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Paper Citation Record · LEDGER

CinePile: A Long Video Question Answering Dataset and Benchmark

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2405.08813.

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

pith.paper-citation-record.v1
2405.08813 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 33 of 33 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 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:50:03.465772Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:39:58.360487Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 43df5068-94b9-4c18-8e5f-65237f2ace4d · inbound

LVBench: An Extreme Long Video Understanding Benchmark cites this paper.

LVBench: An Extreme Long Video Understanding Benchmark CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 31

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arxiv_id, observed 2026-05-19T11:55:30.092244Z

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.

source=pdf_text observed=2026-05-19T11:55:30.048525Z digest=sha256:4ddceac29939dfc3e33c1277794bf9a0102c3dbc85ca1503c60774fb8cbd1465

Observation f9b9e478-9dbf-4b84-83e1-faebb67969f9 · inbound

VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling cites this paper.

VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 43

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arxiv_id, observed 2026-05-18T04:02:43.474946Z

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.

source=pdf_text observed=2026-05-18T04:02:43.261543Z digest=sha256:c8d240e2cd4c60893691127c1a076fa7b5505ca813d5289546bd10cf706e27f7

Observation 5dabb5b7-c2a8-4ec9-8511-aa8ed3599b17 · inbound

LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding cites this paper.

LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 58

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arxiv_id, observed 2026-05-23T06:02:37.664985Z

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.

source=pdf_text observed=2026-05-23T06:01:00.775721Z digest=sha256:171d1311ee7b906ee6eb474a64e62069c14133fe2e612fd7100f08a191080eb1

Observation ef357ce6-3b40-45fc-a870-f1986d9cc78c · inbound

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

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 99

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verified exact
arxiv_id, observed 2026-05-11T01:19:59.716306Z

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.

source=pdf_text observed=2026-05-11T01:19:59.603343Z digest=sha256:ee33dd432cea6e95916e1f1f1e9a27cc4f09d918bb4e98223f1a21b9033f8a76

Observation b2beb961-e52e-4d7a-b0c9-53ffdea1b977 · inbound

ScaleLong: A Multi-Timescale Benchmark for Long Video Understanding cites this paper.

ScaleLong: A Multi-Timescale Benchmark for Long Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 21

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no resolver link, observed 2026-08-07T12:40:45.721408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:40:45.721408Z digest=sha256:b5a6986f7e3dbf75f76d5ec72faaeca61276144e01ee70d1a98f038711f1aa03

Observation e84b1fe9-e717-4a68-9c0a-7c2ab8456258 · inbound

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding cites this paper.

ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 39

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unresolved
no resolver link, observed 2026-08-07T11:51:28.722460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:28.722460Z digest=sha256:5f6a17b3351a7ce79a7bb9c0868e26b1ef59e1c3819e30b1143cbe85f0d74c46

Observation b5a9eed2-063f-4369-83c6-f8cdb5c0e94f · inbound

Vid-SME: Membership Inference Attacks against Large Video Understanding Models cites this paper.

Vid-SME: Membership Inference Attacks against Large Video Understanding Models CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 37

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no resolver link, observed 2026-08-07T12:50:03.465772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:50:03.465772Z digest=sha256:e0991783ae59beb43147b8f694f81dbfd5149af098509fe52fc9ba08cfb66a36

Observation f00b55dd-fc36-4de5-9f00-9b8018792999 · inbound

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning cites this paper.

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 40

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verified exact
arxiv_id, observed 2026-05-19T11:37:15.669903Z

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.

source=pdf_text observed=2026-05-19T11:36:36.687324Z digest=sha256:bf69ecaaa435f8ffd21b0a1df475e16407faed20de321a34f5beab98d61620f5

Observation 13e2c0fb-30ac-409e-9f52-7726c30ad6ae · inbound

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding cites this paper.

Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 44

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no resolver link, observed 2026-08-07T06:00:56.954513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:00:56.954513Z digest=sha256:ee7221f1fada28937cd1e583ab5057e71e3b98de25f1eb6ea849f27847061916

Observation a55a95d4-5a08-4bdb-a540-3c8398416327 · inbound

MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks cites this paper.

MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 13

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no resolver link, observed 2026-08-07T05:49:53.273176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:49:53.273176Z digest=sha256:58c1242878169b35766c1d9a5d2cc79fb78a0b95638aed710a04b9cfc561b7bf

Observation c327895c-27f6-407b-a9e4-89b92372a249 · inbound

ARGUS: Hallucination and Omission Evaluation in Video-LLMs cites this paper.

