Pith. sign in

Paper Citation Record · LEDGER

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

As of 8 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 15 inbound Pith citation observations for arXiv:2506.01300.

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

pith.paper-citation-record.v1
2506.01300 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:52:06.520392Z

measured 91 of 91 standing notices

One-hop event checks from named stored sources.

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

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:39.441018Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:39:57.778113Z

Reference resolution

76 of 76 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved58
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1be81308-c01c-4042-be40-b5813409f28b · outbound

This paper cites Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Flamingo: a visual language model for few-shot learning.Advances in neural information processing systems, 35:23716–23736, 2022

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:57.632772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:57.632772Z digest=sha256:91dceccdb6f831ce15128a2f990e15f1b4975c8bb0e308be342d9cdd223ca2b5

Observation a0f71f7c-9b15-4464-a25f-8382a351b257 · outbound

This paper cites Qwen2.5-VL Technical Report.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Qwen2.5-VL Technical Report

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:58.310770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:58.310770Z digest=sha256:62fdb2515c90567a4e05f527d504dfedb3077cbb9aebc19e74112fa501a2b3c7

Observation 102253c2-96a0-417c-b972-7db43a0eb69c · outbound

This paper cites LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:59.026187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:59.026187Z digest=sha256:bc5eadffe4631320ca6153dc65aea92d0377aa8deacdc496c5c2ce7e4452140e

Observation fb54b947-ccfe-448b-aab7-7f1a68fcba3c · outbound

This paper cites Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Sharegpt4video: Improving video understanding and generation with better captions.Advances in Neural Information Processing Systems, 37:19472– 19495, 2024

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:59.236952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:59.236952Z digest=sha256:1195200be80091e9fbe501351667887274c409d901783841f6bf045817f3294d

Observation b65b9093-4a76-49dc-8d33-1b46a11cc01e · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:59.500410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:59.500410Z digest=sha256:944209e6589f2e055c2b8dc77e35d849ad1a49d4ec87adaea57bb12038e68673

Observation 4b433ea6-da38-41d7-b39f-4361c179682e · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T11:51:59.788980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:51:59.788980Z digest=sha256:f80ddc7385213cab442e6bc5687dfc43bffa6bf3012ae449d346d79a05a040c5

Observation 39ca27fe-336c-4bf1-9fee-22ced1e2a5ba · outbound

This paper cites Supervised learning.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Supervised learning

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.878162Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:00.071530Z digest=sha256:528ed41ba10a5ebefd388759b029643fc03cd8678bd481b6e9cc4dcf397fb65a

Observation df18337e-b269-4427-8dc4-c5eb2a5fbe45 · outbound

This paper cites A long video caption generation algorithm for big video data retrieval.Future Generation Computer Systems, 93:583–595, 2019.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding A long video caption generation algorithm for big video data retrieval.Future Generation Computer Systems, 93:583–595, 2019

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.791899Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:00.521486Z digest=sha256:fbeb6e798949cec34279f87d46cd3a91628c43794958cf3f46823db965db8f99

Observation 6a1f81c9-dcca-470c-b5b3-8940cb5b5fc6 · outbound

This paper cites Videoagent: A memory-augmented multimodal agent for video understanding.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Videoagent: A memory-augmented multimodal agent for video understanding

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:01.205243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:01.205243Z digest=sha256:6355525ebc8039474579bc3a8bef54937564d79f25225da78873feb7c7f493d0

Observation c72fa3eb-90b6-481b-a9bb-877c4d8f6149 · outbound

This paper cites Video-R1: Reinforcing Video Reasoning in MLLMs.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-R1: Reinforcing Video Reasoning in MLLMs

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:01.830424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:01.830424Z digest=sha256:6c927cbf01c38328b8af6eb3cf1346fae9f6cf9a62e364434bf4eeceda390386

Observation 3c70e8a3-ab42-4577-8f51-b5a8019f0ec3 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:01.912701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:01.912701Z digest=sha256:a95bc443e2a3b9dddf43a16dab32cb70194d9ff42489b6e3d1c5ae7e2d02919d

Observation 87f08290-4877-431a-9c5c-8291479b2dd4 · outbound

This paper cites Visbench: A framework for remote data visualization and analysis.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Visbench: A framework for remote data visualization and analysis

