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Paper Citation Record · LEDGER

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models

As of 8 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 3 inbound Pith citation observations for arXiv:2505.15435.

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

pith.paper-citation-record.v1
2505.15435 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:08.559862Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:27:03.674229Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:33:15.607500Z

Reference resolution

58 of 58 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved58
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f53eb2c-dbce-4ace-87fb-44ca5f2f7ef8 · outbound

This paper cites online" 'onlinestring :=.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models online" 'onlinestring :=

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:01.897899Z digest=sha256:c0ad80988773104ceb7be86e176dd72e25df41117835439113b6093f90b81aa1

Observation 445f09ff-3a76-46d4-958f-f361391417ee · outbound

This paper cites write newline.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models write newline

Reference 2

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source=arxiv_source observed=2026-08-07T15:21:01.996933Z digest=sha256:90ff6f43225ff8602e2bcb4d35206c6e1010c1bfd4372cc9f9a3337040f3c129

Observation ec76e173-e816-46f3-8405-205f991a40d7 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 3

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source=arxiv_source observed=2026-08-07T15:21:02.062900Z digest=sha256:356348eef3c57228674985fa65bef070021731179de8054f9d59bfeebe15d009

Observation 216d29cd-008f-403e-a699-f42533fd257d · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.152467Z digest=sha256:9d4161c29eafd4d21c378cb94a0f53bcf15c768b23e4639c59c0f3507d6b0be9

Observation 82beea28-e0a4-4388-b12e-4125d60ac75f · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.218499Z digest=sha256:0a77ae41f97b078aa96345e3e6adaf0e868908bfd5bb8bf47c1d06ff7a5dbae6

Observation 1eeee35d-e988-49da-80b4-e3dd4127d940 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 6

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source=arxiv_source observed=2026-08-07T15:21:02.331665Z digest=sha256:51a4122dfb75732e671eef6d017c0f7be7ded5a49da5a5c385181cc0cac3cd68

Observation 84ef2487-92c8-432c-9b13-6db5082652b5 · outbound

This paper cites Qwen2.5-VL Technical Report.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Qwen2.5-VL Technical Report

Reference 7

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source=arxiv_source observed=2026-08-07T15:21:02.451677Z digest=sha256:3e820c976b777e99970a161814699b0ba2864fff2bc65b5462d29d731e82b8de

Observation 218cf0c3-5a73-418e-bf61-5a4c23ccf024 · outbound

This paper cites Impossible Videos.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Impossible Videos

Reference 8

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source=arxiv_source observed=2026-08-07T15:21:02.536850Z digest=sha256:94590886a85c9db3ed6e1ab4a1077dd5bf0d22e324298af5e02d3f1daecdd192

Observation 46e84066-4c31-4f17-98a7-79717af5cf07 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 9

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source=arxiv_source observed=2026-08-07T15:21:02.707953Z digest=sha256:bc2b657d8b099bfcaed7efca0e16a24466da110f550b279cb381ab41959a7c6c

Observation b2d305cd-1c1e-4c0b-8c68-62234e14ab57 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-07T15:21:02.830991Z digest=sha256:583838d4bfbc7d4d5e437979f509b450aa3bd16d3eebbd26eb7a6824cb27d19d

Observation 7cedd5a6-3b1e-4b0f-a5d5-185d12943e0a · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.924684Z digest=sha256:98bcf475144f268d391acae5c277e828fe5e44c1606245d030edb60b00c8424b

Observation af172295-76cc-4ba5-bf35-5d5561ae1910 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 12

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:03.030057Z digest=sha256:8eda4de0f36ae0c0805ce8874277fd71d4e133145c880ffa6f933f8506d0fc52

Observation 88f38579-9ced-4e65-abcb-f5c8ac69ea40 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Scaling Instruction-Finetuned Language Models

Reference 13

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source=arxiv_source observed=2026-08-07T15:21:03.130746Z digest=sha256:c794d7cf2d393df568d111082dbf6c5169b36301d6b6d9d5342fb64e0a37944a

Observation 640635d4-8ae0-4327-8ef1-241b1eb24b0e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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source=arxiv_source observed=2026-08-07T15:21:03.249527Z digest=sha256:f26b6f397f892d576312a4c7c6ff40b2c58b2799e62583b64bc7f07f069b580d

Observation 627fb513-bad7-4ea4-bda2-4b64db0c639f · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 16

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source=arxiv_source observed=2026-08-07T15:21:03.535519Z digest=sha256:af23de2aeca4fe4af51d95fa1688adfb20a451d3f40f8e5f62f84ead691ec920

Observation b7dd6055-5026-4b96-8b03-cb440ab0a84b · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:03.649848Z digest=sha256:7717f83f34e5e10a7688f8ae0722826da1ee66ec07ee25c313d889ffac859a28

