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

2025-10-06

AI Summary

To address information asymmetry-induced proliferation of low-quality content and misaligned incentives in Web 3.0, this paper proposes an intelligent contract-based incentive mechanism integrating contract theory and large multimodal models (LMMs). Methodologically, we develop an LMM-driven quality assessment framework leveraging prompt engineering for fine-grained multimodal content understanding; design a Mixture-of-Experts–Proximal Policy Optimization (MoE-PPO) reinforcement learning algorithm to dynamically generate optimal incentive contracts; and deploy the mechanism on-chain via Ethereum smart contracts. Our key contribution lies in the first unified modeling of contract theory, multimodal quality evaluation, and MoE-augmented PPO—enabling quality-aware, adaptive incentive allocation. Experiments demonstrate that our algorithm significantly outperforms baseline methods in contract optimization tasks. Furthermore, the system has been successfully deployed and validated on the Ethereum mainnet, confirming both theoretical rigor and engineering feasibility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

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 extit{LMM-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.

Problem

• Addressing low-quality content exploitation in Web 3.0 UGC platforms
• Mitigating adverse selection from information asymmetry using LMMs
• Designing dynamic contracts to incentivize high-quality user content

Innovation

• LMM-based contract model motivates high-quality content
• LMM agents evaluate content quality via prompt engineering
• Improved MoE-based PPO algorithm optimizes contract design