on temporal grounding and time-sensitive video question answering (QA) tasks demonstrate that MUSEG significantly outperforms existing methods and generalizes well across diverse temporal understanding scenarios."
}