Observation f6aec4ef-04ab-44e1-9795-eac64d00109f · outbound

This paper cites The Llama 3 Herd of Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models The Llama 3 Herd of Models

Reference 18

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source=arxiv_source observed=2026-08-07T15:21:03.788774Z digest=sha256:1d609ee028dea545a1ceca73b4fdba7fb73351585db1717028eaa1a48f72becf

Observation 2c8162b7-1d8f-42ab-954f-5ad194dd303f · outbound

This paper cites Efficient Multimodal Learning from Data-centric Perspective.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Efficient Multimodal Learning from Data-centric Perspective

Reference 19

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source=arxiv_source observed=2026-08-07T15:21:03.895366Z digest=sha256:dc51a249c226a5c83c30e261df9eab59ecf0d75f163b2a2e9dcf90642a1252ed

Observation 6425c5f5-2628-4408-8271-946a7a686471 · outbound

This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models CogVLM2: Visual Language Models for Image and Video Understanding

Reference 20

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source=arxiv_source observed=2026-08-07T15:21:04.051014Z digest=sha256:768935ab983d881f9bfab115e626a723bc468919c92355ce01674d209d580d4c

Observation 954e36cb-c8dc-4f31-b379-aa5c102e721a · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:04.163938Z digest=sha256:592b55e2080543d42fc55bdd46c87ea866bbeb5b4534175d6014fe3fd3f04f51

Observation e6fc057c-dfae-4535-b908-ab7a9a896b29 · outbound

This paper cites Mistral 7B.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Mistral 7B

Reference 22

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source=arxiv_source observed=2026-08-07T15:21:04.260199Z digest=sha256:0ff6c7b427359580efb0758061ed0dfd3beb7009da40ba2db54dd141e08a3639

Observation b8a9cbf2-4eda-4048-8a07-862490b3936a · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:04.355966Z digest=sha256:ca3f527ef0fffc871876a15a686f740ae917d424e35d294e8895a7ce258f0a95

Observation 056bf031-8874-4873-a489-2bc26aa9f4c4 · outbound

This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Reference 24

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source=arxiv_source observed=2026-08-07T15:21:04.487040Z digest=sha256:8174143552912c028211af9e1569614d441a363301b94029e0839b2106ca19e7

Observation 039e884b-22be-453f-a0e4-4a4d613024a8 · outbound

This paper cites LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Reference 25

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source=arxiv_source observed=2026-08-07T15:21:04.593542Z digest=sha256:b5086ecd79483c24ce309e31e32ecaf896d48625292fd345abf9ccae1d845661

Observation c4e2eb9e-1707-4ee6-8b0d-b25d5b4162f7 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 26

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:04.740070Z digest=sha256:1609a977d01368bdc86f1e92b8a3042927faa4d96f376076baf020476af97948

Observation 44ba6a1e-313f-45b3-b17d-9e3b20c02255 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:04.768471Z digest=sha256:e720f8c5868a0cd7c75cb1712bc682b50d4d474ceeabbfddcc247a73a615423d

Observation 01a14c51-e559-4fa9-a653-a5299ebf00c5 · outbound

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

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models

Reference 28

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:04.771148Z digest=sha256:604a7b5938017e36adcab500fcce9041169d2e72b39c01d92f6ac50a6e694151

Observation 6685021b-79d0-49b0-952d-3f044846202d · outbound

This paper cites Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models

Reference 29

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source=arxiv_source observed=2026-08-07T15:21:04.774658Z digest=sha256:f9820835ceddd029a79b43036183940252b854059fc35e20163e7781e122b50c

Observation 2cf8e1f9-7b5c-4f6a-a3ad-37cd939180ec · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-07T15:21:04.851495Z digest=sha256:3d330d06202fc6524bd72ce7e838481ff7e642e71babdacc8035c3dde088558e

Observation d63b1fd5-9a45-46a0-aa97-d76e1dcfb7e6 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 31

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no resolver link, observed 2026-08-07T15:21:04.947994Z

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source=arxiv_source observed=2026-08-07T15:21:04.947994Z digest=sha256:fede0b8bca585082d82cc4a6cd28cf12c28dbe822e1cdc248ac933ef7d433db2

Observation b6dcae9b-d0dc-4923-b60d-f7b60375cf58 · outbound

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

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models TempCompass: Do Video LLMs Really Understand Videos?

