SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

arXiv:2607.22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We prop

延伸阅读

1. SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
2. Codifying the Judge: Scalable Evaluation via Program Distillation
3. MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models