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

TL;DR

Web 3.0 UGC incentives suffer from information asymmetry, leading to adverse selection and potential moral hazard. The authors propose LMM-Incentive, an LMM-based contract-theoretic framework that uses LMM agents to directly evaluate UGC quality ($\mathcal{Q}(\phi)$) and an improved MoE-PPO algorithm to design optimal, discrete contracts $(\mathcal{Q}(\phi_k), R(\phi_k))$ under IR/IC constraints. The approach couples a two-stage process—quantifying $\mathcal{Q}(\phi)$ with prompt-engineered LMM evaluation and learning $R^*(\phi_k)$ via MoE-PPO in an MDP—to address both adverse selection and moral hazard in dynamic Web 3.0 environments. Empirical results show MoE-PPO outperforms several DRL baselines in train, test, and final rewards and can be deployed on Ethereum Remix, illustrating practical viability for tokenized UGC incentives. Future work includes fine-tuning LMMs with real datasets and integrating transformer architectures to better capture sequential dependencies in state features.

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 \textit{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.

Figures (7)

• Figure 1: The lifecycle of UGC in Web 3.0, mainly consisting of five steps: identity authentication, content creation, decentralized storage, content verification, and economic incentives.
• Figure 2: A procedure of UGC evaluation through an LMM agent. Part A illustrates the initial setting of the LMM agent. Part B introduces few-shot prompting to enhance its performance of UGC evaluation. Part C presents evaluation cases conducted by the LMM agent, where CoT prompting is employed to guide it to generate accurate results. Note that in practical implementation, we append the prompt "Please directly output the quality rating" to accelerate the UGC evaluation.
• Figure 3: The diagram of the MoE-based PPO algorithm for optimal contract design. LMM agents are first enhanced through prompt engineering (Step 1), after which they evaluate the quality of the input UGC (Step 2). In Steps 3 to 8, the training process of the MoE-based PPO algorithm is presented.
• Figure 4: The probability functions for two user types with the shape parameter $\beta$ as the independent variable. We consider user reputation with two types: low and high 10925877, and set the value of $\alpha$ to $1$. Note that, when $\beta$ is fixed, the probability function with the shape parameter $\alpha$ as the independent variable is symmetrical to the current probability function.
• Figure 5: Performance evaluation of the proposed MoE-based PPO algorithm in optimal contract design.

Theorems & Definitions (2)

• Definition 1
• Definition 2