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.687894Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:01.997840Z digest=sha256:55b83b0027264d4ad3ded7a92bfc11f06d665439d3a5e2d3f1fc4d75593003d5

Observation 69b421ce-8c08-4917-abe0-bdfc3944b2de · outbound

This paper cites Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.095752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.095752Z digest=sha256:72c04c0bd8fe4d03287dfb7351933e4da1d87a1e4a9ab2874c9e1d9705c7f034

Observation 81e2ba49-0023-467d-b944-61d8f8bb5ca1 · outbound

This paper cites GPT-4o System Card.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding GPT-4o System Card

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.191523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.191523Z digest=sha256:1f98e0abf495ac76691e5b13294cd618dc3efaac4e3253be60617747948bd374

Observation eaa67c7e-0c46-4022-aae0-986774e0a664 · outbound

This paper cites BIMBA: Selective-Scan Compression for Long-Range Video Question Answering.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding BIMBA: Selective-Scan Compression for Long-Range Video Question Answering

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.288427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.288427Z digest=sha256:5923f24aa82c795872c065d05fe28813f6b0068281ef885849b7f37aff843c90

Observation d46fa311-2b27-4b04-a6ab-e182a8e94d36 · outbound

This paper cites VideoRAG: Retrieval-Augmented Generation over Video Corpus.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding VideoRAG: Retrieval-Augmented Generation over Video Corpus

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.359068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.359068Z digest=sha256:28c07a204f3c1df418358295cf59258d5172a478dc8a8a5547394e36279f8b68

Observation 36d9ab6b-be07-4308-a478-66bfb3688a3a · outbound

This paper cites New generation deep learning for video object detection: A survey.IEEE Transactions on Neural Networks and Learning Systems, 33(8):3195–3215, 2021.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding New generation deep learning for video object detection: A survey.IEEE Transactions on Neural Networks and Learning Systems, 33(8):3195–3215, 2021

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.593435Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:02.435864Z digest=sha256:7548819ff0ca26de9b11aaa475c22e6c998663b7a626b5c3c3efea81b1630917

Observation a3e5b1a5-cb95-48c0-a050-f451effb1163 · outbound

This paper cites A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037, 2025.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037, 2025

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.510242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.510242Z digest=sha256:166aae28d0e2ebc2c4c06fa4c1713c4f2ba6b3f82a3b97e75ee58859aed80c60

Observation a34bbc0b-9c8c-4d91-afb5-192bdfcb2f23 · outbound

This paper cites MMCTAgent: Multi-modal Critical Thinking Agent Framework for Complex Visual Reasoning.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding MMCTAgent: Multi-modal Critical Thinking Agent Framework for Complex Visual Reasoning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.616703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.616703Z digest=sha256:28bd97519ab895b171a7dc4b8743b9e3349e7e95e5b386ba50a142b79e5faa6f

Observation bb3419ea-c525-44aa-a585-17c074f8fe92 · outbound

This paper cites Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:52:07.114887Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:02.710479Z digest=sha256:82bd47ade2330fea78b9109cf5c0c069e5721d7d0297a92e4a60d4c460b9aee3

Observation 096c6815-5245-45ce-baca-b8e6e4d6cf63 · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding VideoChat: Chat-Centric Video Understanding

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.781775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.781775Z digest=sha256:ef1210c1fe4c8a13d100672c5a565cd9f74578be8c606c782941ea51e7e01933

Observation 55ee274d-81e6-4e95-a5d6-69c51ded5163 · outbound

This paper cites Mvbench: A comprehensive multi-modal video understanding benchmark.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:02.885116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:02.885116Z digest=sha256:e6102dd6ee9ac46dfe9372adf3bef8a7e2c287a7e90418f2331d49e5bb8339f7

Observation cd6432c5-199f-41a0-9fae-5001b5a82a24 · outbound

This paper cites Evaluating real-world robot manipulation policies in simulation.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Evaluating real-world robot manipulation policies in simulation

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.515158Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:02.975337Z digest=sha256:3073bd366c24acfb9683bd918c912d88df45e9fda662720b408bd9d5f667e958

Observation 5250ca97-04eb-43ec-904f-4ead60e8f0b5 · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.037638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.037638Z digest=sha256:e0db0b3ca98d2e7a80d5c58a41ed259e3b323c3d614780d12b0f9493d00670e3

