Long-form video understanding requires multimodal agents to iteratively gather evidence over many reasoning steps. However, most existing agentic methods suffer from semantic thrashing: as append-only working memory grows, attention to key evidence collapses, and the agent loses access to what it has already found. Fir...
Jian-Ming Xu, Jin-Fa Huang, Jing-Yang Lin et al.· 0 citations
SocialOmni is introduced, an offline diagnostic benchmark that separates three turn-level decisions: identifying who is speaking, deciding when a designated participant should enter at an annotated query time, and determining how that participant should continue the dialogue.
Tian-Yu Xie, Jin-Fa Huang, Yue-Xiao Ma et al.· arXiv.org· 12 citations· ⚡1
Matryoshka Evidence-to-Context (MEC) Frame Selection is introduced, a training-free framework that builds a reusable sparse video index, discovers candidates through sparse probing and local zooming, and greedily constructs a position-adaptive ranking - early positions emphasize evidence; later positions progressively...
Wang Chen, Yu Chen, Xiang Wang et al.· 1 citation· ⚡1
WaveZip is proposed, a joint signal-frequency-domain framework for efficient video inference that requires no task-specific training and can be seamlessly integrated into off-the-shelf LVLMs to boost inference efficiency.
Yuhui Zeng, Wang Chen, Jin-Fa Huang et al.· arXiv.org· 1 citation
TimePLE is proposed, which reformulates VTG from endpoint prediction to interval-native grounding by predicting a single joint distribution over valid temporal intervals, and curate 90K-scale grounded samples and human-verify 3K-scale benchmark annotations.
Yuhui Zeng, Xin-Yu Mao, Xiaokun Liu et al.· arXiv.org· 0 citations
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