arXiv:2511.07202v3 Announce Type: replace-cross Abstract: Failures are the norm in highly complex and heterogeneous devices spanning the distributed computing continuum (DCC), from resource-constrained IoT and edge nodes to high-performance computing systems. Ensuring reliability and global consistency across these layers remains a major challenge, especially for AI-driven workloads requiring real-time, adaptive coordination. This work-in-progress paper introduces a Probabilistic Active Inference Resilience Agent (PAIR-Agent) to achieve resilience in DCC systems. PAIR-Agent performs three core
The increasing complexity and heterogeneity of distributed computing systems, particularly for real-time AI workloads, necessitate new resilience mechanisms to cope with endemic failures.
Ensuring reliability and consistency across the compute continuum, from IoT to HPC, is critical for the continuous and robust operation of AI-driven applications that underpin many modern systems.
The explicit introduction of probabilistic active inference into distributed systems promises a more adaptive and self-healing approach to resilience, moving beyond reactive fault tolerance.
- · Edge computing providers
- · IoT device manufacturers
- · AI model developers
- · Cloud infrastructure providers
- · Companies with brittle, non-resilient distributed systems
- · Competitors without advanced AI-driven resilience capabilities
Improved reliability and uptime of critical AI applications across diverse computing environments.
Accelerated adoption of more complex, real-time AI agents and systems in industrial and societal infrastructure due to enhanced robustness.
Shift in system design philosophies towards 'resilient by design' principles, integrating probabilistic inference and adaptive control from the outset.
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Read at arXiv cs.AI