Paper Citation Record · LEDGER

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos

As of 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2505.23693.

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

pith.paper-citation-record.v1
2505.23693 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:50.619699Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T14:51:33.951351Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:56:20.413167Z

Reference resolution

63 of 63 outbound references displayed

verified exact6
verified fuzzy1
unresolved56
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e62cd0b2-3720-49ec-8dea-00813429eb42 · outbound

This paper cites URL: " 'urlintro :=.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos URL: " 'urlintro :=

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:43.741569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3ec7d4b-9381-4207-8114-bb0c96a8d985 · outbound

This paper cites write newline.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:43.846912Z

Source-reported events for the cited work

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Observation 3d689502-d095-41c7-a03b-c2b128bf3b9f · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:43.962180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 533b0339-a776-4d3c-8466-f712a75eaeb6 · outbound

This paper cites Qwen2.5-VL Technical Report.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Qwen2.5-VL Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.067256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abf7a0ea-213f-4d0d-87c1-6d9500376319 · outbound

This paper cites VideoPhy: Evaluating Physical Commonsense for Video Generation.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VideoPhy: Evaluating Physical Commonsense for Video Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.170543Z

Source-reported events for the cited work

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Observation 939add0e-cde7-4d97-a8f7-4d8a88594e64 · outbound

This paper cites Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:45:52.476729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5872b0d7-1f29-43f8-805d-5331c4a711d8 · outbound

This paper cites ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.438501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48ded090-d928-45ea-8b60-f677fc3e38e5 · outbound

This paper cites AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.538343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 931fd778-7db9-4656-9d6d-7386d5d7549f · outbound

This paper cites The Llama 3 Herd of Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos The Llama 3 Herd of Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.679138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3000991f-59c4-4821-afbc-381b73476d7a · outbound

This paper cites AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:45:52.259109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b773435-f45d-44d3-a2a5-28ef34363b23 · outbound

This paper cites MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:44.920605Z

Source-reported events for the cited work

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Observation 6db424d1-fead-4382-9b1b-eaac116a4e15 · outbound

This paper cites Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.015168Z

Source-reported events for the cited work

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Observation c70e6167-669e-4b4c-a7cd-9d330fad562b · outbound

This paper cites LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.113785Z

Source-reported events for the cited work

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Observation 2c6f8ff8-ad4f-4412-a6ae-31560ec03131 · outbound

This paper cites Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.265067Z

Source-reported events for the cited work

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Observation e3340e9e-7745-407d-bb4a-6f16a6251130 · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.379677Z

Source-reported events for the cited work

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Observation 957dce73-1c6d-42dc-be04-396c265a6aa9 · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.475594Z

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Observation 32257275-7bc1-4355-b316-0d74ce9e0869 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:55.433502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8a0adae8-f90e-48c0-a83b-bca2917ef604 · outbound

This paper cites Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.740034Z

Source-reported events for the cited work

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Observation 768a3bfd-c353-44fc-8570-88a19169d001 · outbound

This paper cites MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.857292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddb0f8d9-ba86-4eaa-bdb9-be3a7b772af0 · outbound

This paper cites Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:45.930805Z

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Observation bdd63013-ba0d-4a87-a072-49ed107d3cec · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:55.208825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8046bc62-217f-40d9-83c9-4fe1946366d6 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.117617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4d8b984-834c-40af-b9a4-eb29c5ee3c72 · outbound

This paper cites VideoPoet: A Large Language Model for Zero-Shot Video Generation.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.176451Z

Source-reported events for the cited work

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Observation e423f4ba-9fef-4bd5-ac1a-47c6a316cf4e · outbound

This paper cites Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.236243Z

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Observation f38dbbaa-c200-4890-8354-1c0c167ceb48 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos LLaVA-OneVision: Easy Visual Task Transfer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.299347Z

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Observation 370c580d-0a04-4306-8fb4-1f918c5537d6 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.417128Z

Source-reported events for the cited work

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Observation cfb42be7-2600-43d1-bc3f-806577ce11ec · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VideoChat: Chat-Centric Video Understanding

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.512570Z

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Observation 5c3f3fc5-4b05-4dee-a396-5883fe2c7ab8 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:54.892978Z

Source-reported events for the cited work

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Observation 33b32872-3a47-4d49-9582-00ed23d0e82a · outbound

