DeepGOF-1 introduces a pretrained convolutional network as a goodness‑of‑fit test for logistic regression, where the network reads a grid of standardized residuals as an image and outputs a test statistic. The test is fully calibrated via the analyst’s own bootstrap, ensuring the nominal level is maintained regardless of the network’s training. The authors prove exactness under pivotality, asymptotic exactness without it, and provide a computable consistency certificate from the frozen weights, demonstrating superior stability and power across multiple benchmarks and sample sizes.
By Ebrahim Khaled Ebrahim
The paper investigates whether the choice of loss function or network architecture is more critical for survival analysis with competing risks. By independently varying three loss functions (two strictly proper) and four backbone architectures across 20 tabular and 3 pathology‑image datasets, the authors find that modelling cumulative incidence directly with censoring weighting consistently outperforms other losses, while the backbone must match the data modality. Proper scoring rules reveal that the loss dominates performance on competing‑risk data, whereas the architecture matters more on single‑event data, and the two factors compound rather than substitute.
By Julie Alberge, Ga\"el Varoquaux, Judith Ab\'ecassis
Tabular foundation models are pretrained networks that predict risk from a table without fitting a model. Whether their risks are correct (calibrated) has been judged on real data, where true risk is...
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
By Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
The paper introduces EDGE, a grouped calibration test for logistic regression that remains robust when a few records are corrupted. EDGE projects grouped residuals onto a small basis of calibration shapes, keeping degrees of freedom independent of group count, which allows fine‑grained power or coarse protection against outliers. In simulations and external validation, EDGE matched or exceeded the power of existing tests while maintaining false‑alarm rates below 10% even with corrupted data.
By Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
The study benchmarks seven deep tabular generative models against trivial baselines across a size ladder of 8 public datasets (200–20,000 rows) and 4 small clinical datasets, totaling 2,220 runs. Using AUROC of classifiers trained on synthetic data and tested on real data, it finds that in 23 of 24 model–dataset pairs no deep model surpasses the best trivial baseline beyond seed noise, and the best baseline wins 40 of 49 evaluated cells. Rankings of deep models are relatively stable below 5,000 rows, but the top two methods often differ by less than seed variation, and rankings on small clinical datasets only moderately align with those on subsampled large datasets.
By Shivam Shrivastava
The study evaluates the reliability of deep‑ensemble uncertainty for brain tumour segmentation on the BraTS‑GoAT dataset. A 5‑fold cross‑validated nnU‑Net baseline and a 3‑seed deep ensemble were compared for calibration and error detection; the ensemble showed modest gains in calibration on in‑distribution data but the single model’s confidence remained flat while accuracy degraded under synthetic corruptions. Disagreement among ensemble members rose sharply with corruption severity, proving to be a more sensitive indicator of acquisition shift than single‑model confidence.
By Riya Deepak Shet, Chenxi Liang, Le Zhang
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
By Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
The study audited ten different classifiers—including linear, tree‑ensemble, neural, glass‑box, and tabular foundation models—on national health survey data to predict myocardial infarction. By systematically removing features that could cause target leakage, the authors found that all models’ AUROC scores collapsed into a narrow band, indicating that reported high accuracy in prior work was largely due to leakage rather than model sophistication. The glass‑box explainable boosting machine performed comparably to other models while being much faster, and the authors demonstrated that fairness, calibration, and uncertainty can be audited and repaired without sacrificing performance.
By Raad Bin Tareaf, Murad Al-Rajab, Samia Loucif, Samer Ellaham, Cedric Schmitz
arXiv:2607. 28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class.
By Mushir Akhtar, M. Tanveer
arXiv:2607. 12501v3 Announce Type: replace Abstract: The Forward-Forward algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and low on contrastive ones.
By Paolo Giannitrapani