Paper Citation Record · LEDGER

On the Consistency of Video Large Language Models in Temporal Comprehension

As of 22 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2411.12951.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.12951 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:00.966961Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:40.570984Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

59 of 59 outbound references displayed

verified exact0
verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08c990da-1e8e-4903-88b8-653ca79ca3cd · outbound

This paper cites GPT-4 Technical Report.

On the Consistency of Video Large Language Models in Temporal Comprehension GPT-4 Technical Report

Reference 1

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Observation b955f145-fd79-42bf-b5b4-2da3da0f60c4 · outbound

This paper cites The surprising effectiveness of multimodal large language models for video moment retrieval.

On the Consistency of Video Large Language Models in Temporal Comprehension The surprising effectiveness of multimodal large language models for video moment retrieval

Reference 2

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Observation b3c56589-7156-44e0-8e30-add47b684379 · outbound

This paper cites End- to-end object detection with transformers.

On the Consistency of Video Large Language Models in Temporal Comprehension End- to-end object detection with transformers

Reference 3

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Observation 7f1de3a9-2cdc-4bca-ae3d-de878c1e28fa · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

On the Consistency of Video Large Language Models in Temporal Comprehension VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 4

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Observation ad1d2f73-aa96-4fe9-a1c5-a424690178f3 · outbound

This paper cites Instructblip: Towards general-purpose vision- language models with instruction tuning, 2023.

On the Consistency of Video Large Language Models in Temporal Comprehension Instructblip: Towards general-purpose vision- language models with instruction tuning, 2023

Reference 5

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Observation 6a659422-42fe-42db-b2f5-a86f256dd7a9 · outbound

This paper cites Measuring and improving consistency in pretrained language models.

On the Consistency of Video Large Language Models in Temporal Comprehension Measuring and improving consistency in pretrained language models

Reference 6

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Observation 628ecb47-2ece-4484-bbbe-89b43659ec8e · outbound

This paper cites Tall: Temporal activity localization via language query.

On the Consistency of Video Large Language Models in Temporal Comprehension Tall: Temporal activity localization via language query

Reference 7

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Observation d1c10303-a580-4b2c-85bb-671a12b0ab79 · outbound

This paper cites VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding.

On the Consistency of Video Large Language Models in Temporal Comprehension VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding

Reference 8

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Observation c50191b1-4e91-419b-94f6-ceb545b407d0 · outbound

This paper cites Vtimellm: Empower llm to grasp video moments.

On the Consistency of Video Large Language Models in Temporal Comprehension Vtimellm: Empower llm to grasp video moments

Reference 9

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Observation 83e26ae3-5819-42ba-a3f9-862648089c34 · outbound

This paper cites LITA: Language Instructed Temporal-Localization Assistant.

On the Consistency of Video Large Language Models in Temporal Comprehension LITA: Language Instructed Temporal-Localization Assistant

Reference 10

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Source-reported events for the cited work

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Observation f0cca322-944d-419b-a950-650c1a0b051e · outbound

This paper cites Modal-specific pseudo query genera- tion for video corpus moment retrieval.

On the Consistency of Video Large Language Models in Temporal Comprehension Modal-specific pseudo query genera- tion for video corpus moment retrieval

Reference 11

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Source-reported events for the cited work

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Observation 68388e89-0a61-4557-ba7c-0b2692f18aef · outbound

This paper cites Background-aware moment detection for video moment retrieval.

On the Consistency of Video Large Language Models in Temporal Comprehension Background-aware moment detection for video moment retrieval

Reference 12

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Source-reported events for the cited work

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Observation 129af2f7-9a6b-4b7e-bc82-8b43005c2458 · outbound

This paper cites Language Repository for Long Video Understanding.

On the Consistency of Video Large Language Models in Temporal Comprehension Language Repository for Long Video Understanding

Reference 13

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Observation a9a11936-c5ec-4787-9ec8-97e76b1767f8 · outbound

This paper cites Dense-captioning events in videos.

On the Consistency of Video Large Language Models in Temporal Comprehension Dense-captioning events in videos

Reference 14

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Observation 6ff56cb3-4f18-41ec-ae05-55a96c0bb826 · outbound

This paper cites Detecting mo- ments and highlights in videos via natural language queries.

On the Consistency of Video Large Language Models in Temporal Comprehension Detecting mo- ments and highlights in videos via natural language queries

Reference 15

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Observation 3b98d44e-4679-4ccc-9437-5f7e00f4ee6b · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

On the Consistency of Video Large Language Models in Temporal Comprehension VideoChat: Chat-Centric Video Understanding

Reference 16

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Observation daf696bf-3bbd-4c77-a87b-b3a180fd56cd · outbound

This paper cites Mvbench: A comprehensive multi-modal video understand- ing benchmark.