ARGUS: Hallucination and Omission Evaluation in Video-LLMs CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 48

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no resolver link, observed 2026-08-07T05:41:39.746918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:41:39.746918Z digest=sha256:c50b4a52d43cbe4d17444e9b2d39e961f3202e0af9ba70bf57eebfbd667759a4

Observation bf3a7020-ca82-45ec-a2de-6bcdbce815e5 · inbound

Ming-Omni: A Unified Multimodal Model for Perception and Generation cites this paper.

Ming-Omni: A Unified Multimodal Model for Perception and Generation CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 28

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no resolver link, observed 2026-08-07T04:58:09.654968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:58:09.654968Z digest=sha256:064006636d87aa8a5cd6d507415a5201cc337c092202cfcd3609db88823af227

Observation e967bfc9-8471-4deb-8a98-736eb0df26c4 · inbound

CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models cites this paper.

CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 32

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no resolver link, observed 2026-08-07T04:45:01.238009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:45:01.238009Z digest=sha256:e7afeaa40fd3b1f3ebf2d2a67cb887f4b54cb042cab36dd39860551349c068a1

Observation 2bce9224-a4cb-41a3-853a-88f33fd1a58b · inbound

VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos cites this paper.

VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 53

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no resolver link, observed 2026-08-07T04:22:55.969084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:22:55.969084Z digest=sha256:6c061d17f78335fb9ba890a0d0cc2ce12d361b4626490b8433055a6b6125ace2

Observation b7754035-5226-457d-a5d6-8fd86eda5e15 · inbound

LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation cites this paper.

LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 52

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no resolver link, observed 2026-08-06T23:42:13.831606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:42:13.831606Z digest=sha256:235af35272423f52b40a3c8e5ec893cb74a14a9d3966fc8c2f42f2c548a92c36

Observation 69c68b6f-8159-423e-891e-37fa0df608fe · inbound

MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering cites this paper.

MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 23

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no resolver link, observed 2026-08-06T23:29:24.253829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:29:24.253829Z digest=sha256:38f6f941b703d19b119bcf400ebfc50f157cebef90b05fa3d530f221fdf2e5e8

Observation 5874399a-af36-4487-a27e-be0706e94262 · inbound

AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning cites this paper.

AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 5

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verified exact
arxiv_id, observed 2026-05-17T20:20:11.806042Z

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.

source=pdf_text observed=2026-05-17T20:18:19.580156Z digest=sha256:222f07b8800ecb5540f098058d927e9a57abbf9b88aef9e553faa0e0727262ec

Observation 7a6d0514-8517-4d50-b4ce-a376a0e6a49a · inbound

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

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 123

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arxiv_id, observed 2026-05-16T04:21:29.765977Z

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.

source=pdf_text observed=2026-05-16T04:21:29.526008Z digest=sha256:0868a56c2e93d8131f9c60e2a436de1667ddce7af98defe6bd4063035f0b0b54

Observation b71dd5a6-0085-4564-9835-e73ec935a775 · inbound

POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs cites this paper.

POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 66

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arxiv_id, observed 2026-05-11T10:41:03.971483Z

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.

source=pdf_text observed=2026-05-10T15:23:08.671342Z digest=sha256:e107aa66dabbed22b2de8ec42389cba32cee0900d56454ada02729ac2e6418e5

Observation 0d59c559-8d60-46a4-a31e-61588e821c5d · inbound

TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos cites this paper.

TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 73

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verified exact
arxiv_id, observed 2026-05-11T04:15:56.091351Z

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.

source=pdf_text observed=2026-05-11T01:53:01.939765Z digest=sha256:41d7099e832ef1b98e8b6eee88886074fb7239bb762f21013ca1557a213c20a4

Observation c756b4a3-52a9-4442-9fb9-d230b541a974 · inbound

Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding cites this paper.

Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 37

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arxiv_id, observed 2026-05-19T16:03:08.077787Z

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.

source=pdf_text observed=2026-05-19T16:02:53.887605Z digest=sha256:ea32f3fa3375801553e98d2723652c4c3720fd8b208ca2a2409c30244c3ce1a2

Observation 8b2b92ad-b280-454c-816d-b0984260e524 · inbound

An Attribute-Based Measure of Video Complexity cites this paper.

An Attribute-Based Measure of Video Complexity CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 41

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arxiv_id, observed 2026-06-28T19:02:34.112901Z

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.

source=pdf_text observed=2026-06-28T19:00:54.718177Z digest=sha256:f4e82144754d21c5cdc1ecabe5b67ef9b0fbcdc68eef7994f7202e936c99d341

Observation d5ef4b2a-3c61-4236-8094-edfdcae0ee85 · inbound

VidMsg: A Benchmark for Implicit Message Inference in Short Videos cites this paper.

