Paper Citation Record · LEDGER

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

As of 13 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 3 inbound Pith citation observations for arXiv:2605.18984.

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

pith.paper-citation-record.v1
2605.18984 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-20T10:26:30.661042Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:38:48.378542Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

verified exact20
verified fuzzy21
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c54c235c-aa0d-4b82-a2f6-ba2e812783fb · outbound

This paper cites Qwen3-VL Technical Report.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Qwen3-VL Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.010636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b4db979-b290-4c2d-9222-bc8a0575823b · outbound

This paper cites Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.004383Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 55d00733-8c81-4ce3-96a9-2f0e7bf1b5a8 · outbound

This paper cites Avocado: An audiovisual video captioner driven by temporal orchestration.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Avocado: An audiovisual video captioner driven by temporal orchestration

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.986376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 433a8555-43f4-4252-8770-fc814bdbf8f6 · outbound

This paper cites Versavid-r1: A versatile video understanding and reasoning model from question answering to captioning tasks.arXiv e-prints, pages arXiv–2506.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Versavid-r1: A versatile video understanding and reasoning model from question answering to captioning tasks.arXiv e-prints, pages arXiv–2506

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.819989Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cfee9ff1-bbe8-44ac-b72d-0ad9d2a8fbd5 · outbound

This paper cites Opengpt-4o-image: A compre- hensive dataset for advanced image generation and editing.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Opengpt-4o-image: A compre- hensive dataset for advanced image generation and editing

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.979112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 394b8485-04cd-47da-878f-2004801b393e · outbound

This paper cites EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:12.023952Z

Source-reported events for the cited work

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Observation d26e1dce-6d9b-4a8e-a523-405ef12cdf17 · outbound

This paper cites Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.007859Z

Source-reported events for the cited work

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Observation 01a12773-02d4-467a-8a96-15ed311cadc4 · outbound

This paper cites Gemini 3.1 pro.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Gemini 3.1 pro

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.823715Z

Source-reported events for the cited work

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Observation a0b33ede-92e5-41ca-9f3d-1a3c5a793172 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.829532Z

Source-reported events for the cited work

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Observation 4f6e236f-b176-4518-8f24-8972be9d9b88 · outbound

This paper cites Gemini 3 flash.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Gemini 3 flash

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.832997Z

Source-reported events for the cited work

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Observation cb65038c-e526-40f0-889e-439c846cac38 · outbound

This paper cites LTX-2: Efficient Joint Audio-Visual Foundation Model.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos LTX-2: Efficient Joint Audio-Visual Foundation Model

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.020869Z

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Observation 2ace0ac1-81cd-473f-a744-b365884927b8 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:11.982339Z

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Observation 8f3bf829-9786-4db9-a05d-5b830f4de318 · outbound

This paper cites Kling ai: Video generation model.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Kling ai: Video generation model

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.842277Z

Source-reported events for the cited work

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Observation 2bd2e945-f692-4292-a725-af2ad7fa3a90 · outbound

This paper cites Aegis: Authen- ticity evaluation benchmark for ai-generated video sequences.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Aegis: Authen- ticity evaluation benchmark for ai-generated video sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.844192Z

Source-reported events for the cited work

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Observation 4be6c718-9cf5-4d22-bb4c-33e8b720ea27 · outbound

This paper cites Skyra: Ai- generated video detection via grounded artifact reasoning.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Skyra: Ai- generated video detection via grounded artifact reasoning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.811885Z

Source-reported events for the cited work

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Observation 78962e4b-aeb3-450e-b542-673c0d2a190e · outbound

This paper cites Uve: Are mllms uni- fied evaluators for ai-generated videos?.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Uve: Are mllms uni- fied evaluators for ai-generated videos?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.996389Z

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Observation 2b7562be-2acc-4499-83b1-49852ba058c8 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 17

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Observation 0a83a8b0-a9c0-4446-af5c-4fbb453d8e70 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.810081Z

Source-reported events for the cited work

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Observation 9fe150a7-0b48-409e-b839-c4f618194c9f · outbound

This paper cites Mavors: Multi-granularity video representation for multimodal large language model.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Mavors: Multi-granularity video representation for multimodal large language model

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.808133Z

Source-reported events for the cited work

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Observation 5d48d627-0175-41f5-b314-8ad679245fc1 · outbound

This paper cites Mme-videoocr: Eval- uating ocr-based capabilities of multimodal llms in video scenarios.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Mme-videoocr: Eval- uating ocr-based capabilities of multimodal llms in video scenarios

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.802288Z

Source-reported events for the cited work

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Observation dcfa2802-3bdf-45df-ac1e-62c46d228df6 · outbound

This paper cites Speed by simplicity: A single-stream architecture for fast audio-video generative foundation model.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Speed by simplicity: A single-stream architecture for fast audio-video generative foundation model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.804205Z

Source-reported events for the cited work

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Observation fa0f5961-b3fe-41b5-a6f3-c23a613f6d85 · outbound

This paper cites Vf- eval: Evaluating multimodal llms for generating feedback on aigc videos.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Vf- eval: Evaluating multimodal llms for generating feedback on aigc videos

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.813677Z

Source-reported events for the cited work

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Observation 6e1b667b-4bb8-4bad-8ab2-4af64a44ae2d · outbound

This paper cites Videoveritas: Ai-generated video detection via perception pretext reinforcement learning.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Videoveritas: Ai-generated video detection via perception pretext reinforcement learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.993113Z

Source-reported events for the cited work

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Observation 76d749ad-9ae2-4d4a-b4d8-8a6ff74034dd · outbound

This paper cites Kwai keye-vl 1.5 technical report.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Kwai keye-vl 1.5 technical report

