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arxiv: 2510.19457 · v4 · submitted 2025-10-22 · 💻 cs.CL

MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models

Kailin Jiang , Ning Jiang , Yuntao Du , Yuchen Ren , Yuchen Li , Yifan Gao , Jinhe Bi , Yunpu Ma
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Bin Li Lei Liu Qing Li
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Pith reviewed 2026-05-18 05:07 UTC · model grok-4.3

classification 💻 cs.CL
keywords large multimodal modelstime-sensitive knowledgeknowledge editingtemporal awarenessbenchmarkknowledge updatingmultimodal AI
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The pith

Large multimodal models can update time-sensitive knowledge with editing in single cases but most open-source ones lack temporal understanding.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the MINED benchmark to test large multimodal models on knowledge that changes over time. It builds 2104 samples from Wikipedia across six knowledge types and measures performance on six dimensions and eleven tasks. Results from fifteen models show Gemini-2.5-Pro leading with an average CEM score of 63.07 while most open-source models fall short, with strongest results on organization knowledge and weakest on sports. The study also finds that knowledge editing methods succeed at updating facts when applied one at a time. This matters because models must track changing facts to stay accurate in real applications.

Core claim

MINED evaluates temporal awareness in large multimodal models along six dimensions and eleven tasks with 2104 samples from Wikipedia covering six knowledge types. Among fifteen models tested, Gemini-2.5-Pro achieves the highest average CEM score of 63.07 while most open-source models lack time understanding ability. Models perform best on organization knowledge and weakest on sport knowledge. LMMs can effectively update time-sensitive knowledge via knowledge editing methods in single editing scenarios.

What carries the argument

MINED benchmark that probes temporal awareness through 6 dimensions and 11 tasks on 2104 time-sensitive Wikipedia samples across six knowledge types.

If this is right

  • LMMs can effectively update time-sensitive knowledge via knowledge editing methods in single editing scenarios.
  • Most open-source LMMs lack time understanding ability.
  • Gemini-2.5-Pro achieves the highest average CEM score of 63.07.
  • LMMs perform best on organization knowledge and weakest on sport knowledge.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Single-edit success implies editing could scale to ongoing streams of new facts if batch methods are developed.
  • Performance gaps by knowledge type point to the need for balanced training data that covers fast-changing domains like sports.
  • If the benchmark holds, model developers might add explicit time-stamping layers to pre-training to close the open-closed model divide.
  • Expanding MINED to include non-Wikipedia sources could test whether the current results generalize to other real-world time-sensitive data.

Load-bearing premise

The 2104 samples extracted from Wikipedia by two annotators represent real-world time-sensitive knowledge across the six types and eleven tasks without major bias or coverage gaps.

What would settle it

A follow-up test on facts that occurred after model training cutoffs, checking whether MINED scores predict accuracy on those new events and whether single edits remain stable when more updates are applied later.

read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces MINED, a benchmark for probing temporal awareness in Large Multimodal Models (LMMs) across 6 dimensions (cognition, awareness, trustworthiness, understanding, reasoning, robustness) and 11 tasks. Constructed from 2,104 Wikipedia-derived time-sensitive knowledge samples spanning six knowledge types and annotated by two professionals, it evaluates 15 LMMs and reports Gemini-2.5-Pro achieving the highest average CEM score of 63.07 while most open-source LMMs underperform; models perform best on organization knowledge and weakest on sports. The work also examines knowledge editing and finds that LMMs can effectively update time-sensitive knowledge in single-editing scenarios.

Significance. If the benchmark is shown to be representative and free of substantial annotation bias, the work would be significant for establishing a dedicated probe of time-sensitive multimodal knowledge and for providing direct empirical measurements of current LMM limitations. The evaluation across 15 models together with the editing experiments supplies concrete, falsifiable performance numbers that could inform future model design; the purely empirical construction with held-out samples avoids circularity and strengthens the evidential basis.

