arXiv:2607. 13035v1 Announce Type: cross Abstract: Cloud services experience frequent incidents that require rapid diagnosis and resolution.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608. 12133v1 Announce Type: new Abstract: Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images.
arXiv:2606. 09852v1 Announce Type: cross Abstract: High-quality source code documentation is vital yet often neglected, especially in critical domains like healthcare where reliability and maintainability are essential.
arXiv:2606. 09090v1 Announce Type: cross Abstract: Developers increasingly provide AI coding assistants with persistent context through configuration files such as CLAUDE.
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
arXiv:2607. 24348v1 Announce Type: cross Abstract: Advanced Persistent Threats (APTs) are difficult to detect and interpret due to their multi-stage and stealthy nature.
The paper examines how Vision Language Models (VLMs) can automatically extract structured procedural knowledge from industrial troubleshooting guides, which are typically flowchart-like diagrams combining spatial layout and technical language. It evaluates two VLMs using two prompting strategies—standard instruction-guided and an augmented approach that highlights layout patterns—and finds that each model shows different trade-offs between sensitivity to layout and robustness to semantic content. These insights help determine which VLM and prompting method is most suitable for integrating such guides into operator support systems.