arXiv Machine Learning August 4, 2026 By Behnam Asadi

Dimension-Calibrated Unexplained Mass: An Interpretable GMM Drift Statistic that Matches Kernel Two-Sample Tests

Read the original on arXiv Machine Learning →

arXiv:2607. 16811v3 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

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 14

Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams

arXiv:2607. 16811v4 Announce Type: replace Abstract: Drift detectors that work tend not to explain themselves, and drift detectors that explain themselves tend to fail in high dimension.

By Behnam Asadi

benchmarks

arXiv Machine Learning

Jul 21

Interpretable Anomaly and Drift Detection with Gaussian Mixture Models

arXiv:2607. 16811v1 Announce Type: new Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams.

By Behnam Asadi

benchmarks

arXiv Computation and Language

Sep 1

A Hub of Short Rows Inflates Intrinsic Dimension Estimation of Token Embeddings

arXiv:2608.29702v1 Announce Type: new Abstract: A token-embedding table holds a hub of short rows near its origin, and we show that this cluster biases what nearest-neighbor intrinsic-dimension (ID)...

By Alexandre Quemy

llms rag

arXiv Machine Learning

Jun 9

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods

arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.

By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet

benchmarks

arXiv Machine Learning

Aug 4

ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection

arXiv:2608. 01793v1 Announce Type: new Abstract: Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples.

By Camile Lendering, Erkut Akdag, Joaqu\'in Figueira, Egor Bondarev

rag diffusion benchmarks

arXiv Machine Learning

Sep 2

Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects

This paper presents a reproducibility audit of frozen‑encoder anomaly detection experiments originally reported on arXiv. The authors confirm that the numerical discrimination results can be reproduced from the preserved artifacts, but they find that the claimed causal link to interferometric pretraining is unsupported. They show that near‑zero embeddings and architectural choices, rather than a morphological prior from gravitational‑wave instrumentation, explain the observed anomaly‑detection performance.

By Jose S\'anchez Andreu

rag

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