QOC DAO - Stepwise Development Towards an AI Driven Decentralized Autonomous Organization
- URL: http://arxiv.org/abs/2511.08641v1
- Date: Thu, 13 Nov 2025 01:01:19 GMT
- Title: QOC DAO - Stepwise Development Towards an AI Driven Decentralized Autonomous Organization
- Authors: Marc Jansen, Christophe Verdot,
- Abstract summary: This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO)<n>We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.
Related papers
- Increasing AI Explainability by LLM Driven Standard Processes [0.0]
This paper introduces an approach to increasing the explainability of artificial intelligence (AI) systems by embedding Large Language Models (LLMs) within standardized analytical processes.<n>A layered architecture is presented that separates the reasoning space of the LLM from the explainable process space above it.<n> Empirical evaluations show that the system can reproduce human-level decision logic in decentralized governance, systems analysis, and strategic reasoning contexts.
arXiv Detail & Related papers (2025-11-10T13:16:10Z) - Reasoning Is All You Need for Urban Planning AI [3.3943213418026126]
This paper presents the Agentic Urban Planning AI Framework for reasoning-capable planning agents.<n>It integrates three cognitive layers (Perception, Foundation, Reasoning) with six logic components (Analysis, Generation, Verification, Evaluation, Collaboration, Decision) through a multi-agents collaboration framework.<n>We show how AI agents can augment human planners by systematically exploring solution spaces, verifying regulatory compliance, and deliberating over trade-offs transparently.
arXiv Detail & Related papers (2025-11-07T15:59:06Z) - DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance [7.230919380272301]
This paper presents a first empirical study of agentic AI as autonomous decision-makers in decentralized governance.<n>We build an agentic AI voter that interprets proposal contexts, retrieves historical deliberation data, and independently determines its voting position.<n>The study contributes to the design of explainable and economically rigorous AI agents for decentralized financial systems.
arXiv Detail & Related papers (2025-10-24T03:13:14Z) - A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
arXiv Detail & Related papers (2025-08-10T16:07:32Z) - Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges [49.69200207497795]
The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems.<n>This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms.
arXiv Detail & Related papers (2025-08-04T15:44:58Z) - The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
arXiv Detail & Related papers (2025-06-09T18:37:14Z) - Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
arXiv Detail & Related papers (2025-03-12T21:39:38Z) - Decentralized Governance of Autonomous AI Agents [0.0]
ETHOS is a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs)<n>It establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring.<n>By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance.
arXiv Detail & Related papers (2024-12-22T18:01:49Z) - Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks.
We show that the learned AI control system demonstrates robustness against adversarial tampering.
In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
arXiv Detail & Related papers (2024-07-03T15:38:57Z) - Measuring Value Alignment [12.696227679697493]
This paper introduces a novel formalism to quantify the alignment between AI systems and human values.
By utilizing this formalism, AI developers and ethicists can better design and evaluate AI systems to ensure they operate in harmony with human values.
arXiv Detail & Related papers (2023-12-23T12:30:06Z) - Rational Decision-Making Agent with Internalized Utility Judgment [88.01612847081677]
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step decision-making tasks beyond traditional NLP applications.<n>This paper proposes RadAgent, which fosters the development of its rationality through an iterative framework involving Experience Exploration and Utility Learning.<n> Experimental results on the ToolBench dataset demonstrate RadAgent's superiority over baselines, achieving over 10% improvement in Pass Rate on diverse tasks.
arXiv Detail & Related papers (2023-08-24T03:11:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.