MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for
  Large Multimodal Models
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 factual 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 that evaluates temporal awareness along 6 key
dimensions and 11 challenging tasks: cognition, awareness, trustworthiness,
understanding, reasoning, and robustness. MINED is constructed from Wikipedia
by two professional annotators, containing 2,104 time-sensitive knowledge
samples spanning six knowledge types. 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.
68f98c94b9b2e4ae046737a2
Kailin Jiang
claimed_verified
2025-10-23T14:50:08.809Z
65745569839aa08899ea5d27
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/4X8waDwiphbfKZySrYlFy.jpeg
kailinjiang
user
kailinjiang
68f98c94b9b2e4ae046737a3
Ning Jiang
68f98c94b9b2e4ae046737a4
Yuchen Ren
68f98c94b9b2e4ae046737a5
Yuchen Li
68f98c94b9b2e4ae046737a6
Yifan Gao
68f98c94b9b2e4ae046737a7
Jinhe Bi
68f98c94b9b2e4ae046737a8
Yunpu Ma
68f98c94b9b2e4ae046737a9
Qingqing Liu
68f98c94b9b2e4ae046737aa
Xianhao Wang
68f98c94b9b2e4ae046737ab
Yifan Jia
68f98c94b9b2e4ae046737ac
Hongbo Jiang
68f98c94b9b2e4ae046737ad
Yaocong Hu
68f98c94b9b2e4ae046737ae
Bin Li
68f98c94b9b2e4ae046737af
Lei Liu
68f98c94b9b2e4ae046737b0
Yuntao Du
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for
  Large Multimodal Models
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 factual 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 that evaluates temporal awareness along 6 key
dimensions and 11 challenging tasks: cognition, awareness, trustworthiness,
understanding, reasoning, and robustness. MINED is constructed from Wikipedia
by two professional annotators, containing 2,104 time-sensitive knowledge
samples spanning six knowledge types. 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.