arXiv:2608. 13719v1 Announce Type: new Abstract: Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2503. 08936v3 Announce Type: replace-cross Abstract: Scenario-based testing with driving simulators is extensively used to identify failing conditions of automated driving assistance systems (ADAS).
arXiv:2604. 23099v2 Announce Type: replace-cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks.
arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.
arXiv:2607. 14439v1 Announce Type: new Abstract: Generalist robot manipulation policies trained on large, diverse datasets have shown remarkable promise across a wide range of tasks.
arXiv:2606. 31114v1 Announce Type: new Abstract: Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely.
arXiv:2607. 23134v1 Announce Type: new Abstract: Discovering rare safety-critical failures in autonomous and cyber-physical systems is a fundamental challenge in verification and validation.