Observation 82c0dd4b-7efa-417e-8b24-09987e4f0f69 · outbound

This paper cites Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.120334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.120334Z digest=sha256:212740a55d53fd7ebb691120a57dc40c69d3eddce481080f56e24d99a629549b

Observation f1ce3282-74b9-42a5-b4c9-2734a3a1c058 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.155199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.155199Z digest=sha256:d74cc33a125cf278304985b950f847a841e59508ac2eef1a9055bfaf07e125de

Observation b29f277b-80b9-4b11-910f-aad6dfaa8894 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding TempCompass: Do Video LLMs Really Understand Videos?

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.280995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.280995Z digest=sha256:81ada5b5b57c3fe8038fb2d2055f209f9525b25a35b0a8f74542d596cf46c142

Observation c4c20478-b9f1-41a2-ba72-350cd53df1a5 · outbound

This paper cites Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495, 2025

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.403830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.403830Z digest=sha256:58c5383827fff03766d3649034eee08d3d81e418fdaef6836ce597926a6b5465

Observation 3c1b2c95-f32a-47b8-95cd-374e42339c39 · outbound

This paper cites A real- time object detection algorithm for video.Computers & Electrical Engineering, 77:398–408, 2019.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding A real- time object detection algorithm for video.Computers & Electrical Engineering, 77:398–408, 2019

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.431241Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:03.501662Z digest=sha256:3a0d28d8bf6e22815bdf0b2e4b352a763428626bf6a54f02aff9dd0fa8948b30

Observation 4cd63c2f-cde4-4503-afa9-3c2302e4b9e3 · outbound

This paper cites Video-rag: Visually-aligned retrieval-augmented long video comprehension.arXiv preprint arXiv:2411.13093, 2024.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Video-rag: Visually-aligned retrieval-augmented long video comprehension.arXiv preprint arXiv:2411.13093, 2024

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.589894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.589894Z digest=sha256:3fe2cf81be7f237c91712d718c3344b77ef44d5da934e58991dac99e2c369d4b

Observation 36ba2adb-1ec1-4c2a-9c3b-025e15b9efbb · outbound

This paper cites Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Egoschema: A diagnostic benchmark for very long-form video language understanding.Advances in Neural Information Processing Systems, 36:46212–46244, 2023

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.678829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.678829Z digest=sha256:fc9d1ca25c3115afabe55d1aaa11ca80b9669c29ca34b6f678ce118f800e37c2

Observation f2f78580-1bad-4ff0-b88a-2871da22b966 · outbound

This paper cites Optical character recognition.International journal of recent technology and engineering (IJRTE), 2(1):72–75, 2013.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Optical character recognition.International journal of recent technology and engineering (IJRTE), 2(1):72–75, 2013

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.302660Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:03.791377Z digest=sha256:5c88c74bf954c8fd67e34f8b166318d5dc237f7fe8e91b8f9e72d8dc726816e5

Observation 9589d168-a2a1-46b3-b59d-a0d5b52b5068 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Direct preference optimization: Your language model is secretly a reward model

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:03.906958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:03.906958Z digest=sha256:3d84bc6bc787f84bea9fb82fa87460993e6046fee34db26dc3cef2aaa2f44c43

Observation 2e517384-bf4b-4127-930a-623522809703 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.012050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.012050Z digest=sha256:be091d66dc9886e4e30a19dcec8f9a4299ed12d434748f7fe7ab4fca9a909f7d

Observation 7d5c8bf3-a6c2-4477-8364-6639b3871b1a · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36:8634–8652, 2023

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.092255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.092255Z digest=sha256:0970af5794d4c6dfae8480c18c25155a9810559bb7bc18bb055a5a61cce3b3ff

Observation 8a5c5170-2b8f-4c5f-ad9d-c6bff655e472 · outbound

This paper cites Clip4caption: Clip for video caption.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Clip4caption: Clip for video caption

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.161153Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:04.203180Z digest=sha256:6a8ef9113b6a8bc901a7229d17c25adbd9dc8e5bb3064c7b76e263e76a85b4ee

Observation b492a32e-b99c-42d9-b9d9-f75137683fc1 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.282750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.282750Z digest=sha256:955cedf55257140cac8b8216308cec97153604efe343ea04b6ed77467b1b1a12