On the Consistency of Video Large Language Models in Temporal Comprehension Mvbench: A comprehensive multi-modal video understand- ing benchmark

Reference 17

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Observation 6eb5061f-cf7e-4ea8-b19a-63b639977b23 · outbound

This paper cites VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models.

On the Consistency of Video Large Language Models in Temporal Comprehension VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models

Reference 18

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Observation cb8b437f-aed0-497b-b36b-f42d083d8a54 · outbound

This paper cites Benchmarking and Improving Generator-Validator Consistency of Language Models.

On the Consistency of Video Large Language Models in Temporal Comprehension Benchmarking and Improving Generator-Validator Consistency of Language Models

Reference 19

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Observation a5ef0812-feb0-4a45-9f1d-dbc5e192265f · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

On the Consistency of Video Large Language Models in Temporal Comprehension Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 20

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Observation f8fe26f4-9732-427d-ab48-ac4823d85ce5 · outbound

This paper cites Visual instruction tuning, 2023.

On the Consistency of Video Large Language Models in Temporal Comprehension Visual instruction tuning, 2023

Reference 21

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Observation bcc114b1-bc9b-4935-b563-bf168f02d0ec · outbound

This paper cites TempCompass: Do Video LLMs Really Understand Videos?.

On the Consistency of Video Large Language Models in Temporal Comprehension TempCompass: Do Video LLMs Really Understand Videos?

Reference 22

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Observation 6af2b771-a107-4a7a-a8d2-f466382770b7 · outbound

This paper cites Valley: Video Assistant with Large Language model Enhanced abilitY.

On the Consistency of Video Large Language Models in Temporal Comprehension Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 23

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Observation fd341e08-3ec7-49fb-a242-d8f604083cb9 · outbound

This paper cites Video-chatgpt: Towards detailed video understanding via large vision and language models.

On the Consistency of Video Large Language Models in Temporal Comprehension Video-chatgpt: Towards detailed video understanding via large vision and language models

Reference 24

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Observation cf2e423e-53b8-461c-a37a-a13460241155 · outbound

This paper cites Query-dependent video representation for moment retrieval and highlight detection.

On the Consistency of Video Large Language Models in Temporal Comprehension Query-dependent video representation for moment retrieval and highlight detection

Reference 25

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Observation f10669a0-866d-4530-b045-cb19b8ef92db · outbound

This paper cites Local-global video-text interactions for temporal grounding.

On the Consistency of Video Large Language Models in Temporal Comprehension Local-global video-text interactions for temporal grounding

Reference 26

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Observation 081f7dd8-e3a6-4ac6-a1ad-3459c8ad70bb · outbound

This paper cites Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models.

On the Consistency of Video Large Language Models in Temporal Comprehension Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Reference 27

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Observation f32a4cb9-4c3a-4245-bf2f-05710e46e5d8 · outbound

This paper cites Uncovering Hidden Challenges in Query-Based Video Moment Retrieval.

On the Consistency of Video Large Language Models in Temporal Comprehension Uncovering Hidden Challenges in Query-Based Video Moment Retrieval

Reference 28

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Observation 52ba0e91-0b05-483b-a6b9-dbba00470632 · outbound

This paper cites Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning.

On the Consistency of Video Large Language Models in Temporal Comprehension Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Reference 29

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Observation d7fd9c77-a4ce-4769-99bc-27b13b521333 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

On the Consistency of Video Large Language Models in Temporal Comprehension Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 30

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Observation 7525b355-3d6c-414c-b3e2-48a18b4b3b0f · outbound

This paper cites Timechat: A time-sensitive multimodal large lan- guage model for long video understanding.

On the Consistency of Video Large Language Models in Temporal Comprehension Timechat: A time-sensitive multimodal large lan- guage model for long video understanding

Reference 31

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Observation edd3e7d5-3664-4011-9117-43be9ee7776d · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

On the Consistency of Video Large Language Models in Temporal Comprehension Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

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This paper cites HawkEye: Training Video-Text LLMs for Grounding Text in Videos.

On the Consistency of Video Large Language Models in Temporal Comprehension HawkEye: Training Video-Text LLMs for Grounding Text in Videos

Reference 33

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Observation 3c426dd8-6ad6-4685-a14d-c9b05c6a5b37 · outbound

This paper cites Negative sample matters: A renaissance of metric learning for temporal grounding.

On the Consistency of Video Large Language Models in Temporal Comprehension Negative sample matters: A renaissance of metric learning for temporal grounding

Reference 34

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This paper cites Chain-of- thought prompting elicits reasoning in large language models.

On the Consistency of Video Large Language Models in Temporal Comprehension Chain-of- thought prompting elicits reasoning in large language models

Reference 35

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This paper cites VideoQA in the Era of LLMs: An Empirical Study.

