Paper Citation Record · LEDGER

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 5 inbound Pith citation observations for arXiv:2507.02626.

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

pith.paper-citation-record.v1
2507.02626 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:29:46.160078Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T03:30:33.547880Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:32.191399Z

Reference resolution

73 of 73 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a761286c-eb53-462a-a6cf-367c821dfcbe · outbound

This paper cites Tencentrec: Real-time stream recommendation in practice,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Tencentrec: Real-time stream recommendation in practice,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.626174Z

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Observation aeb3a37a-b3e4-488d-a294-bcac39e315b6 · outbound

This paper cites Spectral collaborative filtering,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Spectral collaborative filtering,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.503711Z

Source-reported events for the cited work

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Observation 24adc1e1-e2c5-4cb9-9b62-8661ba601848 · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Graph convolutional neural networks for web-scale recommender systems,

Reference 3

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Observation 85e90aac-2169-4545-bd7c-17a76b125652 · outbound

This paper cites Mixed negative sampling for learning two-tower neural networks in recommendations,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Mixed negative sampling for learning two-tower neural networks in recommendations,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.377235Z

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Observation 0088e1a3-8341-4721-a164-cfdc6668001d · outbound

This paper cites Parameter-efficient transfer from sequential behaviors for user modeling and recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Parameter-efficient transfer from sequential behaviors for user modeling and recommendation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.251022Z

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Observation ccca9dbe-cde2-45b6-8c52-1bafeb80325b · outbound

This paper cites NoteLLM-2: Multimodal Large Representation Models for Recommendation.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning NoteLLM-2: Multimodal Large Representation Models for Recommendation

Reference 6

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Source-reported events for the cited work

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Observation 4ce0ea76-5961-44e1-8c39-088c88554b34 · outbound

This paper cites HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:29:40.369144Z

Source-reported events for the cited work

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Observation 93ef1178-d0c3-4fd0-93ab-722ec5e15c60 · outbound

This paper cites QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou

Reference 8

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Source-reported events for the cited work

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Observation b97aa6e4-0834-40bc-82ff-efd416ba058e · outbound

This paper cites Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.125343Z

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Observation 66bd7b8b-9ac2-4b54-a6dc-f5f3a8cd6a97 · outbound

This paper cites Representation learning with large language models for recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Representation learning with large language models for recommendation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:54.014630Z

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Observation 1cdb6a71-b397-44b4-950c-bafcb806b5e5 · outbound

This paper cites STAR: A Simple Training-free Approach for Recommendations using Large Language Models.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning STAR: A Simple Training-free Approach for Recommendations using Large Language Models

Reference 11

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Observation c7968594-5fd7-4476-bab0-1320d6d9fb80 · outbound

This paper cites Learn: Knowledge adaptation from large language model to recommendation for practical industrial application,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Learn: Knowledge adaptation from large language model to recommendation for practical industrial application,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.901862Z

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Observation 4f6c8fa2-6b87-44f6-aa71-32772ba9f55e · outbound

This paper cites PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information

Reference 13

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Observation b981319d-046c-48e4-a8c2-c5febce89f8a · outbound

This paper cites Llm-powered user simulator for recommender system,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Llm-powered user simulator for recommender system,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.746610Z

Source-reported events for the cited work

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Observation 155b2a85-b3d3-44e8-8e88-6983642486b9 · outbound

This paper cites Harnessing multimodal large language models for multimodal sequential recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Harnessing multimodal large language models for multimodal sequential recommendation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.564741Z

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Observation c13bdd1c-0c96-41bb-b4f6-9467964fadb9 · outbound

This paper cites Tallrec: An effective and efficient tuning frame- work to align large language model with recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Tallrec: An effective and efficient tuning frame- work to align large language model with recommendation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.417314Z

Source-reported events for the cited work

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Observation 99089063-3da2-4186-bc01-b10a1dd5b39b · outbound

This paper cites Usimagent: Large language models for simulating search users,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Usimagent: Large language models for simulating search users,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.197567Z

Source-reported events for the cited work

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Observation 72b576b5-ed0f-437f-a6ee-9f4c606dc473 · outbound

This paper cites On generative agents in recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning On generative agents in recommendation,

Reference 18

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Observation 92efdefa-5216-4b59-b7ce-265cf9b945c7 · outbound

This paper cites Simuser: Generating usability feedback by simulating various users interacting with mobile applications,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Simuser: Generating usability feedback by simulating various users interacting with mobile applications,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:53.011206Z

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Observation 76ddb3ad-9314-4d15-b51c-1910323313c6 · outbound

This paper cites Vista: A visually, socially, and temporally-aware model for artistic recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Vista: A visually, socially, and temporally-aware model for artistic recommendation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:52.827467Z

Source-reported events for the cited work

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Observation 5bcf6e1f-4799-461c-a10b-5c5e074f9866 · outbound

This paper cites A Content-Driven Micro-Video Recommendation Dataset at Scale.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning A Content-Driven Micro-Video Recommendation Dataset at Scale

Reference 21

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Observation 2721fb66-93e4-4fa3-a70d-d3ded775a893 · outbound

This paper cites Enhancing adversarial robustness of multi-modal recommendation via modality balancing,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Enhancing adversarial robustness of multi-modal recommendation via modality balancing,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:52.676363Z

