@inproceedings{jiang-etal-2026-mined,
title = "{MINED}: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models",
author = "Jiang, Kailin and
Jiang, Ning and
Du, Yuntao and
Ren, Yuchen and
Li, Yuchen and
Gao, Yifan and
Bi, Jinhe and
Ma, Yunpu and
Li, Bin and
Liu, Lei and
Li, Qing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.673/",
pages = "13766--13795",
ISBN = "979-8-89176-395-1",
abstract = "Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs' ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types, which evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios."
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<abstract>Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs’ ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types, which evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios.</abstract>
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%0 Conference Proceedings
%T MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
%A Jiang, Kailin
%A Jiang, Ning
%A Du, Yuntao
%A Ren, Yuchen
%A Li, Yuchen
%A Gao, Yifan
%A Bi, Jinhe
%A Ma, Yunpu
%A Li, Bin
%A Liu, Lei
%A Li, Qing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jiang-etal-2026-mined
%X Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-sensitive knowledge. Existing benchmarks remain constrained by static designs, inadequately evaluating LMMs’ ability to understand time-sensitive knowledge. To address this gap, we propose MINED, a comprehensive benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types, which evaluates temporal awareness along 6 key dimensions and 11 challenging tasks: cognition, awareness, trustworthiness, understanding, reasoning, and robustness. Evaluating 15 widely used LMMs on MINED shows that Gemini-2.5-Pro achieves the highest average CEM score of 63.07, while most open-source LMMs still lack time understanding ability. Meanwhile, LMMs perform best on organization knowledge, whereas their performance is weakest on sport. To address these challenges, we investigate the feasibility of updating time-sensitive knowledge in LMMs through knowledge editing methods and observe that LMMs can effectively update knowledge via knowledge editing methods in single editing scenarios.
%U https://aclanthology.org/2026.findings-acl.673/
%P 13766-13795
Markdown (Informal)
[MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models](https://aclanthology.org/2026.findings-acl.673/) (Jiang et al., Findings 2026)
ACL
- Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, and Qing Li. 2026. MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 13766–13795, San Diego, California, United States. Association for Computational Linguistics.