Observation 6c873815-9fe0-4a56-9899-af6e692d1f15 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.407610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.407610Z digest=sha256:bb583cc6166d4edef6016315c4e62836f4c9fbcd117679a8c5e704c1ba836e48

Observation 694b2ea1-a581-4bfd-9043-d45c68d63642 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding LVBench: An Extreme Long Video Understanding Benchmark

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.486784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.486784Z digest=sha256:c8d939ac7d6bf36d64c7a8ca8916a5b7725009dd036626c7d124678b221a1167

Observation 4f7ade44-8bbf-4824-abaa-b9180d64e2d9 · outbound

This paper cites Videoagent: Long-form video understanding with large language model as agent.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Videoagent: Long-form video understanding with large language model as agent

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.606785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.606785Z digest=sha256:18e9f3c0e7530efa90ef29c2b33fa49c22ea8fc6af10d803baa4ecb9432ee30c

Observation b0cfdf22-0f13-436f-a2cb-c0ff9ef1f06c · outbound

This paper cites Longllava: Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture.arXiv preprint arXiv:2409.02889, 2024.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Longllava: Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture.arXiv preprint arXiv:2409.02889, 2024

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.621903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.621903Z digest=sha256:d396d12e59cb40d62ec3453a4d96c880e5fe5aa6bb64b0304d7583051594c7ed

Observation f9d85935-da37-4c4a-a56d-c37c6088d6a0 · outbound

This paper cites SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.646937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.646937Z digest=sha256:e67971b996b2c026a4f126db929161c6c522bf236ee075e878953c024625dfc5

Observation b6da2d5e-de29-42e3-966d-b14c05c59e11 · outbound

This paper cites Reinforcement learning.Adaptation, learning, and optimization, 12(3):729, 2012.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Reinforcement learning.Adaptation, learning, and optimization, 12(3):729, 2012

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:09.069209Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:04.713331Z digest=sha256:427254189ac8dea3c1ae6d222f0f45ce6473e2d853f8293cb1f448708b536d4b

Observation af4c72b2-bc22-40cc-85b2-9cb08932bbd9 · outbound

This paper cites Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.806540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.806540Z digest=sha256:3e6ad0431f7bea71b7a3b6b0fe5258fc5ec26f7f71f175f4ee8aea2c1a26964b

Observation b4ec8324-c20b-496c-b5d9-2db4aa971f94 · outbound

This paper cites Next-qa: Next phase of question- answering to explaining temporal actions.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Next-qa: Next phase of question- answering to explaining temporal actions

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.896434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.896434Z digest=sha256:1093289c55feaadc8127e6fb01b2ce91477b81424bc014b1ab2ff5626f3cc13e

Observation a764790d-75e9-40b8-92e1-412508e6e4e9 · outbound

This paper cites Qwen2.5 Technical Report.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Qwen2.5 Technical Report

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:04.983800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:04.983800Z digest=sha256:fd3b5f367d16f75fbb76e622f3c30103fdac66456fbf02a3d97600b654cc711b

Observation 90e8c85e-ca08-4738-bf48-0c7e041833a7 · outbound

This paper cites Vid2seq: Large-scale pretraining of a visual language model for dense video captioning.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Vid2seq: Large-scale pretraining of a visual language model for dense video captioning

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.930996Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.081211Z digest=sha256:f30e7b6158250fd89c0a0eddb592ff8904f29a9cbd3ee534ac214da1d3ff0bf2

Observation 1e00a398-26eb-43cb-8fe3-3094f39cb71d · outbound

This paper cites VCA: Video Curious Agent for Long Video Understanding.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding VCA: Video Curious Agent for Long Video Understanding

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.146191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.146191Z digest=sha256:b6ee7983a07cbcad836f65ff44fc35e6461c95dc571ac750801912828ac4fdc6

Observation 4fec2d96-4ea0-4ee6-83dd-722aeaa204ae · outbound

This paper cites T*: Re-thinking Temporal Search for Long-Form Video Understanding.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding T*: Re-thinking Temporal Search for Long-Form Video Understanding

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.217519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.217519Z digest=sha256:a27b7c2b1c37fb7cbc76e001c3fbe0c4bfd87fe6d8197200995c624004aa41d0