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.796201Z

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Observation 358803e5-c1bc-437e-b2ce-0f44f1a9d115 · outbound

This paper cites Hunyuanvideo 1.5 technical report.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Hunyuanvideo 1.5 technical report

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.798115Z

Source-reported events for the cited work

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Observation 7821b086-3f33-44f9-9363-01f35282355c · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Wan: Open and Advanced Large-Scale Video Generative Models

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:11.999686Z

Source-reported events for the cited work

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Observation c054fbb0-1c2e-4c77-9755-2156af800524 · outbound

This paper cites Geollava-8k: scaling remote-sensing multimodal large language models to 8k resolution.Ad- vances in Neural Information Processing Systems, 38:159185– 159218.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Geollava-8k: scaling remote-sensing multimodal large language models to 8k resolution.Ad- vances in Neural Information Processing Systems, 38:159185– 159218

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.794389Z

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Observation 6c660d42-9de4-4463-8d8b-2db23d06d843 · outbound

This paper cites Geoeyes: On-demand visual focusing for evidence-grounded understanding of ultra-high-resolution re- mote sensing imagery.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Geoeyes: On-demand visual focusing for evidence-grounded understanding of ultra-high-resolution re- mote sensing imagery

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:12.014486Z

Source-reported events for the cited work

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Observation bcb3a668-91c0-47f6-ae8f-d56a6aec417a · outbound

This paper cites Text before vision: Staged knowledge injection matters for agentic rlvr in ultra-high- resolution remote sensing understanding.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Text before vision: Staged knowledge injection matters for agentic rlvr in ultra-high- resolution remote sensing understanding

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:12.017696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2947fca7-017c-47c7-bc4d-7890215934b6 · outbound

This paper cites Monet: Reasoning in latent visual space beyond images and language.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Monet: Reasoning in latent visual space beyond images and language

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.792418Z

Source-reported events for the cited work

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Observation 07399145-4c41-4ab1-9de0-9dcbbea2c3c0 · outbound

This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.027068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation efd6b785-5987-4978-abc2-6bfdb1e417ec · outbound

This paper cites BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-19T17:09:47.379931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d94a8963-4adb-4f9c-9dfc-738f8f039e52 · outbound

This paper cites BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-06-19T17:09:48.005747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c85ea067-c67e-40f8-badf-372a7eb483ff · outbound

This paper cites Mimo-vl technical report.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Mimo-vl technical report

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.800046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1f7175d7-a71c-4471-afa6-cbe439c07696 · outbound

This paper cites MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-20T10:28:12.033963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 003a6872-151c-417c-bad2-2e357295fe6b · outbound

This paper cites Debiasing multimodal large language models via penal- ization of language priors.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Debiasing multimodal large language models via penal- ization of language priors

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.790590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b80cfbf5-0edc-4e68-925f-24735fec3fbd · outbound

This paper cites MM-RLHF: The Next Step Forward in Multimodal LLM Alignment.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos MM-RLHF: The Next Step Forward in Multimodal LLM Alignment

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.975833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7b2ad82f-1f47-4297-86af-b1de134c1c45 · outbound

This paper cites When modalities conflict: How unimodal reasoning uncertainty governs preference dynamics in mllms.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos When modalities conflict: How unimodal reasoning uncertainty governs preference dynamics in mllms

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.968875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5e6778a8-eb85-4923-aac9-2454dc46c1f3 · outbound

This paper cites yes". Otherwise, classify it as.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos yes". Otherwise, classify it as

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:28:11.989221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d245eecf-0e62-4c02-910c-8ccef921aaf2 · outbound

This paper cites yes" - "no.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos yes" - "no

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.840474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dcc9735f-ad26-4281-8811-38260b06d7b9 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.836821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4ef0210c-e7a1-40e3-a9e9-f38ed2fac0fa · outbound

This paper cites <Video A>.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos <Video A>

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.847966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 43d3b967-9c76-47a3-b12a-e7c74371910e · outbound

This paper cites <Video A>.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos <Video A>

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.831285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 45b0b97c-bb0a-49b7-8a99-3c71171728ca · outbound

This paper cites The original task is to identify all AIGC-specific artifacts that are clearly observable in a given AI-generated video.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos The original task is to identify all AIGC-specific artifacts that are clearly observable in a given AI-generated video

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.834828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0b5662b-787a-4dfb-9e95-45a0905d7449 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.838696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c845e693-7f94-4c11-a13f-43060ffe83da · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.845958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 72b908b0-3571-4c7b-8a30-0846551b71b8 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.827533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0a030f72-7661-4daf-9d66-14631e747e03 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.821935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4c3a405a-2f54-47d2-9f76-4acc5bf60a41 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.825698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 010d97f5-6844-4aa6-b2d1-dd89e4445c86 · outbound

This paper cites an unresolved cited work.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-20T10:28:12.815600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 498bf751-7ee0-4e2f-a4f5-5130285fc5a6 · outbound

This paper cites yes" if the video is AIGC, and.

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos yes" if the video is AIGC, and

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T10:28:12.818053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 473c8604-bbe8-46ef-9bd6-7289835f4d84 · inbound

G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement cites this paper.

G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-02T08:38:48.378542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 876e8a13-a802-409f-99dc-de6a84439464 · inbound

Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream cites this paper.

Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T12:46:13.187376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1320625-5011-40a3-83c6-97bb1683d67f · inbound

RefCaptioner: Multi-Reference Image-Grounded Video Captioning cites this paper.

RefCaptioner: Multi-Reference Image-Grounded Video Captioning Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T05:08:19.981955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.