Helicase: Uncertainty-Guided Supply Chain Knowledge Graph Construction with Autonomous Multi-Agent LLMs
arXiv:2605.26835v1 Announce Type: new Abstract: LLM-based multi-agent systems have been widely adopted for knowledge retrieval and report generation, synthesizing known information through web search and textual reasoning. However, many critical information tasks in supply chains are not simple one-shot queries: they are structural inference problems requiring multi-hop reasoning across complex, fragmented web resources. Questions such as \textit{``Which Tesla components use lithium from Australian mines?''} have no answer in any single document; answers must be computationally synthesized thr
The proliferation of more capable large language models and multi-agent system architectures is enabling new approaches to complex data synthesis and knowledge graph construction.
This development addresses a critical need for inferential reasoning over fragmented data, which is essential for understanding and managing intricate global supply chains.
Supply chain transparency and resilience can be significantly enhanced through autonomous, uncertainty-guided knowledge graph construction, moving beyond simple information retrieval.
- · Supply Chain Management Software Providers
- · Logistics and Manufacturing Companies
- · AI Agent Developers
- · Consultancies specializing in supply chain optimization
- · Traditional Manual Data Analysts
- · Companies with Opaque Supply Chains
- · Inefficient Information Intermediaries
Companies can identify and mitigate supply chain risks with greater speed and accuracy.
Enhanced supply chain visibility could lead to more robust geopolitical risk assessments and strategic sourcing decisions.
The ability to rapidly reconfigure supply chains based on dynamic, inferred knowledge may lead to entirely new models of distributed manufacturing and resource allocation.
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Read at arXiv cs.AI