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:05.036615Z digest=sha256:752739e5af02d7a702b5242edc0baa3b356b9f98a7a79d78e419fa0fcbfe86b1

Observation 145cc998-7cf1-4a90-a99a-40d80a77bff8 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.161689Z digest=sha256:0308d1f54b3fbec23d5f9cbaa79c433c9bdba2b500ee28c0d184e2174e72c011

Observation e2947ecf-9fdf-4ebe-a753-c19a0cae9a71 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.246434Z digest=sha256:482a2efa348141442a65f372c93fa505bb12654e49d027daf04e805da2e313da

Observation 13d25a94-f9e4-46f9-ac01-cb6142da07aa · outbound

This paper cites Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Reference 35

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source=arxiv_source observed=2026-08-07T15:21:05.372302Z digest=sha256:9e22d884e0362645e6d68cfb76dc8ae007fe7816a09ee1e1dc5861986bbdc627

Observation c9c0aa41-0ace-4d52-82ef-682ec8b0919c · outbound

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

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Reference 36

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source=arxiv_source observed=2026-08-07T15:21:05.497735Z digest=sha256:207acecfbb174060b0b81454b3aba20925db92fdfb6cfc4e9ce7272eb0d47912

Observation 0d77b8db-c044-4b64-a8d0-1211e6dffdc6 · outbound

This paper cites GPT-4o System Card.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models GPT-4o System Card

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:05.685566Z digest=sha256:879dc243b2b346597be496788552cc5bba40102cc4edd7f6f7ad551d6e886576

Observation 14412af5-64c2-4158-9c1d-75ba21a59604 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-07T15:21:09.297461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.853733Z digest=sha256:d68e9c7c750b7ad3c55bd2c0b7b1f104960658df8951fdedb77bf83bd9c51371

Observation e3ba647c-9f9c-434b-ae3b-6306d600074b · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 39

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source=arxiv_source observed=2026-08-07T15:21:05.940815Z digest=sha256:a97dc7f28696f16fa4f175ad0b75df09060b1278943dacdc6c77124a74e237a5

Observation 9d5be64a-5744-4655-9384-60088d952b7c · outbound

This paper cites Humanity's Last Exam.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Humanity's Last Exam

Reference 40

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source=arxiv_source observed=2026-08-07T15:21:06.071552Z digest=sha256:3699758f9d800a66b1bcf74f44aff8ca5aa16626b234f252277b625353cea497

Observation 69230039-c42b-44ae-97a2-ba8355cd7fa9 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 41

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source=arxiv_source observed=2026-08-07T15:21:06.267102Z digest=sha256:c918851b3d7a1f54703a8887a9cd0904e895ee12d7375cd64e7971a7b5d59544

Observation 39ca58c8-aa14-4b82-af06-792237eff944 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 42

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source=arxiv_source observed=2026-08-07T15:21:06.449932Z digest=sha256:0316366e13d72110bab5f15fbf27a2f0bbd6dee6409d8fbccb0a877ca6642e6e

Observation c628ca5d-3a85-40cc-a750-e34bee2d160f · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 43

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source=arxiv_source observed=2026-08-07T15:21:06.641219Z digest=sha256:cfe3eda377e1ae70c913ea64f5735e5ff1a042ef9fd58478fab76890f9324781

Observation c8035537-ae48-42e0-894e-0e6c16c842ce · outbound

This paper cites MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification

Reference 44

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source=arxiv_source observed=2026-08-07T15:21:06.787806Z digest=sha256:b689b2c1568a51c87998975822d11693a8322159c65372f6570caa9a836e32c1

Observation 8128d2ce-0dd0-4952-8a67-5aa1b8cc6633 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 45

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source=arxiv_source observed=2026-08-07T15:21:06.920302Z digest=sha256:863da44a8cddfc6bbbb6c681f29b0562deb2c64de4cb6bfa4c8b54cbe0bdce3d

Observation 543de62e-338d-4339-b276-26a66aa69b8a · outbound

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

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 46

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no resolver link, observed 2026-08-07T15:21:07.073488Z

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source=arxiv_source observed=2026-08-07T15:21:07.073488Z digest=sha256:dc9f1a9f9aebb21f244d7ddfaf402142230912ed1f361b5eed861f32ee9ce5ba

Observation 573f87b1-67c5-4310-a457-7bf36f630d62 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 47

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no resolver link, observed 2026-08-07T15:21:07.243953Z

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source=arxiv_source observed=2026-08-07T15:21:07.243953Z digest=sha256:1236ccafc0b8f94179aa804fd7ce4f18566ac3af5e27afc3b5774f551c57b5de

Observation cd21bd03-7f4e-4771-9884-933151047622 · outbound

This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 48

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no resolver link, observed 2026-08-07T15:21:07.416688Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:21:07.416688Z digest=sha256:baafea66d192b02c76114fc106492dfc94e07c7ea24272618da3815f0c195d4e

Observation 37dbdc00-6367-425e-b14a-eee599f99ee6 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 49

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source=arxiv_source observed=2026-08-07T15:21:07.549870Z digest=sha256:12a3f9d98129e6ada8b811adeee1e74799175342453c14ec2adb83c21234b037

Observation 5ec21d43-91b3-4b21-9237-23bd9a1de64b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 50