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Observation 68f3d1fb-6d12-4a68-ad1d-d274e464c3e6 · outbound

This paper cites Multimodal recommender systems: A survey,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Multimodal recommender systems: A survey,

Reference 23

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Observation 7fb5cf82-f11b-49de-89d9-f1969f36f5c2 · outbound

This paper cites A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

Reference 24

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning User behavior simulation with large language model-based agents,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:52.332329Z

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Observation 6b965be8-eea0-46c7-8769-3b7cffd28f28 · outbound

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

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 26

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning GPT-4o System Card

Reference 27

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This paper cites Super encoding network: Recursive association of multi-modal encoders for video understanding,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Super encoding network: Recursive association of multi-modal encoders for video understanding,

Reference 28

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This paper cites Percept, chat, and then adapt: Multimodal knowledge transfer of foundation models for open-world video recognition,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Percept, chat, and then adapt: Multimodal knowledge transfer of foundation models for open-world video recognition,

Reference 29

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Large language models are zero-shot rankers for recommender systems,

Reference 30

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This paper cites Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Reference 31

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems

Reference 32

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning GPT-4 Technical Report

Reference 33

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 34

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning

Reference 36

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning o1-Coder: an o1 Replication for Coding

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 39

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Learning transferable visual models from natural language supervision,

Reference 40

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Qwen2.5 Technical Report

Reference 41

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This paper cites Learning deep structured semantic models for web search using clickthrough data,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Learning deep structured semantic models for web search using clickthrough data,

Reference 42

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Lightgcn: Simplifying and powering graph convolution network for recommendation,

Reference 43

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This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 44

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This paper cites A simple convolutional generative network for next item recommendation,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning A simple convolutional generative network for next item recommendation,

Reference 45

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This paper cites Session-based Recommendations with Recurrent Neural Networks.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Session-based Recommendations with Recurrent Neural Networks

Reference 46

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Self-attentive sequential recommendation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.670888Z

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Observation 56dd3aeb-b79c-473f-af4b-bb53519966eb · outbound

This paper cites The movielens datasets: History and context,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning The movielens datasets: History and context,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.479698Z

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Observation 48642270-6566-4c23-9636-f372372ff81c · outbound

This paper cites Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:51.285283Z

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Observation 43595e11-93a2-4665-8f1c-c5326cd31053 · outbound

This paper cites Generative adversarial user model for reinforcement learning based recommendation system,.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Generative adversarial user model for reinforcement learning based recommendation system,

Reference 50

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Kuaisim: A comprehensive simulator for recommender systems,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:50.971719Z

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Observation e31147bc-4f0f-4f86-b4b6-c62219ad15e4 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Factual and Personalized Recommendations using Language Models and Reinforcement Learning

Reference 52

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Direct Preference Optimization for LLM-Enhanced Recommendation Systems

Reference 53

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Rec-r1: Bridging generative large language models and user-centric recommendation systems via reinforcement learning,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T20:29:44.302020Z

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Observation 086f83c0-fce0-4c84-a78d-8c0eae62bf8f · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Unresolved cited work

Reference 55

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Reinforcement learning: Theory and algorithms,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:50.723294Z

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Observation d360dca5-1e49-48b6-a23f-a16fe8ac0ec3 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 57

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:50.546306Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f535053-caf1-4f76-8d98-2b06f84fd99d · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Limitations

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:50.349373Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:50.167861Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:49.960702Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:49.778545Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:49.554688Z

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Observation 5716155d-6118-45c0-9a94-d03ca03c79f9 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning We report the bar of our main results in the appendix

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:49.294230Z

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Observation 065ecc45-3d3d-4170-8af8-ca378611140e · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:49.112724Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:48.898357Z

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Observation af6c52c9-d2dc-4bec-a886-4d9d7146c5a7 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning But we establish the user profile which may have privacy consideration problems

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:48.626862Z

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper poses no such risks

Reference 68

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not use existing assets

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:48.422929Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0c442f8e-361c-4142-af37-c2a2708b9b29 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning We prepare the documentation of our code for future reproduction and will release it afterward

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:48.155255Z

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Observation 0a7bbcfd-8ee7-4b13-a469-85aa2949a440 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:47.954069Z

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Observation e9d763b9-2e83-4d8a-89e9-cc270d228041 · outbound

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Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:47.748025Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c94c1fd7-71f4-4207-ace6-1c5fad4385f7 · outbound

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VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning Answer: [Yes] Justification: We use MLLM to help understand video contents and and train the LLM with reinforce- ment fine-tuning to simulate user decision

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:29:47.498748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 132d98db-681f-453c-8c02-be8b69d6427b · inbound

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers cites this paper.

HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

Reference 125

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Observation b1811375-dcea-4ff1-a46a-efa589f2d289 · inbound

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Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

Reference 58

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Observation 5805d1c6-11f2-414b-9a7f-d539fca235be · inbound

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Twins: Learn to Predict Unified Representations with Focal Loss VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

Reference 131

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Observation f10cab37-556f-4522-ab6c-a021264665b4 · inbound

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RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

Reference 3

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Observation b0f102fa-431a-46be-a074-4fb0e1c4dac1 · inbound

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RecoReward: Recommender-Guided Multimodal Description Generation for Recommendation VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning

Reference 3

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