arXiv:2604. 28118v2 Announce Type: replace-cross Abstract: Transformers now underpin critical AI systems across industry and research.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.
arXiv:2607. 11193v1 Announce Type: cross Abstract: To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired.
arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.
arXiv:2606. 26492v1 Announce Type: cross Abstract: Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task.
arXiv:2405. 01741v4 Announce Type: replace-cross Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies.
arXiv:2606. 26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data.