@inproceedings{pei-etal-2026-tempcloze,
title = "{T}emp{C}loze: Can Video-{LLM}s Identify the Missing Middle?",
author = "Pei, Wenqi and
Zhao, Henry Hengyuan and
Liu, Yilai and
Meng, Jiahao and
Han, Chen and
Wang, Ziyu and
Du, Hongyang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.651/",
pages = "13676--13693",
ISBN = "979-8-89176-462-0",
abstract = "Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors. To reduce such shortcuts, we introduce $\textbf{TempCloze}$, a video cloze benchmark for evaluating visual temporal reasoning in Video-LLMs. Given the beginning and ending clips of a video, models must identify the true missing middle from four candidates. TempCloze contains 1,521 carefully filtered videos from seven sources, mainly long-take and egocentric videos. We construct same-source distractors along three dimensions: $\textit{Semantic}$ asks what event should happen, $\textit{Alignment}$ probes when it should occur, and $\textit{Progression}$ tests how it should unfold, while shared scenes and objects reduce appearance cues. Our evaluation of 10 proprietary and 21 open-source Video-LLMs reveals Alignment as the primary bottleneck: models often recognize plausible semantic content and local event progression but struggle with temporal alignment. We further conduct error pattern and behavioral sensitivity analyses on TempCloze-Mixed and TempCloze-Hard with four representative models to examine where errors arise and how candidate order, context direction, visible span, frame density, and test-time scaling influence model choices."
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<abstract>Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors. To reduce such shortcuts, we introduce TempCloze, a video cloze benchmark for evaluating visual temporal reasoning in Video-LLMs. Given the beginning and ending clips of a video, models must identify the true missing middle from four candidates. TempCloze contains 1,521 carefully filtered videos from seven sources, mainly long-take and egocentric videos. We construct same-source distractors along three dimensions: Semantic asks what event should happen, Alignment probes when it should occur, and Progression tests how it should unfold, while shared scenes and objects reduce appearance cues. Our evaluation of 10 proprietary and 21 open-source Video-LLMs reveals Alignment as the primary bottleneck: models often recognize plausible semantic content and local event progression but struggle with temporal alignment. We further conduct error pattern and behavioral sensitivity analyses on TempCloze-Mixed and TempCloze-Hard with four representative models to examine where errors arise and how candidate order, context direction, visible span, frame density, and test-time scaling influence model choices.</abstract>
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%0 Conference Proceedings
%T TempCloze: Can Video-LLMs Identify the Missing Middle?
%A Pei, Wenqi
%A Zhao, Henry Hengyuan
%A Liu, Yilai
%A Meng, Jiahao
%A Han, Chen
%A Wang, Ziyu
%A Du, Hongyang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F pei-etal-2026-tempcloze
%X Temporal reasoning benchmarks for Video-LLMs are often mediated by language, leaving room for linguistic shortcuts from option wording, answer correlations, or language priors. To reduce such shortcuts, we introduce TempCloze, a video cloze benchmark for evaluating visual temporal reasoning in Video-LLMs. Given the beginning and ending clips of a video, models must identify the true missing middle from four candidates. TempCloze contains 1,521 carefully filtered videos from seven sources, mainly long-take and egocentric videos. We construct same-source distractors along three dimensions: Semantic asks what event should happen, Alignment probes when it should occur, and Progression tests how it should unfold, while shared scenes and objects reduce appearance cues. Our evaluation of 10 proprietary and 21 open-source Video-LLMs reveals Alignment as the primary bottleneck: models often recognize plausible semantic content and local event progression but struggle with temporal alignment. We further conduct error pattern and behavioral sensitivity analyses on TempCloze-Mixed and TempCloze-Hard with four representative models to examine where errors arise and how candidate order, context direction, visible span, frame density, and test-time scaling influence model choices.
%U https://aclanthology.org/2026.findings-emnlp.651/
%P 13676-13693
Markdown (Informal)
[TempCloze: Can Video-LLMs Identify the Missing Middle?](https://aclanthology.org/2026.findings-emnlp.651/) (Pei et al., Findings 2026)
ACL
- Wenqi Pei, Henry Hengyuan Zhao, Yilai Liu, Jiahao Meng, Chen Han, Ziyu Wang, and Hongyang Du. 2026. TempCloze: Can Video-LLMs Identify the Missing Middle?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13676–13693, Budapest, Hungary. Association for Computational Linguistics.