# LMM-Incentive: Large Multimodal Model-based Incentive Design for User-Generated Content in Web 3.0

arXiv: https://arxiv.org/abs/2510.04765 · HTML: https://mlsanity.com/paper/2510.04765/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Contracts under Moral Hazard and Adverse Selection](https://mlsanity.com/paper/2010.06742/) (2020)
- [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023)
- [Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT](https://mlsanity.com/paper/2407.10979/) (2024)
- [Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services](https://mlsanity.com/paper/2311.11094/) (2023)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches](https://mlsanity.com/paper/2404.08899/) (2024)
- [Diffusion-based Dynamic Contract for Federated AI Agent Construction in Mobile Metaverses](https://mlsanity.com/paper/2504.14326/) (2025)
- [Enhancing Trust and Security in the Vehicular Metaverse: A Reputation-Based Mechanism for Participants with Moral Hazard](https://mlsanity.com/paper/2405.19355/) (2024)
- [Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation](https://mlsanity.com/paper/2411.00825/) (2024)
- [Defending Against Network Attacks for Secure AI Agent Migration in Vehicular Metaverses](https://mlsanity.com/paper/2412.20154/) (2024)
- [Learning-based Big Data Sharing Incentive in Mobile AIGC Networks](https://mlsanity.com/paper/2407.10980/) (2024)
- [Vision Language Model-Empowered Contract Theory for AIGC Task Allocation in Teleoperation](https://mlsanity.com/paper/2407.17428/) (2024)
- [Learning to Incentivize: LLM-Empowered Contract for AIGC Offloading in Teleoperation](https://mlsanity.com/paper/2508.03464/) (2025)

## Abstract (arXiv)

Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging blockchain and artificial intelligence technologies, Web 3.0 offers unprecedented opportunities for users to create, own, and monetize their content, thereby enabling User-Generated Content (UGC) to an entirely new level. However, some self-interested users may exploit the limitations of content curation mechanisms and generate low-quality content with less effort, obtaining platform rewards under information asymmetry. Such behavior can undermine Web 3.0 performance. To this end, we propose textitLMM-Incentive, a novel Large Multimodal Model (LMM)-based incentive mechanism for UGC in Web 3.0. Specifically, we propose an LMM-based contract-theoretic model to motivate users to generate high-quality UGC, thereby mitigating the adverse selection problem from information asymmetry. To alleviate potential moral hazards after contract selection, we leverage LMM agents to evaluate UGC quality, which is the primary component of the contract, utilizing prompt engineering techniques to improve the evaluation performance of LMM agents. Recognizing that traditional contract design methods cannot effectively adapt to the dynamic environment of Web 3.0, we develop an improved Mixture of Experts (MoE)-based Proximal Policy Optimization (PPO) algorithm for optimal contract design. Simulation results demonstrate the superiority of the proposed MoE-based PPO algorithm over representative benchmarks in the context of contract design. Finally, we deploy the designed contract within an Ethereum smart contract framework, further validating the effectiveness of the proposed scheme.