arXiv Machine Learning July 9, 2026 By Joao Pinelo, Joao Goncalves, Arun Shukla, Adriana Santos-Ferreira

Prior-matched evaluation of operational Earth-observation classifiers: a three-number reporting method demonstrated on Sentinel-1 internal-wave detection

Read the original on arXiv Machine Learning →

arXiv:2607. 07146v1 Announce Type: new Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time is the resource the effort exists to conserve.

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

arXiv Machine Learning

Jun 24

Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics

arXiv:2606. 22054v2 Announce Type: replace-cross Abstract: Detectors for GNSS radio-frequency impairments (jamming, spoofing, multipath) are usually reported with a single AUC measured on the distribution they were tuned on.

By Chakshu Baweja

arXiv Machine Learning

Aug 4

Real-Time Detection and Repair of LLM Agent Failures

arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.

By Sunny Dubey

llms agents

arXiv Machine Learning

Jun 15

Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches

arXiv:2606. 13780v1 Announce Type: cross Abstract: Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it.

By Jack Y. Araz, Michael Spannowsky

arXiv Machine Learning

5d ago

Excess Separability: Nuisance-Controlled Residual-Stream Probing for Benchmark Contamination Detection

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By Florian Braun

llms benchmarks

arXiv Machine Learning

Jul 15

Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection

arXiv:2607. 11969v1 Announce Type: cross Abstract: Point-adjustment (PA), long the default scoring protocol in time-series anomaly detection (TSAD), was shown by Kim et al.

By Zongye Lyu

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arXiv AI

1d ago

Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling

arXiv:2608. 15565v1 Announce Type: new Abstract: Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide.

By Junbo Jacob Lian, Huiling Chen, Hanzhang Qin, Chung-Piaw Teo

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