arXiv:2605. 24818v2 Announce Type: replace-cross Abstract: The literature on test set contamination largely focuses on detection, but the correction of contaminated test scores is underexplored.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.
arXiv:2608. 02985v1 Announce Type: new Abstract: The standard check for contamination in LLM backtests is simple: compare scores before and after the training cutoff.
arXiv:2510. 20351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly exposed to data contamination, i.
arXiv:2402. 08922v3 Announce Type: replace Abstract: Large-scale black-box models have become ubiquitous across numerous applications.
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.
arXiv:2607. 20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important.