AGENTIC-SYSTEMSJuly 28, 20261 min read

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

Researchers introduced SeT-Diff, the first semantic foundation model for HPC telemetry and time-series data using diffusion techniques. It enables highly adaptable digital twins capable of forecasting, data imputation, and virtual sensing with high accuracy.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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 propose SeT-Diff, the first foundational model for compute node telemetry and time-series. Unlike rigid architectures, our diffusion-based approach conditions the generative process on each sensor's semantic description, decoupling the system dynamics from the structure of the dataset. Experiments on a real-world supercomputer dataset demonstrate a Mean Absolute Error (MAE) of 0.0470 on reconstruction tasks. SeT-Diff exhibits zero-shot permutation stability, maintaining accuracy with negligible degradation even when sensors are shuffled. A single pre-trained model effectively performs data imputation, forecasting, and virtual sensing - achieving a 0.033 MAE in thermal inference - making SeT-Diff an effective data-driven digital twin for HPC systems.

Current browse context:

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Concept-based Visual Counterfactual Explanations with Diffusion Models

Researchers introduced C-VCE, a novel diffusion framework that generates human-interpretable visual counterfactual explanations by building concept bottleneck layers directly into the generative model.

agentic-systems

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

Researchers have developed ScenarioGeneratorAgent, an automated pipeline that generates synthetic evaluation scenarios for Industry 4.0 AI agents. Tested on Smart Grid Transformers, the approach accelerates benchmark generation by 8x while maintaining high quality and adherence to IEC standards.

agentic-systems

Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy

A new study reveals that large language models (LLMs) often produce inconsistent answers when the same question is rephrased, with mismatch rates exceeding 23%. The findings suggest that standard benchmark accuracy masks substantial instability, but a self-paraphrasing strategy can help recover latent knowledge at inference time.

agentic-systems

DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

By leveraging a structured five-stage agentic workflow, the 7-billion-parameter DeepLens Diagnosis Agent outperforms frontier models like Claude Sonnet 4.5 and Gemini 3.1 Pro on complex clinical diagnostics, demonstrating that disciplined process design can outweigh raw parameter count and API costs.

agentic-systems

MedLoCoMo: A Long-Context Multi-Session Medical Dialogue Benchmark for Large Language Models

MedLoCoMo is a new medical benchmark designed to evaluate LLMs' clinical reasoning over multi-session, long-context patient histories. Experiments reveal that reasoning across different hospital admissions remains particularly difficult for current AI models.

agentic-systems

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

Researchers have introduced SCAIR, a training-free agentic framework that enhances Knowledge Graph-based RAG (KG-RAG) for enterprise applications by injecting schema constraints into multi-hop iterative reasoning.