On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins
arXiv:2603.25898v3 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and sensor data. However, resilience to LLM hallucination, human oversight, and real-time model adaptability remain challenging and often mutually conflicting requirements. We present three critical design principles for integrating resilience and oversight into such workflows, derived from insights gained through our work on FactoryFlow - an open-source LLM-assisted framework for building simulation-based
The rapid advancement of LLMs necessitates addressing their integration challenges, particularly regarding reliability and human oversight, as their application expands into critical engineering domains like digital twins.
This research directly tackles pressing issues of trustworthiness and control in advanced AI applications, impacting the viability and safety of LLM-assisted systems like digital twins for complex industrial and societal infrastructure.
The focus shifts from merely demonstrating LLM capabilities to developing principled approaches for their resilient and accountable deployment in real-world engineering workflows, emphasizing human-AI co-evolution in design.
- · AI safety researchers
- · Industrial automation sector
- · Digital twin developers
- · Manufacturers adopting LLM-assisted design
- · Organizations deploying LLMs without robust oversight
- · Developers ignoring resilience principles
- · Traditional modeling approaches
Increased confidence and adoption of LLM-assisted design and digital twin technologies across various industries due to enhanced reliability.
Reduced incidence of costly errors and system failures traced back to AI hallucinations or lack of human intervention in LLM-driven processes.
The acceleration of 'lights-out' manufacturing and self-optimizing infrastructure, with human roles evolving towards high-level supervision and ethical governance of AI-driven systems.
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Read at arXiv cs.AI