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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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:dcc54b936eb835bad01e9048f188196e8c4f0b8776dd6ddff65e5a57779c90ba

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=sha256:6f1bdc95170b6de733d4690f2c0928846793fdcc1f38242e7e00ec6f2a6f542e

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:ea780dd57bf5385c11cf7ffbfe14a3b10b957a3b3b58ab348708d5d0108ab7b6

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.152467Z digest=sha256:5b177fc87daec54579a695729be2e842968f762e1eb05741e51f93a313c62b42

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.218499Z digest=sha256:ad8e3e792ed62bc7283f871d0dd11d3477d24384901607130cdddf1af51d6cde

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:fb7d0a70861d507a055997c5eb41ffebb516e0cdef39c05cdc8e62a0e7743e57

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=sha256:0268ddf5782ca0afa397ea75ab8987956fea7b82190e123fdeb173ab6a12b165

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=sha256:359de4731601c6686b00c329be6803030a2aa7f0bc9fa49791af8e47b580e640

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=sha256:49142fe9d5d3651359543ff67e48005df66cfae6a3379ce79e266647057d4aa4

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=sha256:21231b53d4603e576d0c533025541766e49918147fbc7cd476c6d58a880a4a40

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:02.924684Z digest=sha256:24865b68854e490258e5a4e4b4fda0bf4ca54f8ca50e98d1eb8641133f45318b

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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source=arxiv_source observed=2026-08-07T15:21:03.030057Z digest=sha256:7f112fabe6b34e51cd11c51385a44df9b95cd03705e4d77a3602b49d25686189

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:a6b7c5c33a8b0bdc537ddedb1fdc1336a91589de0c49bd16a6a3af134685d6db

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:b08a813adb20d84eb891ddcf75d619543dc212f58d7ea1dce23277023c061455

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:d123817c57f3607cd7d9826aede371fdb4a27767fbbc4666c1177b19607f356a

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:03.649848Z digest=sha256:c01e4e116ce75b5a63c6471b02e0129031dba0ccec90f615dbca17fb5c2f4de9

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=sha256:3b386915e6a87b76f4c59915e474f67b566968708cf85b82e2ec3167aee8fbee

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=sha256:091a1fc5da22a63cafa0815afd254e4285c8e6a59e94e485fd39bf973440c591

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=sha256:589699d22a2aa6ca9b3163763559d2f371c2f7d4bb6c72d0a805709ff5b6b29b

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:04.163938Z digest=sha256:d04d061477926c850fac7b4b435f8d0b29b70c72019d5c88e69d297de87ece2e

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=sha256:302e7bb5966609818d751d7bad4c546d7092f757dcf69a663003e4e89051361c

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:04.355966Z digest=sha256:cfcd3d2aee797e01121c343c64279f4b1244649a9419eece9b20c2aa847e6332

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=sha256:22e19f8314f2918e4e9c8e3c075accb32db90a067cbfc347b3374d995d366c4e

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=sha256:74bcb42c93b5632658ed792bd6db8802443636fcab513f3c09a47d790b7f77ea

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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source=arxiv_source observed=2026-08-07T15:21:04.740070Z digest=sha256:9e3bb31af802bb4d4ed11ebdcaa7df1ded53e17c179902f689601c18d2b392c2

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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source=arxiv_source observed=2026-08-07T15:21:04.768471Z digest=sha256:84125a79e3c50c85d39a2809aa9bf638fafb8ec8d7c959cd40286f653d369ecc

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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source=arxiv_source observed=2026-08-07T15:21:04.771148Z digest=sha256:0c0b470b356d684b65c3cc0715ab2a8c7f324631174c067285868a950f123c22

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:da9b3e756e5394d6b86cc3f34343abfdaa38bca9bf33c18a3229ae9e01818ede

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:dd223cd3b62c66d5bc768f2f68de81e33e64106f6d6d89e021949319f1e4a784

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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source=arxiv_source observed=2026-08-07T15:21:04.947994Z digest=sha256:9b1fcd6ffa2b1cf3cfc64b915dcd42e6a372957b3c41179d34ab09da42ce3736

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:05.036615Z digest=sha256:b8f64c9f5eaf17aed046b3230f6157e9e8937c07a697c53396479097ca619bb9

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.161689Z digest=sha256:7ff8eb80ba563e80b16a54a0fb3d65c6c235d93aa2cd37013f08694ee060f32e

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.246434Z digest=sha256:9e5da208ede0791eac2b0050584f99dfb7bd09f96325e438c3918038e9ab6aa0

