arXiv:2606. 24903v2 Announce Type: replace Abstract: Few-shot label acquisition lacks a label-free signal for when additional labels cease to improve accuracy: existing stopping criteria either require a held-out validation set (violating the few-shot premise) or rely on theoretically ungrounded heuristics, so we introduce the spectral saturation index $S(K)=\mathrm{erank}(\hat{\Sigma}_W^{(K)})/K$, the exponential spectral entropy of the pooled within-class covariance normalized by per-class support size $K$, which measures the exploration rate per label and falls below a fixed threshold $\tau=0.
By Arnav Gupta
The paper investigates how many normal samples are required to reliably set an alarm threshold for few‑shot anomaly detectors, focusing on distribution‑free certification limits. Using a frozen DINOv2 PCA residual ranker on 15 MVTec and 12 VisA categories, the authors show that simple leave‑one‑image‑out calibration is limited by resolution and shift, leading to empirical false‑alarm rates far above the nominal level. They derive a category‑count feasibility calculus, demonstrating that at least 14, 29, and 59 independent category draws are needed for 95% upper confidence bounds at α=0.20, 0.10, and 0.05, and propose the CRESS protocol to split source categories into reference, proposal, and certification roles.
whyItMatters":"The study provides concrete numerical thresholds for the amount of source evidence needed to guarantee reliable anomaly detection in new categories, informing practical deployment of few‑shot detectors."
By Gia Huy Thai, Nguyen Thai Anh
arXiv:2606. 07596v1 Announce Type: new Abstract: Fine-tuning often introduces spurious correlations alongside task knowledge, causing systematic failures on underrepresented groups.
By Edward Sun, Dmitrii Troitskii
The paper introduces EDGE, a closed‑form statistical test for assessing the calibration of probabilistic binary classifiers, specifically logistic regression. EDGE uses the same binned predicted‑versus‑observed table as a reliability diagram, projects standardized bin residuals onto a small basis of smooth calibration‑distortion shapes, and yields a null distribution that is a weighted sum of chi‑square variables. The method requires only a single pass over the data and a small eigendecomposition, avoiding refitting, resampling, or tuning, and remains robust in sparse or misspecified settings where other binned tests fail.
By Ebrahim Khaled Ebrahim, Ahmed El-Kotory
arXiv:2609.09417v1 Announce Type: new
Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...
By Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana
The study investigates whether the number of discrete class‑separability jumps (phase transitions) observed during ResNet fine‑tuning can predict final test accuracy. Across 75 experiments on four benchmarks (CIFAR‑10, CIFAR‑100, TinyImageNet, CIFAR‑10‑C) and three ResNet variants, a strong negative correlation is found on standard i.i.d. datasets (r = −0.84 on CIFAR‑10, r = −0.87 on CIFAR‑100), while the correlation weakens under distributional stress. Additional analyses show that the transition count retains predictive power after controlling for architecture depth and outperforms other training‑curve signals on in‑distribution benchmarks, though it is dominated by other signals on stressed datasets.
By Arunan J
OmniMed‑FL is a multimodal federated learning framework that fuses chest radiographs and synthetic patient notes to classify five clinical conditions. The study benchmarks eight fusion strategies, three initializations, and four missing‑text imputation rules across 3–20 hospital clients under non‑IID Dirichlet partitioning, showing that federated approaches (FedAvg, FedProx, SCAFFOLD‑AdamW) outperform local‑only training. Multimodal fusion consistently improves performance, achieving macro‑F1 scores up to 0.956 on the synthetic corpus and 0.906 on the radiograph corpus.
By Ayush Debnath, Ruelia Saha, Sudip Misra
arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.
By Weijia Han, Lisha Qu
The paper investigates how many human annotators are equivalent to a panel of 32 large‑language‑model (LLM) judges. By comparing the panel’s label distributions to empirical human labels on three ChaosNLI tasks, the authors find two distinct effective panel sizes: distribution‑error matching yields effective sizes of 2.304, 3.750, and 3.445, while spectral matching gives 4.242, 6.459, and 6.499, indicating a 1.72–1.89× gap. The study also explores how spectral diversity, participation ratio, and panel composition affect effective size, and demonstrates that carefully chosen panels can outperform baseline accuracy while improving effective size.
By Chao Li, Yingying Yu, Yunfeng Li
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhil...
arXiv:2607. 27763v1 Announce Type: cross Abstract: We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with two tracks: Concept Detection (Task 1), assigning UMLS Concept Unique Identifiers (CUIs) to radiology images, and Caption Prediction (Task 2), generating natural-language captions.
By Bowen Wang, Youwen Zhang, Ritesh Mehta
arXiv:2609.38901v1 Announce Type: new
Abstract: Representer explanations rank the training landmarks that most influence a self-supervised representation. At scale, this ranking rests on up to four s...
By Jayanta Mukherjee, Shourya Verma, Mengbo Wang, Jasorsi Ghosh, Ananth Grama