A novel multi-agent architecture has been developed to support human-AI decision-making in high-precision manufacturing, particularly for CNC machining of complex aerospace components1. This framework integrates physics-based models with AI-driven agents to provide risk-aware and traceable decision support. Unlike off-the-shelf large language models, this approach enables the reliable execution of multi-step numerical workflows and generates auditable provenance for high-stakes decisions. The architecture is designed to incorporate process knowledge, simulation, and inspection data to inform bounded compensations and optimize manufacturing outcomes. By providing a transparent and explainable decision-making process, this framework can increase trust in AI-driven decision support systems. This matters to manufacturing practitioners because it has the potential to improve the accuracy and reliability of high-precision machining, reducing the risk of errors and improving overall product quality.
Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing
⚠️ Critical Alert
Why This Matters
Off-the-shelf large language model (LLM) assistants can generate text, but they do not reliably execute risk-constrained multi-step numerical workflows or provide auditable provena
References
- Author. (2026, May 5). Physics-Grounded Multi-Agent Architecture for Traceable, Risk-Aware Human-AI Decision Support in Manufacturing. arXiv. https://arxiv.org/abs/2605.04003v1
Original Source
arXiv AI
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