arXiv Machine Learning
Jul 1
Probing Memorization of Tabular In-Context Learning
arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.
Spiking the training data to correct for test set contamination
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.
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arXiv Machine Learning
Aug 5
Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scores
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 AI
Aug 6
When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets
arXiv:2510. 20351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly exposed to data contamination, i.
arXiv Machine Learning
Jun 9
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
arXiv:2402. 08922v3 Announce Type: replace Abstract: Large-scale black-box models have become ubiquitous across numerous applications.
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.
arXiv AI
Jul 23
Test Case Prioritization for DNNs via Neural Collapse Instability
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.
arXiv Machine Learning
Jul 2
Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs
arXiv:2509. 17314v4 Announce Type: replace-cross Abstract: Software increasingly relies on the emergent capabilities of Large Language Models (LLMs), from natural language understanding to program analysis and generation.
arXiv Machine Learning
Jul 15
Extractable Memorization From First Principles
arXiv:2607. 12649v1 Announce Type: new Abstract: Recent work on extractable memorization in LLMs suffers from two contrasting validity problems.
arXiv Machine Learning
1d ago
MoRFI: Monotonic Sparse Autoencoder Feature Identification
arXiv:2604. 26866v2 Announce Type: replace-cross Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction.
arXiv Machine Learning
Jul 10
Prediction-Powered Active Testing
arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.
Hugging Face Trending Papers
Jul 9
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently breaks in real-world deployment, where data undergoes continual distributional shifts. Continual Test-Time Adaptation (CTTA) addresses this challenge by adapting pretrained models to non-stationary target distributions on-the-fly, without access to source data or labeled targets, while mitigating two critical failure modes: catastrophic forgetting of source knowledge and error accumulation from noisy pseudo-labels over extended time horizons.
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.