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

arXiv: https://arxiv.org/abs/2512.20973 · HTML: https://mlsanity.com/paper/2512.20973/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 8 of 8)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [Global and Local Semantic Completion Learning for Vision-Language Pre-training](https://mlsanity.com/paper/2306.07096/) (2023)
- [Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach](https://mlsanity.com/paper/2509.16736/) (2025)
- [HiveMind: Contribution-Guided Online Prompt Optimization of LLM Multi-Agent Systems](https://mlsanity.com/paper/2512.06432/) (2025)
- [Design and Evaluation of IPFS: A Storage Layer for the Decentralized Web](https://mlsanity.com/paper/2208.05877/) (2022)
- [Swarm Contract: A Multi-Sovereign Agent Consensus Mechanism](https://mlsanity.com/paper/2412.19256/) (2024)

## Built on by (most cited first, 3 of 3)

- [Coalition Formation in LLM Agent Networks: Stability Analysis and Convergence Guarantees](https://mlsanity.com/paper/2604.14386/) (2026)
- [Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions](https://mlsanity.com/paper/2608.04626/) (2026)
- [SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems](https://mlsanity.com/paper/2609.00595/) (2026)

## Abstract (arXiv)

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.