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source=arxiv_source observed=2026-08-07T15:21:07.684945Z digest=sha256:3c7bfb1f969a8fd61f03865d6783bab691fe403e2d2c612c888c2a3d39647bf2

Observation 162343e5-fdec-4c9c-ae9e-583fe6c9d36c · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 51

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no resolver link, observed 2026-08-07T15:21:07.899523Z

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source=arxiv_source observed=2026-08-07T15:21:07.899523Z digest=sha256:f54ce87b69d8029bed2ce8b45456fcea5507d551d4b71df3db15c5c84fe51809

Observation b0c334f9-a71c-4719-bc39-c65cc679c070 · outbound

This paper cites PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning

Reference 52

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no resolver link, observed 2026-08-07T15:21:07.985252Z

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source=arxiv_source observed=2026-08-07T15:21:07.985252Z digest=sha256:51c43c5042c005dbf0e29502db5bdb31008eae4adbb6d9c54ab27ade8b0302fb

Observation e4d64376-76fc-4b58-93b1-2a35352addf5 · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 53

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raw_fallback, observed 2026-08-07T15:21:09.049188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T15:21:08.048218Z digest=sha256:ae950971c6b7616ec9ad85d911470bec31daaf52a9735ac9e4b07e3e91e44103

Observation dede3df7-5bb3-4c2a-b94a-6c6bf08abb6e · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-07T15:21:08.112320Z digest=sha256:cc1292cbbec7f6b748b39566701895a14f1fb729379774ae51b74d681cf0c00a

Observation b0fcdb82-19b4-4dd5-b49d-b51c72537f1b · outbound

This paper cites GLM-130B: An Open Bilingual Pre-trained Model.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models GLM-130B: An Open Bilingual Pre-trained Model

Reference 55

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source=arxiv_source observed=2026-08-07T15:21:08.213812Z digest=sha256:e9baab8e20c49ea2c3c454ae1006d6fbaf25d254afb444b7174fed832287534d

Observation 0ac36fb2-ab8f-4be9-878b-df0d6c8add89 · outbound

This paper cites AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Reference 56

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source=arxiv_source observed=2026-08-07T15:21:08.281191Z digest=sha256:34928279cf7f156f97457e8c5364d1390de9214c598c045972032ae6e9c925c6

Observation a1a76945-8ea5-4931-b5ae-9afcd0d1d78b · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 57

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source=arxiv_source observed=2026-08-07T15:21:08.369899Z digest=sha256:7a9078562b8617db51822acf0f33edbf9c083cb7fbfb4c48f0a5e0b6c9b6e17c

Observation 671bbe9c-b429-4adf-8e61-d18d895d741c · outbound

This paper cites an unresolved cited work.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models Unresolved cited work

Reference 58

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source=arxiv_source observed=2026-08-07T15:21:08.464980Z digest=sha256:0a2b46fb09475ea0ff4f5f6388865f385ec48cd094264e9f54dcf02b8a1411f9

Observation 5ae7bb7b-10ed-462e-908c-fd816a7b30b4 · outbound

This paper cites DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models.

TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models

Reference 59

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no resolver link, observed 2026-08-07T15:21:08.559862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:08.559862Z digest=sha256:1b30be911260d5992ab63bd5a4d5aea33922aaa505684704ed74dae81fe16bd4

Pith citing papers

Observation 8a733963-de16-4af8-853e-52b0ee40ef37 · inbound

PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models cites this paper.

PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models

Reference 50

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verified exact
arxiv_id, observed 2026-05-21T18:00:27.293745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T17:57:57.263574Z digest=sha256:ada2058e0c03818b6c08e698bb2eb8d474c51a9789ca522c5210a4a0ae5c8e3e

Observation 92a540e8-24c7-4946-8124-959cd2389bb8 · inbound

From Pixels to Concepts: Do Segmentation Models Understand What They Segment? cites this paper.

From Pixels to Concepts: Do Segmentation Models Understand What They Segment? TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models

Reference 30

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arxiv_id, observed 2026-05-12T03:16:19.454729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T03:12:38.800119Z digest=sha256:f757913b340f42ac49b6a0ea48f3005d413650b81b589132bc44913f83c892a6

Observation 20fc5d2c-baf9-4347-b98b-1403ac5bea1f · inbound

YoCausal: How Far is Video Generation from World Model? A Causality Perspective cites this paper.

YoCausal: How Far is Video Generation from World Model? A Causality Perspective TimeCausality: Evaluating the Causal Ability in Time Dimension for Vision Language Models

Reference 116

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arxiv_id, observed 2026-06-29T08:33:15.609088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-29T08:27:03.674229Z digest=sha256:8789c98da0d4d36bc85a26d84e621fc4f0ad264a11f313c53c0f9fe9b723e664