arXiv Machine Learning July 3, 2026 By Weizhi Nie, Weijie Wang, Yuting Su

Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling

Read the original on arXiv Machine Learning →

arXiv:2607. 01986v1 Announce Type: new Abstract: Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent degradation process.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Aug 4

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

arXiv:2608. 01819v1 Announce Type: new Abstract: To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical.

By Fatih \"Urgen, Do\u{g}ay Alt{\i}nel

arXiv AI

Jun 12

When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics

arXiv:2601. 06227v3 Announce Type: replace-cross Abstract: Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints.

By Dhivya Dharshini Kannan, Wei Li, Wei Zhang, Jianbiao Wang, Zhi Wei Seh, Man-Fai Ng

efficiency

arXiv Machine Learning

2d ago

CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems

arXiv:2609.39784v1 Announce Type: new Abstract: Can heterogeneous physical degradation systems benefit from joint pretraining and move beyond system-specific prognostics toward reusable cross-system...

By Haibo Li, Zhiguo Zeng

arXiv Machine Learning

Jul 27

General Value Functions for Remaining Useful Life and Failure-Mode Prediction

arXiv:2607. 22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance.

By Hao Yan, Ali Sarabi, Qing Zou, Boyang Xu

arXiv Machine Learning

Jul 20

Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics

arXiv:2507. 14194v3 Announce Type: replace-cross Abstract: This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Gated Temporal Attention, a Spiking Neural Network (SNN) refinement stage, and a Temporal Fusion Transformer (TFT) classifier.

By David J Poland

llms

arXiv AI

Jul 29

Emergent Latent-State Computation under Stochastic Volatility

arXiv:2607. 25459v1 Announce Type: cross Abstract: Mechanistic interpretability has largely focused on language models and deterministic toy tasks.

By Xiaoyu Huang, Lulu Wang

llms benchmarks safety

One email a morning, machine-written

Related stories

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics

CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems

General Value Functions for Remaining Useful Life and Failure-Mode Prediction

Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics

Emergent Latent-State Computation under Stochastic Volatility