AI Agent Architecture for Decentralized Trading of Alternative Assets

• URL: http://arxiv.org/abs/2507.11117v1
• Date: Tue, 15 Jul 2025 09:11:19 GMT
• Title: AI Agent Architecture for Decentralized Trading of Alternative Assets
• Authors: Ailiya Borjigin, Cong He, Charles CC Lee, Wei Zhou,
• Abstract summary: GoldMine OS is a research oriented architecture that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain based stablecoin ("OZ")<n>We describe four cooperative agents (Compliance, Token Issuance, Market Making, and Risk Control) and a coordinating core, and evaluate the system through simulation and a controlled pilot deployment.<n>In experiments the prototype delivers on demand token issuance in under 1.2 s, more than 100 times faster than manual.
• Score: 2.8195433571821162
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Decentralized trading of real-world alternative assets (e.g., gold) requires bridging physical asset custody with blockchain systems while meeting strict requirements for compliance, liquidity, and risk management. We present GoldMine OS, a research oriented architecture that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain based stablecoin ("OZ"). Our approach combines on chain smart contracts for critical risk controls with off chain AI agents for decision making, blending the transparency and reliability of blockchains with the flexibility of AI driven automation. We describe four cooperative agents (Compliance, Token Issuance, Market Making, and Risk Control) and a coordinating core, and evaluate the system through simulation and a controlled pilot deployment. In experiments the prototype delivers on demand token issuance in under 1.2 s, more than 100 times faster than manual workflows. The Market Making agent maintains tight liquidity with spreads often below 0.5 percent even under volatile conditions. Fault injection tests show resilience: an oracle price spoofing attack is detected and mitigated within 10 s, and a simulated vault mis reporting halts issuance immediately with minimal user impact. The architecture scales to 5000 transactions per second with 10000 concurrent users in benchmarks. These results indicate that an AI agent based decentralized exchange for alternative assets can satisfy rigorous performance and safety requirements. We discuss broader implications for democratizing access to traditionally illiquid assets and explain how our governance model -- multi signature agent updates and on chain community voting on risk parameters -- provides ongoing transparency, adaptability, and formal assurance of system integrity.

Related papers

• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• Hide-and-Shill: A Reinforcement Learning Framework for Market Manipulation Detection in Symphony-a Decentralized Multi-Agent System [4.808769904090313]
  Decentralized finance (DeFi) has introduced a new era of permissionless financial innovation but also led to unprecedented market manipulation.<n>We propose a Multi-Agent Reinforcement Learning framework for decentralized manipulation detection, modeling the interaction between manipulators and detectors as a dynamic adversarial game.<n>This framework identifies suspicious patterns using delayed token price reactions as financial indicators.
  arXiv  Detail & Related papers  (2025-07-12T07:55:40Z)
• Building crypto portfolios with agentic AI [46.348283638884425]
  The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
  arXiv  Detail & Related papers  (2025-07-11T18:03:51Z)
• AI-Governed Agent Architecture for Web-Trustworthy Tokenization of Alternative Assets [3.0801485631077457]
  Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web.<n>This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets.
  arXiv  Detail & Related papers  (2025-06-30T11:28:51Z)
• Hybrid Stabilization Protocol for Cross-Chain Digital Assets Using Adaptor Signatures and AI-Driven Arbitrage [0.2621730497733947]
  Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance.<n>We present a hybrid cryptographic stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain pools.
  arXiv  Detail & Related papers  (2025-06-06T03:14:05Z)
• BotDetect: A Decentralized Federated Learning Framework for Detecting Financial Bots on the EVM Blockchains [3.4636217357968904]
  This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Virtual Machine (EVM)-based blockchains.<n>The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior.<n> Experimental results show that our DFL framework achieves high detection accuracy while maintaining scalability and robustness.
  arXiv  Detail & Related papers  (2025-01-21T13:15:43Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts [26.70294159598272]
  This paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second.
  arXiv  Detail & Related papers  (2023-08-30T07:17:20Z)
• Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
  Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology. One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds. This introduces the problem of finding an optimal strategy for choosing price intervals. We formalize this problem as an online learning problem with non-stochastic rewards.
  arXiv  Detail & Related papers  (2023-02-01T17:21:40Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)
• Regulation conform DLT-operable payment adapter based on trustless - justified trust combined generalized state channels [77.34726150561087]
  Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks. We give an overview of current solutions that differ in their fundamental values and technological possibilities. We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
  arXiv  Detail & Related papers  (2020-07-03T10:45:55Z)
• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning [55.20040781688844]
  QMIX is a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. We propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-19T16:51:51Z)

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.