arXiv Machine Learning

ADEPT: A Unified Framework for Deep Learning Test Adequacy

arXiv:2608. 12144v1 Announce Type: cross Abstract: Over the past decade, many test adequacy metrics have been proposed for deep learning that characterize test dataset adequacy from different perspectives, e.

arXiv Machine Learning

Jun 4

Latent Anchor-Driven Test Generation for Deep Neural Networks

arXiv:2606. 04310v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate model weaknesses.

By Bin Duan, Matthew B. Dwyer, Guowei Yang

diffusion efficiency safety

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.

By Chaomeng Lu, Bert Lagaisse

llms benchmarks

arXiv AI

Jun 26

Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs

arXiv:2606. 26492v1 Announce Type: cross Abstract: Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task.

By Sigma Jahan

arXiv Machine Learning

Jul 15

Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.

By Sigma Jahan

benchmarks

arXiv Machine Learning

Jun 25

Training Dynamics of Neural Software Defect Predictors under Coupled Data-Quality Issues

arXiv:2606. 24968v1 Announce Type: new Abstract: Context: Software defect prediction supports maintenance decisions such as testing prioritization, release-risk assessment, and quality monitoring.

By Emmanuel Charleson Dapaah, Philip Makedonski, Jens Grabowski

data-engineering

arXiv AI

Jun 24

BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents

arXiv:2605. 06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering.

By Jinge Wu, Hongjian Zhou, Mingde Zeng, Jiayuan Zhu, Junde Wu, Jiazhen Pan, Ayush Noori, Sean Wu, Honghan Wu, Fenglin Liu, David A. Clifton

agents benchmarks

arXiv Machine Learning

Jun 4

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

arXiv:2606. 04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability.

By Bin Duan, Meiru Che, Guowei Yang

fine-tuning safety

arXiv AI

Jun 16

Agentic Framework for Deep Learning workload migration via In-Context Learning

arXiv:2606. 15994v1 Announce Type: new Abstract: Translating deep learning models from PyTorch's flexible, object-oriented design to JAX's functional, stateless setup is usually a manual and error-prone task.

By Qiyue Liang, Steven Ingram, George Vanica, Andi Gavrilescu, Newfel Harrat, Hassan Sipra, Sethuraman Sankaran

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jul 9

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.

By Tolgay Atinc Uzun, Waleed Khalid, Saif U Din, Sai Revanth Mulukuledu, Akashdeep Singh, Chandini Vysyaraju, Raghuvir Duvvuri, Avi Goyal, Yashkumar Rajeshbhai Lukhi, Muhammad A. Hussain, Krunal Jesani, Usha Shrestha, Yash Mittal, Roman Kochnev, Pritam Kadam, Mohsin Ikram, Harsh R. Moradiya, Alice Arslanian, Dmitry Ignatov, Radu Timofte

llms rag diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 14

HyperNet-Adaptation for Diffusion-Based Test Case Generation

arXiv:2601. 15041v2 Announce Type: replace Abstract: The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios.

By Oliver Wei{\ss}l, Vincenzo Riccio, Severin Kacianka, Andrea Stocco

diffusion robotics fine-tuning safety

arXiv AI

Jul 14

RepTran: Search-Based Repair of Transformer Models

arXiv:2607. 11193v1 Announce Type: cross Abstract: To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired.

By Yuta Ishimoto, Paolo Arcaini, Fuyuki Ishikawa, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei

llms benchmarks

arXiv AI

Jul 14

DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.

By Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen

llms agents reinforcement-learning multimodal benchmarks safety

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