Time, Causality, and Observability Failures in Distributed AI Inference Systems

Computer Science > Artificial Intelligence [Submitted on 23 Apr 2026] Time, Causality, and Observability Failures in Distributed AI Inference Systems Ankur Sharma, Deep Shah, David Lariviere, Hesham ElBakoury Distributed AI inference pipelines rely heavily on timestamp-based observability to understand system behavior. This work demonstrates that even small clock skew between nodes can cause observability to become causally incorrect while the system itself remains functionally correct and performant. We present controlled experiments on a multi-node AI inference pipeline, where clock skew is introduced at a single stage. Results show that no violations are observed under synchronized conditions and up to 3 ms skew, while clear causality violations emerge by 5 ms. Despite this, system throughput and output correctness remain largely unaffected. We further observe that violation behavior is not strictly static. In longer runs, negative span rates may stabilize or decrease over time, indicating that effective skew evolves due to relative clock drift between nodes. Experiments were conducted using Kafka and ZeroMQ transports, with consistent results across both. Aeron is under active exploration but is not yet included in the completed validation set. These findings suggest that observability correctness depends not only on system functionality but also on precise time alignment, and that timing must be treated as a first-class concern in distributed AI systems. Comments: 17 pages, 6 figures. Produced as part of the Unified Intelligent Infrastructure workstream at the Open Compute Project (OCP) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.21361 [cs.AI]   (or arXiv:2604.21361v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21361 Focus to learn more Submission history From: Deep Shah [view email] [v1] Thu, 23 Apr 2026 07:21:45 UTC (566 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)