Hallucination-aware learning and latency optimization transformer (HALL-OPT) for real-time edge intelligence.

Transformer architectures and large language models remain competitive across a broad range of AI tasks, making them challenging to deploy in resource-constrained edge computing environments due to high resource demands and the generation of erroneous or fake outputs (hallucinations). In this paper, a single scheme, HALL-OPT, is proposed to address both latency detection and reduction in hallucination for real-time edge intelligence. The paper presents three main elements of the framework, namely, (1) a dual-stream hallucination detector that analyses internal attention behaviour, (2) an adaptive token-pruning system, which decodes and extracts the necessary context at minimal computation, and (3) a lightweight edge-optimized transformer obtained by knowledge distillation. On SQuAD 2.0 and CNN/DailyMail, HALL-OPT detects hallucinations accurately at 94.3% and achieves a 67.8% reduction in inference latency with only a 2.1% decrease in accuracy compared to the BERT-base model. The system (when deployed on edge hardware) provides sub-50 ms response times while consuming 43% less energy. It is appropriate for real-time applications in industrial IoT, autonomous systems, healthcare monitoring, and other applications where low latency is critical. Existing transformer optimisation and hallucination mitigation approaches treat reliability and Efficiency as separate objectives, limiting their applicability in real-time edge environments. HALL-OPT uniquely integrates hallucination-aware attention, adaptive pruning, and edge-oriented optimisation into a single unified framework, enabling simultaneous reductions in hallucination, latency, and energy consumption. This integrated design distinguishes HALL-OPT from prior work that optimises accuracy or Efficiency in isolation.