Observation 1d2cc1b7-310b-4135-8c15-ef95429627c8 · outbound

This paper cites Springer, 2016.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Springer, 2016

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.810813Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.233294Z digest=sha256:dc4de7200a14111f76734e58e95c17986257c6acb6b424aafadc4ac52c61bd3d

Observation a656f6a0-36b8-455c-b943-f2498f93a962 · outbound

This paper cites Merlot: Multimodal neural script knowledge models.Advances in neural information processing systems, 34:23634–23651, 2021.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Merlot: Multimodal neural script knowledge models.Advances in neural information processing systems, 34:23634–23651, 2021

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.744429Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.243564Z digest=sha256:078ceaef528545ca8ccce7fced81b149c47b9af9e80c7a3ab46639c81458cce0

Observation 9fb880f2-dc92-4e74-840c-74ba33e7b7f9 · outbound

This paper cites OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.252358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.252358Z digest=sha256:b614218287b34442f3892f35176303edd7191ce9883fc4dea4eb4a81e64efdf2

Observation 1037613c-34e7-4cf4-993a-c89ec1469acc · outbound

This paper cites Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.260604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.260604Z digest=sha256:c117e62e3c9eb750de3c26b4867af886f270613570c14ede7bf0a24a70a2e2c7

Observation 4afaad58-44d7-4e64-8e01-b8477d86472e · outbound

This paper cites TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.300590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.300590Z digest=sha256:5ce52e89836f9fae2e65d7289973db4345c9f6ddd5e8bf521f9f8779e814e3f0

Observation 750e3829-9bd7-499d-a53f-33291ba92b22 · outbound

This paper cites Llava-next: A strong zero-shot video understanding model.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Llava-next: A strong zero-shot video understanding model

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.389434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.389434Z digest=sha256:1c652cebd1614e33c94017b2ccb7059891d70ec413ab7f033aaaaa0f97b25b98

Observation 759fd701-e7b1-4f1a-86f2-762e8626f470 · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.475245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.475245Z digest=sha256:d9588b429d5b7059b00e976c72f9cded2823ac11e20a476dac78509e10dd716a

Observation 7c6129e4-3d2e-4edd-b340-313df9e42b50 · outbound

This paper cites GRAPE: Generalizing robot policy via preference alignment.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding GRAPE: Generalizing robot policy via preference alignment

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.646448Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.542812Z digest=sha256:3de62b41cad9995eddd7111f7499204c1dceb73af4d417796c8db2395a5856fb

Observation 3ec4580c-f95e-4276-833e-804df956f725 · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding MMVU: Measuring Expert-Level Multi-Discipline Video Understanding

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.618855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.618855Z digest=sha256:921a2dc48f30ad97a887fd346e18b4159102e00a861a67114b96182d23564935

Observation e7957743-e946-4247-b3f0-3d91b93f9cdd · outbound

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

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding MLVU: Benchmarking Multi-task Long Video Understanding

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.694478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.694478Z digest=sha256:1cac8d82759f84a7f3b9cb63407a0a489680c2172e434e139bcab783b0c804aa

Observation 27221795-aa84-426e-8810-dc44697e6c62 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Fine-Tuning Language Models from Human Preferences

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T11:52:05.786594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:52:05.786594Z digest=sha256:e45e81a67da9ac25246e225ef9bf972a0e4a4a18281f191efa408976a425ce20

Observation 60af8306-df46-48fa-b256-7449baec38fa · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.575739Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.855492Z digest=sha256:57ce7092e642a929d201be287e5f4cfc9ab7fcf0954d1b81b10dc3c25a5d2abb

Observation cbf29bb9-eb39-41a8-8929-c98c1dc62353 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.484882Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.875636Z digest=sha256:4fd3fc9a51c151d4290d5f51d80aef7a5bd4fce1c446ed8a6a65624010972055

Observation 2526a2cf-926f-4e6f-91f2-4837c46a85c5 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.415386Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.881469Z digest=sha256:5f4bea88c0141f7ea06725e51a8e751c8997c144d196772710c5e2304e439a11

Observation 4fca7a39-865d-4986-a0e4-ea0c49908c0a · outbound

This paper cites final_answer.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding final_answer

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.336877Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.889587Z digest=sha256:d6668615f1df2e3e1e5bab0967e102e6d979bfa462c69881c1a160fc791e4f5b

