arXiv:2607. 06111v1 Announce Type: cross Abstract: Industrial prediction and soft sensing depend on credible input measurements.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
arXiv:2603. 25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deployment.
arXiv:2607. 06623v1 Announce Type: cross Abstract: Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online.
arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.
arXiv:2606. 29654v1 Announce Type: new Abstract: Multi-agent deliberation among LLMs can improve reasoning, but deployment requires deciding when the current answer is reliable enough to act on and when it should be escalated to human review.
arXiv:2608. 14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief.
arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.
arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.
arXiv:2601. 21293v3 Announce Type: replace-cross Abstract: Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery.
arXiv:2607. 06625v1 Announce Type: cross Abstract: Process industries have accumulated validated specialist models, yet sensor drift, feedstock variation, and regime switching cause these models to degrade systematically in new scenarios.
The paper proposes a Diagnostic Evidence Network (DENet) that augments traditional fault‑diagnosis outputs with structured evidence, including classification, predicted characteristic frequency, and temporal localization of impulses. This evidence aligns with theoretical bearing physics and can be validated at inference time, achieving high AUROC for misclassification detection without sacrificing accuracy. A QLoRA‑adapted language model then translates DENet’s evidence into maintenance reports, markedly reducing unsupported claims.
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.