arXiv Machine Learning July 14, 2026 By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

Detecting Explanatory Insufficiency in Learned Representations: A Framework for Representational Vigilance

Read the original on arXiv Machine Learning →

arXiv:2606. 13172v2 Announce Type: replace Abstract: Learned representations are central to modern machine learning and are commonly evaluated through predictive performance, robustness, uncertainty estimation, and generalization.

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

benchmarks

arXiv Machine Learning

Aug 5

Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from Training to Deployment

arXiv:2608. 02786v1 Announce Type: new Abstract: AI systems can fail silently.

By Priyanka Bajaj (Independent Researcher)

llms fine-tuning benchmarks

arXiv AI

Jul 2

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.

By Shalaleh Rismani, Roel Dobbe, AJung Moon

llms reinforcement-learning safety

arXiv Machine Learning

Jul 23

Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance

arXiv:2607. 19386v1 Announce Type: new Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores.

By Sinie van der Ben, Neele Roch, Anna Hedstr\"om, Mennatallah El-Assady

llms safety

arXiv AI

Jul 28

Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis

arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.

By Yuntong Chen, Jianyu Liu, Guobin Zhao, Ziang Wang, Chao Chen, Ju Huang, Xitian Tian, Lijiang Huang

llms fine-tuning safety

arXiv AI

Jun 11

Models That Know How Evaluations Are Designed Score Safer

arXiv:2605. 28591v2 Announce Type: replace-cross Abstract: The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings.

By Katharina Deckenbach, Haritz Puerto, Jonas Geiping, Sahar Abdelnabi

fine-tuning benchmarks safety

arXiv AI

Jun 8

Beyond the Black Box: Interpretability of Agentic AI Tool Use

arXiv:2605. 06890v3 Announce Type: replace Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagnose and control.

By Hariom Tatsat, Ariye Shater

llms agents benchmarks safety

Related stories

Evaluation Blindness: How Silent Measurement Failures Corrupt AI Systems from Training to Deployment

From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems

Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance

Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis

Models That Know How Evaluations Are Designed Score Safer

Beyond the Black Box: Interpretability of Agentic AI Tool Use