Paper Citation Record · LEDGER

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

As of 7 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 14 inbound Pith citation observations for arXiv:2506.01908.

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

pith.paper-citation-record.v1
2506.01908 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:35:48.103145Z

measured 55 of 55 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:03:29.800596Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:57.219030Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy2
unresolved39
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e2ed415-a459-42f9-a09f-d03a53b8e48a · outbound

This paper cites Localizing moments in video with natural language.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Localizing moments in video with natural language

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:44.652453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e6a877c-0211-4de8-ac75-94c96c2d6796 · outbound

This paper cites Qwen2.5-VL Technical Report.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Qwen2.5-VL Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:44.713622Z

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Observation eccda8be-c02a-419c-b524-25ba3ae25d04 · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:44.824339Z

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Observation 8fc8c328-42a3-459d-8d07-088e5c15919c · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Video-R1: Reinforcing Video Reasoning in MLLMs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:44.928073Z

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Observation 8569e3b4-44da-4cba-a19a-2d97d92b075c · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.014401Z

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Observation 45f9f087-ac3c-4db6-88b4-1b9f4814e1cb · outbound

This paper cites Tall: Temporal activity localization via language query.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Tall: Temporal activity localization via language query

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.104221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df619bf6-970c-49cf-b621-4114a27adc8b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.203654Z

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Observation d07b57b7-0ada-4364-a2cb-8ef7e1fd7278 · outbound

This paper cites Vtg-llm: Integrating timestamp knowledge into video llms for enhanced video temporal grounding.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Vtg-llm: Integrating timestamp knowledge into video llms for enhanced video temporal grounding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:35:49.620578Z

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 2771756b-e8fa-4632-881e-95c545e86d39 · outbound

This paper cites VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.372866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9da80ad0-f63a-4bc9-a3db-7b3d1e21690c · outbound

This paper cites Vtimellm: Empower llm to grasp video moments.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Vtimellm: Empower llm to grasp video moments

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.455429Z

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Unavailable: canonical work link unavailable.

Observation 43b7121e-3cac-4838-88fd-b8361a7a9dc0 · outbound

This paper cites Lita: Language instructed temporal-localization assistant.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Lita: Language instructed temporal-localization assistant

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.552030Z

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Observation 45cfdbd5-8265-4231-a9f7-f3e8f5fd40f1 · outbound

This paper cites Unleashing the temporal-spatial reasoning capacity of gpt for training-free audio and language referenced video object segmentation.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Unleashing the temporal-spatial reasoning capacity of gpt for training-free audio and language referenced video object segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:35:49.420703Z

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 3d982fbe-eb3f-4b05-8f52-62c741a52b0c · outbound

This paper cites Dense- captioning events in videos.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Dense- captioning events in videos

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.715578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6e2bcb-30ea-4ec2-9188-066139becf08 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency LLaVA-OneVision: Easy Visual Task Transfer

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.796764Z

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Unavailable: canonical work link unavailable.

Observation f79c5a1c-78e4-4915-8011-72b2ac8f5279 · outbound

This paper cites LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.865632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4f70289-08d2-46c8-ae70-8bcb2c2b402d · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency VideoChat: Chat-Centric Video Understanding

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:45.945951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea610f96-09fd-40b8-9665-c8b5486b81d1 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Mvbench: A comprehensive multi-modal video understanding benchmark

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.014402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a53e36c-eb1c-4e6f-8fb3-3e6f8f8694de · outbound

This paper cites Llama-vid: An image is worth 2 tokens in large language models.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Llama-vid: An image is worth 2 tokens in large language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.091779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ba70d3-efd9-41c4-bb52-345f72de1a67 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.174675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79e47c18-6a64-4be8-92c6-176f2ac99756 · outbound

This paper cites St-llm: Large language models are effective temporal learners.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency St-llm: Large language models are effective temporal learners

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.261016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56dba7d3-10b7-4430-87a0-afb694e0234a · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency TempCompass: Do Video LLMs Really Understand Videos?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.327864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 123f7ac3-f57a-4fee-8903-251e01aa0edd · outbound

This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.421128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 573628b1-4839-4208-820e-ad9f759dbedb · outbound

This paper cites Perception test: A diagnostic benchmark for multimodal video models.Advances in Neural Information Processing Systems, 36:42748–42761, 2023.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Perception test: A diagnostic benchmark for multimodal video models.Advances in Neural Information Processing Systems, 36:42748–42761, 2023

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.515892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10982dd-14f0-48e5-a377-08aea53acc44 · outbound

This paper cites Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.597000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e07006d-1dbc-4672-bfbc-14f921321bc9 · outbound

This paper cites Timechat: A time-sensitive multimodal large language model for long video understanding.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Timechat: A time-sensitive multimodal large language model for long video understanding

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.692867Z

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Observation 886ba38e-c3c1-4fe8-ae62-1a6c3912c6d6 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.785592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc1fead3-176b-4331-8547-5c910e8d96d6 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.872216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac4dc91a-c40b-4533-9d0e-c72615597896 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:46.943484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921a662e-7183-44a5-b656-983f70e2ff53 · outbound

This paper cites Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.027169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8498e34-385e-46d5-b571-f9cbbe09859c · outbound

This paper cites InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.149960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85ce6aae-bad9-42a4-9987-1de562e8821c · outbound

This paper cites HawkEye: Training Video-Text LLMs for Grounding Text in Videos.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency HawkEye: Training Video-Text LLMs for Grounding Text in Videos

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.240790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e97de0ce-7f61-4652-b68c-917365ddae96 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Next-qa: Next phase of question- answering to explaining temporal actions

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.308312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 295dcf46-87a4-4baf-b49c-c5725e07db3d · outbound

This paper cites Can i trust your answer? visually grounded video question answering.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Can i trust your answer? visually grounded video question answering

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.389258Z

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Observation 0bb155bc-f509-48e6-9723-679ee5d23c62 · outbound

This paper cites mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.480823Z

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Unavailable: canonical work link unavailable.