VidMsg: A Benchmark for Implicit Message Inference in Short Videos CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 32

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arxiv_id, observed 2026-07-02T02:56:30.072053Z

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.

source=pdf_text observed=2026-06-28T10:25:06.594946Z digest=sha256:7992a7f2b38787eaaf64d19a9c3a0dad6ae9576a3efb3917772f06341151dc55

Observation de21a677-beb4-497e-9fec-9d79c92fbe3d · inbound

StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset cites this paper.

StoryVideoQA: Scaling Deep Video Understanding with a Large-Scale, Multi-Genre and Auto-Generated Dataset CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 25

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arxiv_id, observed 2026-07-02T12:36:56.175154Z

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.

source=pdf_text observed=2026-06-28T02:05:47.810096Z digest=sha256:48cb0b7a62dacaa6fde4cf1b274e684e612ee9dd83c4f77a4cca8a513a02be33

Observation ea758d1e-6bc5-4a94-be91-d8e6a4490dcd · inbound

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning cites this paper.

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 298

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arxiv_id, observed 2026-07-03T10:48:03.100934Z

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

source=arxiv_source observed=2026-06-27T09:48:27.652901Z digest=sha256:5623a2d2ccedd93b5883d885690b9dee74c05723258a0e77bc281dcb7a6f2e61

Observation 26eb1072-5e44-415f-8087-f415ad638a38 · inbound

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding cites this paper.

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 48

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arxiv_id, observed 2026-07-04T16:39:58.362180Z

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.

source=pdf_text observed=2026-06-26T00:19:26.153682Z digest=sha256:62a1aada3b87aaa9204340d2d79cfb8639a80407599fcdc5cb4dc422bb96710f

Observation ea3ab46a-aa52-49e5-a6e4-b087c3833377 · inbound

Video-MME-Logical: A Controlled Diagnostic Benchmark for Video Temporal-Logical Reasoning cites this paper.

Video-MME-Logical: A Controlled Diagnostic Benchmark for Video Temporal-Logical Reasoning CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 14

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metadata mismatch
arxiv_id, observed 2026-06-29T19:13:52.928268Z

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.

source=arxiv_source observed=2026-06-29T04:53:25.259840Z digest=sha256:5ca4054c4e8507924ae1b475d8fff516e2a1b3c7c09d41e4a5a5ff2a101eeb5f

Observation bcf308be-2c98-45ea-9815-632c2360ed4f · inbound

LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension cites this paper.

LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 37

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arxiv_id, observed 2026-07-03T15:48:35.009088Z

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.

source=pdf_text observed=2026-07-03T15:41:03.548799Z digest=sha256:df49e4d7f380b1d8a1de11719b5bf9b6907ec687eddfdbe59471241fab66d46c

Observation 63fe83c7-4305-4236-8c2d-31689e10f41c · inbound

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding cites this paper.

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 51

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no resolver link, observed 2026-08-02T00:44:45.027159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T00:44:45.027159Z digest=sha256:b2b561c1eeeb49728492138c9dc7a365b4c319ffea695335b06838cee3c53c22

Observation ad1eaa7f-5f72-46f1-90ab-976ec2efaa86 · inbound

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model cites this paper.

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 56

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no resolver link, observed 2026-08-01T16:32:51.325578Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:32:51.325578Z digest=sha256:f20c90a35497b44165bdfab2eca873d4b03a062cfb156966e449baba94ca10d5

Observation a3e89658-a477-4b0e-8a6a-13ebfc5b7c3a · inbound

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model cites this paper.

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 56

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no resolver link, observed 2026-08-03T01:57:33.221572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T01:57:33.221572Z digest=sha256:25a5b1eb923d6fa026c08dc8902341d8d51f56aab81a7f57940db9b96c6ed92d

Observation 9b02b645-9ae3-4301-bf5b-badd6dea3b76 · inbound

Reading Between the Frames: Interpreting Implicit and Non-literal Meaning in Social Media Videos cites this paper.

Reading Between the Frames: Interpreting Implicit and Non-literal Meaning in Social Media Videos CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 37

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no resolver link, observed 2026-08-06T13:26:49.688571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T13:26:49.688571Z digest=sha256:d512d37af30c443c4677ca0c77efefe037e3d86dea8152733646ca4fbb2ea5e8

Observation 44b4a782-ef62-4217-b57a-179974c28124 · inbound

The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping cites this paper.

The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping CinePile: A Long Video Question Answering Dataset and Benchmark

Reference 74

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no resolver link, observed 2026-08-07T04:24:54.985958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:24:54.985958Z digest=sha256:a5e91d353ae7e582a9c0c51ff24778c0b5e1247c607bd1a1be59511ba5479e65