major comments (2)
  1. [Benchmark construction] Benchmark construction section: The claim that MINED reveals most open-source LMMs lack time understanding ability rests on the 2,104 samples being representative across the six knowledge types and six dimensions. With extraction performed by only two annotators and no reported inter-annotator agreement or explicit coverage analysis for dynamic domains such as sports, selection bias or under-coverage could artifactually depress scores on certain tasks, undermining the interpretation of performance gaps.
  2. [Editing experiments] Editing experiments section: The observation that LMMs can effectively update knowledge via editing methods is presented as addressing the identified challenges, yet the manuscript provides no evidence that results generalize beyond the single-editing scenarios tested. This limits the strength of the secondary claim and requires either additional multi-edit experiments or explicit qualification of scope.
minor comments (2)
  1. [Abstract and evaluation] Abstract and evaluation sections: The CEM metric is used to report the headline 63.07 score for Gemini-2.5-Pro, yet its precise definition, aggregation across the 11 tasks, and normalization are not stated; adding this would improve interpretability of all reported comparisons.
  2. [Dataset description] Dataset description: Explicit details on how the 11 tasks map onto the 6 dimensions and on the exact annotation guidelines would help readers assess whether the benchmark adequately captures temporal awareness without gaps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have addressed each major comment point by point below, making revisions where they strengthen the work without misrepresenting our contributions. We believe these changes improve the clarity and rigor of the paper.

read point-by-point responses

  1. Referee: [Benchmark construction] Benchmark construction section: The claim that MINED reveals most open-source LMMs lack time understanding ability rests on the 2,104 samples being representative across the six knowledge types and six dimensions. With extraction performed by only two annotators and no reported inter-annotator agreement or explicit coverage analysis for dynamic domains such as sports, selection bias or under-coverage could artifactually depress scores on certain tasks, undermining the interpretation of performance gaps.

    Authors: We appreciate the referee's emphasis on benchmark validity and representativeness. The 2,104 samples were extracted from Wikipedia by two professional annotators following explicit guidelines to span six knowledge types and the six evaluation dimensions, with samples drawn from a broad temporal range to mitigate obvious selection effects. We acknowledge that reporting inter-annotator agreement and a dedicated coverage analysis for high-variance domains such as sports would further strengthen the claims. In the revised manuscript we will add (i) the full annotation protocol, (ii) inter-annotator agreement statistics computed on an overlapping subset of samples, and (iii) a supplementary analysis comparing the sports subset distribution against publicly available event-frequency statistics. These additions will allow readers to assess potential bias directly while preserving the empirical observation that open-source LMMs underperform relative to closed models on the collected data. revision: partial

  2. Referee: [Editing experiments] Editing experiments section: The observation that LMMs can effectively update knowledge via editing methods is presented as addressing the identified challenges, yet the manuscript provides no evidence that results generalize beyond the single-editing scenarios tested. This limits the strength of the secondary claim and requires either additional multi-edit experiments or explicit qualification of scope.

    Authors: We agree that the editing results are demonstrated only for single-edit settings, which is the standard initial evaluation protocol in the knowledge-editing literature but does not speak to sequential or multi-edit robustness. Conducting new multi-edit experiments would require substantial additional compute and data collection beyond the scope of the current study. Accordingly, we will revise the relevant section and conclusion to explicitly qualify the scope: the reported editing success applies to single-edit updates, and we will note that evaluating generalization to multi-edit scenarios constitutes an important avenue for future research. This qualification accurately reflects the evidence presented while avoiding overstatement of the secondary claim. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark construction and evaluation

full rationale

The paper constructs the MINED benchmark by extracting 2,104 time-sensitive knowledge samples from Wikipedia via two professional annotators spanning six knowledge types, then evaluates 15 LMMs across 6 dimensions and 11 tasks while testing knowledge editing feasibility in single scenarios. All reported results consist of direct performance measurements such as average CEM scores (e.g., Gemini-2.5-Pro at 63.07) on held-out samples. There are no equations, fitted parameters renamed as predictions, self-citation chains, uniqueness theorems, or ansatzes that reduce any central claim to its own inputs by construction. The work is self-contained empirical measurement without mathematical derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the representativeness of Wikipedia-extracted samples and the validity of the proposed evaluation dimensions; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Wikipedia entries provide accurate and representative time-sensitive factual knowledge suitable for benchmark construction across six knowledge types.
    Samples are constructed from Wikipedia by two professional annotators as stated in the abstract.
  • domain assumption The six dimensions (cognition, awareness, trustworthiness, understanding, reasoning, robustness) and eleven tasks sufficiently capture temporal awareness in LMMs.
    Benchmark design is presented as comprehensive without further justification in the abstract.

pith-pipeline@v0.9.0 · 5766 in / 1398 out tokens · 30529 ms · 2026-05-18T05:07:36.471723+00:00 · methodology

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