Observation 8493c7c0-257f-4882-ae9a-c2fa44dea18b · outbound

This paper cites Unhackable Temporal Rewarding for Scalable Video MLLMs.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Unhackable Temporal Rewarding for Scalable Video MLLMs

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.573465Z

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Unavailable: canonical work link unavailable.

Observation 997cf40a-d098-4e74-9803-f8343e4597cb · outbound

This paper cites TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.666720Z

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Observation b6525d68-4675-45d3-ad5f-a6f602a39e3a · outbound

This paper cites Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.736748Z

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Unavailable: canonical work link unavailable.

Observation 03c4b6b9-57e9-42a5-ae6d-9513ed906ae0 · outbound

This paper cites R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.821566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc55fd6e-4219-447b-b325-f6fe6f62535c · outbound

This paper cites Long Context Transfer from Language to Vision.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency Long Context Transfer from Language to Vision

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.916580Z

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Unavailable: canonical work link unavailable.

Observation 81a57794-523a-4f6d-aa33-8a70e34563d8 · outbound

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

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency MMVU: Measuring Expert-Level Multi-Discipline Video Understanding

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:48.023662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68083eb2-8d87-452c-9426-e39db36d209f · outbound

This paper cites R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model.

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:48.103145Z

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Pith citing papers

Observation c2f4e65e-d940-42b8-8d43-1f68641fb498 · inbound

Empowering Nanoscale Connectivity through Molecular Communication: A Case Study of Virus Infection cites this paper.

Empowering Nanoscale Connectivity through Molecular Communication: A Case Study of Virus Infection Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 38

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Source-reported events for the cited work

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Observation 9ab09eb4-570a-404a-9b79-903c34a99536 · inbound

TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding cites this paper.

TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T22:01:25.936958Z

Source-reported events for the cited work

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Observation 050345ff-e308-45d7-9f82-44827a2917c7 · inbound

OneThinker: All-in-one Reasoning Model for Image and Video cites this paper.

OneThinker: All-in-one Reasoning Model for Image and Video Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:11:26.630505Z

Source-reported events for the cited work

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Observation 5d6fde9d-8de0-49ff-b154-7fd71dcbe877 · inbound

AdaTooler-V: Adaptive Tool-Use for Images and Videos cites this paper.

AdaTooler-V: Adaptive Tool-Use for Images and Videos Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:28:34.346222Z

Source-reported events for the cited work

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Observation d62ebeaa-5f0d-4f4c-bb7e-d320650c0026 · inbound

GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking cites this paper.

GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:50:17.320193Z

Source-reported events for the cited work

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Observation 5f329e02-4e84-42af-94a8-3b9914d312ba · inbound

DynFrame: Adaptive Reasoning-Driven Multimodal Framework with Dynamic Frame Augmentation for Complex Video Understanding cites this paper.

DynFrame: Adaptive Reasoning-Driven Multimodal Framework with Dynamic Frame Augmentation for Complex Video Understanding Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:53:47.135590Z

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 2673ee9c-ee9e-4103-a9ff-3e0560d47143 · inbound

Temporal-Aware Reasoning Optimization for Video Temporal Grounding cites this paper.

Temporal-Aware Reasoning Optimization for Video Temporal Grounding Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 122

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:17:29.087471Z

Source-reported events for the cited work

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Observation 78b1981f-1631-4e84-8e43-db819365475a · inbound

Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs cites this paper.

Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:57.749200Z

Source-reported events for the cited work

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Observation 5fe7afff-ff8f-4d04-bc81-d73212b62804 · inbound

CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs cites this paper.

CARE: Competence-Aware Reward Shaping for Adaptive Reasoning Length in Video-MLLMs Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:31.391135Z

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 17baca14-be29-4760-8abd-79b8b54c4837 · inbound

DramaDirector: Geometry-Guided Short Drama Generation cites this paper.

DramaDirector: Geometry-Guided Short Drama Generation Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 66

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T14:59:55.979608Z

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 c728d01b-29e6-4990-bdbd-fdb58bd8a711 · inbound

SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards cites this paper.

SER: Learning to Ground Video Reasoning with Semantic Evidence Rewards Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:49:57.220535Z

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 2ba040e6-b12e-4f6b-a703-ede10224c1a3 · 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 Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T19:13:52.951272Z

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 2c46cb5e-b4d1-46c7-a68f-ac8bc4892e0c · inbound

TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs cites this paper.

TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T18:04:13.530716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ed0b2c7-1ed2-42c3-be13-e58a000da904 · inbound

AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning cites this paper.

AdaThinkV: Adaptive Thinking for Token-Efficient Video Reasoning Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T17:24:14.453570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.