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        <title>TempCore: Are Video QA Benchmarks Temporally Grounded?</title>
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    <abstract>Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current Video QA benchmarks genuinely require temporal frame selection, or can most questions be answered regardless of which frames are shown? We introduce Frame Selection Sensitivity (FSS), a per-sample diagnostic that measures how much VLM accuracy changes when the most relevant frames are replaced with the least relevant ones. Across six benchmarks and eight VLMs, we find that a large majority of samples are frame-agnostic: only a minority are genuinely sensitive to frame choice. Combining FSS with a Language Independence Score (LIS) reveals that merely 5.5–31% of samples are Temporally Sensitive. We construct TempCore, compact evaluation subsets that isolate these temporal samples from existing benchmarks, and will release code and per-sample annotations upon publication.</abstract>
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%0 Conference Proceedings
%T TempCore: Are Video QA Benchmarks Temporally Grounded?
%A Ok, Hyunjong
%A Lee, Jaeho
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ok-lee-2026-tempcore
%X Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current Video QA benchmarks genuinely require temporal frame selection, or can most questions be answered regardless of which frames are shown? We introduce Frame Selection Sensitivity (FSS), a per-sample diagnostic that measures how much VLM accuracy changes when the most relevant frames are replaced with the least relevant ones. Across six benchmarks and eight VLMs, we find that a large majority of samples are frame-agnostic: only a minority are genuinely sensitive to frame choice. Combining FSS with a Language Independence Score (LIS) reveals that merely 5.5–31% of samples are Temporally Sensitive. We construct TempCore, compact evaluation subsets that isolate these temporal samples from existing benchmarks, and will release code and per-sample annotations upon publication.
%U https://aclanthology.org/2026.emnlp-main.125/
%P 2880-2901

Markdown (Informal)

[TempCore: Are Video QA Benchmarks Temporally Grounded?](https://aclanthology.org/2026.emnlp-main.125/) (Ok & Lee, EMNLP 2026)

• TempCore: Are Video QA Benchmarks Temporally Grounded? (Ok & Lee, EMNLP 2026)

ACL

• Hyunjong Ok and Jaeho Lee. 2026. TempCore: Are Video QA Benchmarks Temporally Grounded?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2880–2901, Budapest, Hungary. Association for Computational Linguistics.