Observation e867ee9b-fdb4-4ed4-b918-dd808f59b5e0 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.271741Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:05.929450Z digest=sha256:767d8107cfe5ede1589289aee880050723daef4d3c05b3d6b3e05570bd7732ba

Observation 8ba41646-316e-41d8-9685-c1db615163a4 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 66

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.208571Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.028957Z digest=sha256:3117c6ec84690fca37c56ec923e7ae7905d769ff85908fc390aaae43b8001f75

Observation 6f519e9a-0846-43cc-8fc4-5572ce1cd1bf · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 67

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:08.151703Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.106668Z digest=sha256:b8f24813e4dac8fd285c918219a269bf5cf6ce9bbe8d0dfa1614ce8a35519463

Observation 7e859022-84ce-4c5c-a1e8-b94fa7b6e6b4 · outbound

This paper cites final_answer.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding final_answer

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:08.067744Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.173820Z digest=sha256:688aa96bdf8a590247177695a64476a7e116ca853da1c0ecf666d8d648aa8806

Observation 20ad729e-21f9-4a82-b3a3-3256ece21e10 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 69

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.975166Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.240410Z digest=sha256:99aa8f6aa072ae8920abc1b5303379fe4be05840186c92af5d5685d80fb2f0b5

Observation da03baae-7c04-4766-9244-e4359eaf489c · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 70

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.894057Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.309906Z digest=sha256:2821d66c09ca3740c5aff7ccab66e6ff7c730b6107b018213e5491aaf59678a3

Observation 0f5f08f1-18c0-40c1-a535-d8c5f4ea15b1 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 71

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.821986Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.375025Z digest=sha256:7b5c11e73e027d25a0c34725ba9a4be7c736c9fcbb4fdaeadd8b9f02c9ebeffe

Observation 39ceaa36-4aa4-47b5-877e-7ea413dc8402 · outbound

This paper cites canine” = “dog.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding canine” = “dog

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:07.725231Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.440832Z digest=sha256:cbb5f02ad46150ad27201112e34454989ae45ddc25c96da4a5c81e73770c0689

Observation 33859cb0-8896-4928-ac80-402e402f787e · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 73

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.631729Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.475059Z digest=sha256:b849d3f4072fbd163fa9b3a2eefaf74278a1be932f84f9570364ccab52df4abb

Observation 1d5a6fe0-6ccc-431e-b8cc-84d455415f74 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 74

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.537346Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.487611Z digest=sha256:dbfa9040147a26dd0724ea2b3f3f57ef92b2529207ef9c5e637351f3e553095e

Observation 366c6bcd-9312-482f-9bf6-2dc94b70f5e1 · outbound

This paper cites an unresolved cited work.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding Unresolved cited work

Reference 75

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:52:07.445401Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.507204Z digest=sha256:97969b49bd4e796956ea30ef44610d5f9db90cd2f411e24d606daa6c86081d78

Observation 70d8f986-230c-46ed-8267-0690b04d910e · outbound

This paper cites What”, “Where.

ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding What”, “Where

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:52:07.382681Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-07T11:52:06.520392Z digest=sha256:77a12ec9df8824d6ef5cc7d43dd17a246595d9fe53a756823062bfc10f522486

Pith citing papers

Observation 3a4df78e-d045-4809-9b5e-907452d9e57a · inbound

4KAgent: Agentic Any Image to 4K Super-Resolution cites this paper.

4KAgent: Agentic Any Image to 4K Super-Resolution ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 268

Resolution
unresolved
no resolver link, observed 2026-08-06T18:52:39.441018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:52:39.441018Z digest=sha256:f1c822c7439612d1cab0e63ab0468a9ef133aeab2b92ea523b69ba5f1cfe30ab

Observation ea46245c-9613-4a23-86d7-d7331fac33fa · inbound

Low-Cost Test-Time Adaptation for Robust Video Editing cites this paper.

