From Human Intent to Strategy Decisions: A Review Framework Integrating Large Language Models and Digital Twins

Abstract

Automation systems must adapt to changing tasks, equipment states, and staffing conditions while providing evidence for human review. This study presents an intent-review framework integrating large language models, digital twins, and human decision-making, implemented for multi-line task adjustment. A Propose-Verify-Decide workflow translates operator intent into structured requirements, generates a bounded set of candidate strategies, and checks semantics, simulation execution, and operational constraints. Linked records preserve traceability from requests to verification evidence and decisions. Thirty fixed test records were evaluated using four virtual surgical-instrument sorting lines: 28 assessed the workflow and two assessed model generation. Eighteen workflow cases met expectations (64.29%); three of ten strategy-workflow requests completed autonomously (30%); and seven of eight invalid inputs were correctly intercepted (87.5%). Analysis by anomaly source showed that the framework rejected two candidate batches that failed throughput constraints and paused recommendations in two cases with incomplete evidence. A mistranslated priority rule nevertheless reached simulation. These findings demonstrate handling of specific invalid inputs, throughput violations, and evidence gaps, and identify consistency between original intent and execution specifications as a key improvement area. The effect on overall erroneous decisions remains to be evaluated through matched comparisons, together with the influence of digital-twin fidelity using independent physical references.