arXiv Machine Learning June 5, 2026 By Raffael Theiler, Lev Telyatnikov, Leandro Von Krannichfeldt, Olga Fink

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

Read the original on arXiv Machine Learning →

arXiv:2606. 05481v1 Announce Type: new Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

arXiv Machine Learning

Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

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llms benchmarks

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Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

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llms diffusion nlp benchmarks

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LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

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llms rag benchmarks

arXiv Machine Learning

Jul 21

Tabular Foundation Models Can Do Survival Analysis

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llms

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PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model

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fine-tuning

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Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation

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