arXiv Machine Learning June 25, 2026 By Xun Jiao, Fred Lin, Harish D. Dixit, Joel Coburn, Sajin Nair, Abhinav Pandey, Han Wang, Venkat Ramesh, Jianyu Huang, Daniel Moore, Sriram Sankar

PVF:Understanding AI Vulnerability Against SDCs

Read the original on arXiv Machine Learning →

arXiv:2405. 01741v4 Announce Type: replace-cross Abstract: Reliability of AI systems is a fundamental concern for the successful deployment and widespread adoption of AI technologies.

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

arXiv AI

Aug 12

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

arXiv:2608. 10530v1 Announce Type: cross Abstract: Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory.

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arXiv AI

Jun 19

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.

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llms fine-tuning benchmarks

arXiv Machine Learning

Jul 3

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.

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arXiv AI

Aug 11

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

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arXiv AI

Jun 26

Hierarchical Fault Detection and Diagnosis for Transformer Architectures

arXiv:2604. 28118v2 Announce Type: replace-cross Abstract: Transformers now underpin critical AI systems across industry and research.

By Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma, Mohammad Masudur Rahman

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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)

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Related stories

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

Hierarchical Fault Detection and Diagnosis for Transformer Architectures

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