Fault Detection and Control Systems

Últimos artículos

• BayesNDE: Bayesian Generative Modeling for Neural Density Estimation

  Chenglin Li, Qiao Liu · 1 de octubre de 2026

  Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based on Bayesian generative modeling. BayesNDE learns a Bayesian generative model and evaluates its density without requiring invertible networks or Jacobi…

• When World Models Lie: Adaptive Safety Analysis Under Wrong Imaginations

  John Cao, Somil Bansal · 29 de septiembre de 2026

  World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be biased, miscalibrated, or confidently wrong. This creates a central challenge for latent-space safety filters, which often learn Hamilton-Jacobi safe…

• STAMP: Predicting Out-of-Distribution Generalization without Target Data

  Md Kawsher Mahbub, Milon Biswas · 29 de septiembre de 2026

  Predicting whether a trained model will generalize under distribution shift remains difficult, especially when target-domain data are unavailable. We introduce STAMP (Semantic Temporal Augmented Model Prediction), a source-only, target-label-free criterion that estimates out-of-distribution (OOD) pe…

• RSD-Poker: Structure-Adaptive and Shift-Robust Risk-Utility Certification for Residual Policies in Imperfect-Information Games

  Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Peng Zhang, Daren Zha, Jun Xiao · 29 de septiembre de 2026

  Residual policy adaptation provides a lightweight way to modify a strong reference policy, but a shared scale and a fixed subgroup partition can hide heterogeneous degradation and become fragile when the deployment mixture of information states changes. We introduce RSD-Poker, a structure-adaptive a…

• When 10,000 Windows Are Not 10,000 Tests: Auditing Statistical Confidence in Sliding-Window Time-Series Classification

  Xinze Shi, Litian Zhang, Binrui Shi · 28 de septiembre de 2026

  Sliding-window classifiers are often evaluated on thousands of overlapping test windows, even though neighboring predictions share observations and remain nested within recordings and subjects. Subject-disjoint evaluation prevents one form of leakage but does not make those test windows independent.…

• Conditional Predictive Sufficient Statistics for Visual Representation Learning

  Yuzhou Hong · 28 de septiembre de 2026

  A useful visual representation is a statistic of the observed past that retains the latent factors shared with the future and discards patch-private noise. We formalize this requirement as a conditional predictive sufficient statistic (CPSS). Under a shared-factor model of image patches, the mutual …

• Intrinsic-Extrinsic Coupling in Learning Dynamics

  Qinyou Wang · 25 de septiembre de 2026

  A learner's current observations need not determine its response to further training. We formulate intrinsic-extrinsic coupling through the continuation-conditioned value of a constrained learning-state intervention, with observation-relative fibers describing present agreement. An executable finite…

• Certified Task-Conditioned Active Observability

  Linzhe Zhang, Changming Xu · 25 de septiembre de 2026

  Before acting upon an unobservable physical system, an autonomous agent must determine which latent distinctions govern downstream tasks, how many active interventions are necessary to certify them, and when to abstain to prevent catastrophic errors. Classical observability treats state reconstructi…

• Discovery of fully efficient fault indicators along a data-based diagnosis process

  Igor Bezmaternykh (INSA Toulouse), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS) · 24 de septiembre de 2026

  The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learn…

• Median Temporal Ensembling: Training-Free Robust Aggregation for Action-Chunked Visuomotor Policies

  Yuhang Jiang · 24 de septiembre de 2026

  Action-chunked visuomotor policies predict overlapping trajectories, so every executed action is covered by several predictions. Temporal ensembling smooths execution by combining these predictions with an exponentially weighted mean. One corrupted prediction can move the aggregate without bound: it…

• Risk-Aware Online Conformal State Probing

  Pietro Talli, Petar Popovski, Osvaldo Simeone · 23 de septiembre de 2026

  AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are …

• The Source of Disturbance Matters: External, Internal, and Control-Generated Noise in Adaptive Regulation

  Veronique Ziegler · 23 de septiembre de 2026

  Adaptive regulation can itself perturb the state it is intended to stabilize. In replicated simulations of an adaptive agent, we compare external disturbance, persistent internally generated disturbance, and control-generated disturbance under regulation-first and disturbance-first ordering. Persist…

• When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads

  Zhengshu Zhang · 22 de septiembre de 2026

  When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt. For such diagnosis to improve behavior, six ordered empirical conditions must hold: a meaningful reference, identifiability of the physical condition, use of the acquired evidenc…

• What Can a Recurrent State Safely Forget?