Low-Cost Test-Time Adaptation for Robust Video Editing ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T12:21:30.770642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T12:21:30.770642Z digest=sha256:50fd06e205794b1796a9506ac2570c14a64e2a6d05fd18a5de1379d12e6c87d1

Observation 495ed2be-70c7-4bb2-9c66-9c8f48b5db12 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:04.377414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:04.377414Z digest=sha256:0fa5f1e1b3566f5f672839e82f4ac6309bc86c4f554679ddac35500baf42e460

Observation 7ff03557-9f3d-4582-b7f2-a9e28202df8a · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-05T12:53:15.065485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:53:15.065485Z digest=sha256:0a671a0f071ef66bf55c55e21605dfaf60529c90e34baf9b1092584c49b45ce5

Observation e6b80ed8-feae-4fbf-bb1c-b69d507d9c7f · inbound

DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables cites this paper.

DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-05-18T15:31:33.406292Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-18T15:30:20.060374Z digest=sha256:2320148c96d1ef458436b891b27121aab9d22f29f9c8165cac5523699805dfa4

Observation 1771a2f9-43da-462e-bac9-3a53fa1657a0 · inbound

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting cites this paper.

Hermes: A Multi-Scale Spatial-Temporal Hypergraph Network for Stock Time Series Forecasting ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 77

Resolution
verified exact
arxiv_id, observed 2026-05-18T12:36:22.633218Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-05-18T12:33:46.039899Z digest=sha256:f72b8f0beeda5b926dffb293de74d4572f068d2c3cc988eb14e5ca62a0d21204

Observation 7bb90a75-89c5-41aa-874e-7cffd7629122 · inbound

GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing cites this paper.

GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:20:51.103089Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T19:13:30.400059Z digest=sha256:f128885a5bdc13421869fb43419418a8158115e1eb579d7be4df6eedbee8fa6d

Observation b3921a8d-93da-49c9-9b9f-542e6e097ea7 · inbound

HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration cites this paper.

HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:26:02.242836Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-09T21:55:35.699057Z digest=sha256:f85b9e9e1dbbbdf7eed14c635abfc4a2ce7dffb215a948062eade3ea09d435ac

Observation 67121b98-4b0d-41c1-99b7-5b3181bee725 · inbound

Agentic Collaborative Cognition for Zero-Shot 3D Understanding cites this paper.

Agentic Collaborative Cognition for Zero-Shot 3D Understanding ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 63

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T16:39:57.779695Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-26T00:22:06.082183Z digest=sha256:b8b8c7102b1ccf6e4fda4150521507961e038f2f318b28e7d3c8a562c56e28b1

Observation 70a1566f-46d7-4825-b7de-c0e186576dab · inbound

Agentic Collaborative Cognition for Zero-Shot 3D Understanding cites this paper.

Agentic Collaborative Cognition for Zero-Shot 3D Understanding ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 63

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T12:59:52.640164Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-26T05:37:41.407624Z digest=sha256:78ea9ba93fe44a6809f73299e1a79c32b762889a3857462f5133001ada55b3fa

Observation 73db7383-415f-4ca2-9be8-7d65c81c02a8 · inbound

EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control cites this paper.

EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 89

Resolution
verified exact
arxiv_id, observed 2026-06-30T10:04:36.295073Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-30T09:57:12.402737Z digest=sha256:62b92cc9d4b459137cd8daecb13f916a13904334441999c93b485a92784bf8f4

Observation 105f4180-394b-4e53-990c-2e7c1b0159e6 · inbound

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions cites this paper.

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T09:54:34.421694Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-30T09:54:05.683478Z digest=sha256:8e34f882d4795796fa15afd2125f949d18a8a795ad1eaa6a010d0455a21b0b66

Observation 2bad20b2-32ca-4641-8968-6c61e4ae1166 · inbound

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework cites this paper.

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-03T15:28:33.596585Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-07-03T15:26:40.974564Z digest=sha256:481d2401fa243b1950325a54df1a50c050b977c93417314c8dbca08a123e8f96

Observation 98e38a82-915f-4527-a8f8-5882a6c71685 · inbound

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift cites this paper.

FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-14T17:36:18.728469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T17:36:18.728469Z digest=sha256:eb6cbb17d2c93c4c6c2a98ae43485bc2bde71df1a64566f3def8c6b53f996905

Observation 94a0d757-191e-49df-9044-6f0de5e6aec7 · inbound

Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Framework cites this paper.

Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Framework ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-01T13:14:48.090658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:14:48.090658Z digest=sha256:95e927cca812d81dae59ddcdfdd5f9b3be546294036eea9cbf7a5af9a888aabb