Multimodal large language models (MLLMs) can now process hour-long videos, making extended multi-turn dialogue over a single video a natural mode of use. Sustaining such a dialogue requires more than
VideoMemBench evaluates multimodal language models on long multi-turn video dialogues requiring retrieval of earlier visual and conversational context, revealing substantial performance gaps.
Multimodal large language models (MLLMs) can now process hour-long videos, making extended multi-turn dialogue over a single video a natural mode of use. Sustaining such a dialogue requires more than answering each question in isolation: the model must retrieve and reuse information established earlier in the conversation, both what it has seen and what it has said, an ability we term in-context video memory. Existing video QA benchmarks leave this ability unmeasured: their questions are mutually independent, never reference one another, and each is answerable from the video alone, so nothing ever requires carrying information across turns. We introduce VideoMemBench, in which each sample…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
VideoMemBench: How Well Do MLLMs Remember in Video Conversations? was first seen on Hugging Face · Daily papers on Nov 5, 2026. Since then it has appeared in 15 checks on 2 different days. Its best position was #12 of 30, reached on Nov 6, 05:28 UTC. It dropped off the list after Nov 6, 11:28 UTC.
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#12 on Hugging Face · Daily papers, on Nov 6, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 5, 2026 and has been on the list on 2 days (15 checks) since.