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=sha256:07ab12983debba7545c5b59ffe7dc9a78ae4021b3270175e1d3ba21e286eadca

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=sha256:9848598de439f14d005867695e9b19bf5b800517abdb97042b8f5dc024deee94

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:cf6c876d65dd48c051753940e720127f18e0e0bfe29f49f1dd1657e49d632817

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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:05.853733Z digest=sha256:ebf1320ec3f00c55cc3ed388bd63124d3183d3b0a4ebc71a691eaf1420dfc6c1

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:05.940815Z digest=sha256:a2f954a3f9abd77932fa3c7b0c9b1bb78aa4bfa7065d2a87c226b11d739d83d4

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:c62650526689ce5034e61c88e85e2f682a101da0926cb43aced59652c67eaf6c

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:f927048276de0c5c60c13acf1a39d8a4df9b7a0928d7d426a54ec9b6550b1efb

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=sha256:742220c37ccda89aeef65ec0aa39898c25de35f9e50d77fb787a99477c3bf231

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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no resolver link, observed 2026-08-07T15:21:06.641219Z

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source=arxiv_source observed=2026-08-07T15:21:06.641219Z digest=sha256:561be5ac6647f81f8aff22a6989a0266a7a80376370de03e30a0b995573f7af6

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=sha256:1445411d61031c568b7c694a1f91fa0b17d1da6262ce808705beec50242ef5eb

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=sha256:0fc4420c0e7a3ae8dacb448ee674cece69be7ba0d310b255eb7765036fb6153b

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:e118eb4904a1781210b8c5134ad1a34e1a72e9086544633996fb41808c6f3745

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=sha256:3df61451f678a9deae3fb8d2eb9849fe1923dc5c9eac3d9e243cd1f7045b2e09

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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unresolved
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=sha256:7a64aa6aba834a6efe3fa04b9b46e789b0b086eb7a0a8d65d7b9fee4dab572d9

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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no resolver link, observed 2026-08-07T15:21:07.549870Z

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source=arxiv_source observed=2026-08-07T15:21:07.549870Z digest=sha256:891653e521540fa6eb16e2356c3fb32332f8342613c85b9ddcdb311a792092b7

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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no resolver link, observed 2026-08-07T15:21:07.684945Z

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source=arxiv_source observed=2026-08-07T15:21:07.684945Z digest=sha256:2517562b58f4c1282333aac64d0380630a4fa9d2ebe01893cc7938704940cfb1

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:b2bd9f2e40b9d2e01367f374b9128cc8cadcc4a745bf1bab7c7d938e3654fca0

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=sha256:715da19259ca65197d46965b32f5fd5e03739f17fac587e6c54d2f7b9ddbee4d

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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unresolved
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 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T15:21:08.048218Z digest=sha256:cc427fe82fb1063c53cbfbbc08f075981fc2ffc5ded071edd64b37d20027bcdc

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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no resolver link, observed 2026-08-07T15:21:08.112320Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:08.112320Z digest=sha256:5b3ab78b95c4eccec2e52da2b967a7a9e3c12d37a4b89efdb38f2e141d95453c

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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no resolver link, observed 2026-08-07T15:21:08.213812Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T15:21:08.213812Z digest=sha256:f01278c4692cdd4fb0cd2f2882cb79953c1a0dac85c87262582ca85fa1648b5d

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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no resolver link, observed 2026-08-07T15:21:08.281191Z

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source=arxiv_source observed=2026-08-07T15:21:08.281191Z digest=sha256:f5d8c95b584b64294d99ce63c40e17704e42ee7a973c0578e6e75b28a5c1c950

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=sha256:9897de479731df344a5d42572d96e5fabb80f3a111985d33e9f4f2a0653b3cd7

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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no resolver link, observed 2026-08-07T15:21:08.464980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:08.464980Z digest=sha256:687671592c410744479b2b32160cbea902adec61c75ad74049717225b07fe701

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

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:21:08.559862Z digest=sha256:6ff0f9eca41e95fffa7aa0c387b684ffbf57c336d2405c9836c5544f1a1edef4

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T17:57:57.263574Z digest=sha256:412fd348ed1950bb734bf820e0caecc780482a8fb179cea6578d7fece9e5ee93

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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verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T03:12:38.800119Z digest=sha256:531d9a04ea4b3c3cf8d6a294f2dcc697b0157279d18cdb643069233a688223c1

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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verified exact
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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T08:27:03.674229Z digest=sha256:f3e00b406a6b16f0befd8e08ad7dcb48617aa48a5ae408ef80ca2f4ce0cc7d9c