  Linzhe Zhang, Changming Xu · 22 de septiembre de 2026

  Recurrent models must preserve information that changes future behavior while suppressing hidden-state error. These objectives conflict: contraction improves stability, but contraction along a future-distinguishing direction destroys memory. We formalize this boundary through the predictive quotient…

• On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation

  Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi, Po Yang · 21 de septiembre de 2026

  Substantial efforts have been devoted to making deep learning objectives, representations, and architectures interpretable, with the goal of improving the safety, robustness, and generalisation of learning systems in diverse real-world applications. The recently proposed maximal coding rate reductio…

• Diagnose, Recover, Certify: Task Readiness under Hidden Dynamics Changes

  Nguyen Viet Tuan Kiet, Huynh Thi Thanh Binh · 18 de septiembre de 2026

  A deployed control policy can conceal consequential dynamics changes: an actuator may lose effectiveness without affecting the current task when the policy rarely excites it, despite being critical for a future task that has not yet been specified. We introduce task readiness under dormant dynamics …

• Reliable Virtual Sensing: A Multi-Domain Benchmark for Robustness Under Sensor Failures

  Jens U. Brandt, Noah C. Puetz, Alexander Windmann, Marc Hilbert, Elena Raponi, Thomas B\"ack, Thomas Bartz-Beielstein · 17 de septiembre de 2026

  Virtual sensing, the estimation of hard-to-measure quantities from available sensor measurements, is a critical enabler for control and monitoring in cyber-physical systems. However, when sensors fail, learning-based predictors can produce physically implausible estimates that propagate to system-le…

• Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap

  Ayoub Louaye Bouaziz, Matthieu Ostertag, Anton Demasles · 16 de septiembre de 2026

  Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control units. We study a schema-adaptive action-conditioned Joint-Embedding Predictive Architecture (SAAC-JEPA) for CNC dynamics, where the source machine has …

• Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

  Tesfay Zemuy Gebrekidan, Hadush Hailu Gebrerufael · 15 de septiembre de 2026

  A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. We test that premise using exact polynomial invariants recovered from trajectories and canonicalised as reduced Gr\"obner bases over $\mathbb{Q}$. On Acrobo…

• Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

  Rachel Longjohn, Giri Gopalan, Emily Casleton · 14 de septiembre de 2026

  Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of downstream tasks. To summarize performance across multiple tasks, evaluation metrics are often aggregated into a summar…

• Large Distant Gradients Need Not Be Reliable: reliability-weighted credit assignment for long-horizon autoregressive forecasting

  Junhao Zhao, David Michael Simberg, Jacob Kang, Colin Connor Kurniawan, Nan Xu · 14 de septiembre de 2026

  In autoregressive forecasting, long prediction rollouts provide distant supervision, but backpropagation through time (BPTT) carries gradients from those losses through many autoregressive steps. Repeated Jacobian products can make distant gradients dominate the update while amplifying predictable s…

• Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach

  Adri\'an Detavernier, Jasper De Bock · 11 de septiembre de 2026

  We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM): naive Bayes classifiers and generative forests (a probabilistic extension of random forests). Following the paradigm of …

• Algorithmic stability via ensembling

  Rina Foygel Barber, Richard J. Samworth · 10 de septiembre de 2026

  Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may vary depending on the setting. In this work, we develop a general framework to quantify the extent to which any ensembling strategy defined via averag…

• Cost-Aware Post-Hoc Deferral Under Calibration and Shift: An Environmental AI Case Study

  Haoran Yu, Lifei Liu, Danping Zhang · 10 de septiembre de 2026

  Choosing a deferral policy for a frozen classifier requires more than ranking uncertain cases: confidence may be miscalibrated, errors have unequal costs, reviewers can err, and deployment data can leave calibration support. We study these interactions through EcoTrust, a post-hoc framework that com…

• Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground

  Benny Platte (Mittweida University of Applied Sciences), Rico Thomanek (Mittweida University of Applied Sciences), Christian Roschke (Mittweida University of Applied Sciences), Marc Ritter (Mittweida University of Applied Sciences) · 10 de septiembre de 2026

  Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and each change degrades performance in ways a random train-test split cannot reveal. We propose a staged, accountable evaluation protocol that treats the e…