On the Consistency of Video Large Language Models in Temporal Comprehension VideoQA in the Era of LLMs: An Empirical Study

Reference 36

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This paper cites Can i trust your answer? visually grounded video question answering.

On the Consistency of Video Large Language Models in Temporal Comprehension Can i trust your answer? visually grounded video question answering

Reference 37

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This paper cites A closer look at temporal sentence ground- ing in videos: Dataset and metric.

On the Consistency of Video Large Language Models in Temporal Comprehension A closer look at temporal sentence ground- ing in videos: Dataset and metric

Reference 38

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This paper cites Sc-tune: Unleashing self-consistent referential compre- hension in large vision language models.

On the Consistency of Video Large Language Models in Temporal Comprehension Sc-tune: Unleashing self-consistent referential compre- hension in large vision language models

Reference 39

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On the Consistency of Video Large Language Models in Temporal Comprehension Span-based Localizing Network for Natural Language Video Localization

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On the Consistency of Video Large Language Models in Temporal Comprehension Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 41

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This paper cites Learning 2d temporal adjacent networks for moment local- ization with natural language.

On the Consistency of Video Large Language Models in Temporal Comprehension Learning 2d temporal adjacent networks for moment local- ization with natural language

Reference 42

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On the Consistency of Video Large Language Models in Temporal Comprehension Unveiling the Tapestry of Consistency in Large Vision-Language Models

Reference 43

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On the Consistency of Video Large Language Models in Temporal Comprehension Prompt Consistency for Zero-Shot Task Generalization

Reference 44

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This paper cites Towards auto- matic learning of procedures from web instructional videos.

On the Consistency of Video Large Language Models in Temporal Comprehension Towards auto- matic learning of procedures from web instructional videos

Reference 45

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This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

On the Consistency of Video Large Language Models in Temporal Comprehension MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 46

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On the Consistency of Video Large Language Models in Temporal Comprehension It shows a remarkable zero-shot audio understanding capability and also generates responses to the visual and audio information presented in the videos

Reference 47

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This paper cites To do this, Video- LLaV A collects both image and video-text datasets and incorporates them in its instruction tuning.

On the Consistency of Video Large Language Models in Temporal Comprehension To do this, Video- LLaV A collects both image and video-text datasets and incorporates them in its instruction tuning

Reference 48

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Observation a35b7f22-f46b-48f1-ae24-83cb385aaf2d · outbound

This paper cites It introduces a new dataset for video instruction tuning, containing 100,000 high-quality video-instruction pairs.

On the Consistency of Video Large Language Models in Temporal Comprehension It introduces a new dataset for video instruction tuning, containing 100,000 high-quality video-instruction pairs

Reference 49

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Observation 17f59f9c-44dc-49ae-827c-ec549a3eddba · outbound

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On the Consistency of Video Large Language Models in Temporal Comprehension Unresolved cited work

Reference 50

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Observation 3996aac8-ead6-49ad-879d-829a2fe2e155 · outbound

This paper cites The format should be: ’start time - end seconds’.

On the Consistency of Video Large Language Models in Temporal Comprehension The format should be: ’start time - end seconds’

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:07:01.789263Z

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Observation a304e0bb-3c63-451d-a8fa-d2311d2200bc · outbound

This paper cites The output format should be: ’start - end seconds’.

On the Consistency of Video Large Language Models in Temporal Comprehension The output format should be: ’start - end seconds’

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:07:01.771741Z

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On the Consistency of Video Large Language Models in Temporal Comprehension Specifically, they aim to align vision and text in the first stage and then generate captions from various image-text pairs

Reference 53

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This paper cites They seamlessly integrate both visual and audio modalities in videos and propose STC connector to understand spatiotemporal video informa- tion.

On the Consistency of Video Large Language Models in Temporal Comprehension They seamlessly integrate both visual and audio modalities in videos and propose STC connector to understand spatiotemporal video informa- tion

Reference 54

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On the Consistency of Video Large Language Models in Temporal Comprehension Unresolved cited work

Reference 55

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On the Consistency of Video Large Language Models in Temporal Comprehension Unresolved cited work

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On the Consistency of Video Large Language Models in Temporal Comprehension I’m unable to find timestamps in the video

Reference 57

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This paper cites Experiments on Charades-CON with TimeChat.

On the Consistency of Video Large Language Models in Temporal Comprehension Experiments on Charades-CON with TimeChat

Reference 58

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This paper cites 16 Figure 11.

On the Consistency of Video Large Language Models in Temporal Comprehension 16 Figure 11

Reference 59

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Pith citing papers

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Video-MMLU: A Massive Multi-Discipline Lecture Understanding Benchmark cites this paper.

Video-MMLU: A Massive Multi-Discipline Lecture Understanding Benchmark On the Consistency of Video Large Language Models in Temporal Comprehension

Reference 75

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