This paper cites VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.762139Z

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Observation f4e62474-1e58-4261-b56c-11dbe3e6481f · outbound

This paper cites VideoVista: A Versatile Benchmark for Video Understanding and Reasoning.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VideoVista: A Versatile Benchmark for Video Understanding and Reasoning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.866771Z

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Observation 25a57ab5-a095-4b2e-9d00-571c75d43ef8 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:46.934791Z

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Observation 8c73fc07-fd34-401a-aeef-2da79402d7d8 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 32

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This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 33

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Observation da49f215-98c6-4595-9b16-c524a000d953 · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:54.420036Z

Source-reported events for the cited work

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Observation c8a1f739-ef73-45c0-ab0a-7aa86a36405f · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos TempCompass: Do Video LLMs Really Understand Videos?

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.315409Z

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Observation e30ebd95-91dd-4632-90e8-d9560d30dc93 · outbound

This paper cites Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.443705Z

Source-reported events for the cited work

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Observation 39f30c25-7fce-4277-af82-32f295dca67d · outbound

This paper cites Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.596974Z

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Observation 061ae997-6995-458a-a16a-dfbbb2036e48 · outbound

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:54.169749Z

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Observation e79e1b7e-d904-4d9e-bac3-d687bd900045 · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.827826Z

Source-reported events for the cited work

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Observation c56c653a-e8bf-4d33-b1b4-46ccdbe8278c · outbound

This paper cites GPT-4 Technical Report.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos GPT-4 Technical Report

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:47.934620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fded728-4d52-4a7c-a247-77c5221686b7 · outbound

This paper cites Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:45:51.709687Z

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Observation c5774b23-4dd8-4aa4-87d0-f249c70fb3ba · outbound

This paper cites an unresolved cited work.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:53.980201Z

Source-reported events for the cited work

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Observation 51d896f8-66df-4027-9c6e-e48beb66ab3b · outbound

This paper cites TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.261199Z

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Observation 1d1b9025-dfa8-4623-ab78-e404bc7a46e3 · outbound

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos 2020-2024

Reference 44

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Observation ed4ca545-399d-4284-9814-4c6876c2381b · outbound

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 45

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Observation 9692c2c6-a8e4-4a3b-9d0d-22b165b52e3c · outbound

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 46

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Source-reported events for the cited work

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Observation bd778fa0-cc31-4ff2-af7b-a704eecad666 · outbound

This paper cites Camera-Based HRV Prediction for Remote Learning Environments.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Camera-Based HRV Prediction for Remote Learning Environments

Reference 47

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Observation abc5d636-b7bd-4bfe-af42-a838d218fe02 · outbound

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

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.866290Z

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Observation 7e315a08-adef-4e27-bc2d-17cfe2d36839 · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos CogVLM: Visual Expert for Pretrained Language Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.950196Z

Source-reported events for the cited work

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Observation d1939357-4ace-45e9-b8e8-8547e57afecb · outbound

This paper cites LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.003746Z

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Observation 01ef8c20-4f38-45d4-ac7f-d997d7992b77 · outbound

This paper cites GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:45:51.382028Z

Source-reported events for the cited work

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Observation f020b70c-87f6-44bb-9d4e-21811f81ec82 · outbound

This paper cites Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.132169Z

Source-reported events for the cited work

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Observation c1df389f-4589-48c7-a8a1-380212b6e94c · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 53

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 54

Resolution: verified exact; doi, observed 2026-08-07T12:45:50.863614Z

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This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.396161Z

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This paper cites Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:45:51.193106Z

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This paper cites Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.660056Z

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Observation 3f443b40-92ff-4dd4-ad46-dac628dbf559 · outbound

This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.782603Z

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 59

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 60

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This paper cites MMVU: Measuring Expert-Level Multi-Discipline Video Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos MMVU: Measuring Expert-Level Multi-Discipline Video Understanding

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:50.222550Z

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VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:52.674815Z

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This paper cites MLVU: Benchmarking Multi-task Long Video Understanding.

VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos MLVU: Benchmarking Multi-task Long Video Understanding

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:50.619699Z

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Pith citing papers

Observation eb3d2990-9dde-45f9-a425-9dcaa0fb0205 · inbound

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation cites this paper.

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:56:20.415369Z

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