DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination

2025-12-24

AI Summary

In untrusted environments, LLM-based multi-agent systems face core challenges including non-auditable task execution, unfair incentive allocation, leakage of strategic privacy, and high on-chain overhead. To address these, this paper proposes a hybrid verifiable collaboration architecture integrating on-chain DAO governance, zero-knowledge Shapley-value-based contribution verification (ZK-SCV), and a lightweight on-chain ZKP verification mechanism. Our approach is the first to simultaneously achieve fair incentive allocation under strategic privacy preservation and end-to-end auditability. Crucially, it reduces on-chain verification complexity to constant time and slashes gas costs by 99.9%. We empirically validate the full pipeline on cryptographic trading tasks, demonstrating support for large-scale agent coordination while balancing transparency, privacy, and scalability.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.

Problem

• Enables auditable task execution for autonomous LLM agents in trustless environments
• Ensures fair incentive distribution while preserving strategic privacy of agents
• Minimizes on-chain costs for decentralized multi-agent coordination

Innovation

• On-chain DAO governance for transparent coordination
• ZKP mechanism for off-chain contribution measurement
• Hybrid architecture verifies ZKP on-chain with low overhead