Resilient Multi-Agent Negotiation for Medical Supply Chains:Integrating LLMs and Blockchain for Transparent Coordination

2025-07-22

AI Summary

To address inefficiencies in resource allocation, lack of transparency, and insufficient dynamic adaptability in healthcare supply chains during public health emergencies such as COVID-19, this paper proposes a blockchain–large language model (LLM)–integrated multi-agent negotiation system. The system adopts a cross-layer architecture comprising off-chain LLM-powered agents for context-aware, semantic negotiation and on-chain smart contracts for tamper-proof, auditable execution, supported by a lightweight cross-layer communication protocol to ensure decision consistency. Innovatively embedding LLMs within a multi-agent framework enables adaptive, natural-language-based negotiation, while blockchain guarantees end-to-end auditability and immutability. In simulated pandemic scenarios, the system achieves significant improvements: 37% higher negotiation efficiency, 29% reduction in Gini coefficient (enhancing fairness), 42% faster response latency, and substantially improved audit transparency—establishing a verifiable, resilient, and ethics-aligned paradigm for emergency supply chain management.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Security and Privacy: Blockchains and distributed ledgers

Abstract

Global health emergencies, such as the COVID-19 pandemic, have exposed critical weaknesses in traditional medical supply chains, including inefficiencies in resource allocation, lack of transparency, and poor adaptability to dynamic disruptions. This paper presents a novel hybrid framework that integrates blockchain technology with a decentralized, large language model (LLM) powered multi-agent negotiation system to enhance the resilience and accountability of medical supply chains during crises. In this system, autonomous agents-representing manufacturers, distributors, and healthcare institutions-engage in structured, context-aware negotiation and decision-making processes facilitated by LLMs, enabling rapid and ethical allocation of scarce medical resources. The off-chain agent layer supports adaptive reasoning and local decision-making, while the on-chain blockchain layer ensures immutable, transparent, and auditable enforcement of decisions via smart contracts. The framework also incorporates a formal cross-layer communication protocol to bridge decentralized negotiation with institutional enforcement. A simulation environment emulating pandemic scenarios evaluates the system's performance, demonstrating improvements in negotiation efficiency, fairness of allocation, supply chain responsiveness, and auditability. This research contributes an innovative approach that synergizes blockchain trust guarantees with the adaptive intelligence of LLM-driven agents, providing a robust and scalable solution for critical supply chain coordination under uncertainty.

Problem

• Enhancing resilience in medical supply chains during crises
• Integrating blockchain and LLMs for transparent coordination
• Improving negotiation efficiency and fairness in resource allocation

Innovation

• Integrates blockchain with LLM multi-agent negotiation
• Uses smart contracts for transparent decision enforcement
• Combines adaptive reasoning with decentralized negotiation