Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2512.07661v4 Announce Type: replace Abstract: Realistic and diverse multi-agent driving scenes are crucial for evaluating autonomous vehicles, but safety-critical events which are essential for this task…
arXiv:2608.11425v1 Announce Type: new Abstract: Underwater image restoration consists of recovering an image which looks like there is no water present. To date, evaluation has not…
arXiv:2411.15122v2 Announce Type: replace Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for…
arXiv:2608.11287v1 Announce Type: new Abstract: Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While…
arXiv:2608.11292v1 Announce Type: new Abstract: Multimodal models increasingly reach for tools when solving visual tasks (crop, zoom, rotate, brighten), a paradigm known as thinking-with-images. The central…
arXiv:2608.11317v1 Announce Type: new Abstract: High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE). This technique is considered…
arXiv:2608.11367v1 Announce Type: new Abstract: Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that…
arXiv:2608.11335v1 Announce Type: new Abstract: Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that governs…
arXiv:2603.11211v3 Announce Type: replace Abstract: Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language…
arXiv:2608.11422v1 Announce Type: new Abstract: Explainability is essential for deploying deep learning models in high-stakes medical applications. Existing explainability methods for volumetric imaging predominantly operate in…
arXiv:2607.14616v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound…
arXiv:2608.11280v1 Announce Type: cross Abstract: Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability…
arXiv:2608.12299v1 Announce Type: new Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence. Its purpose is…
arXiv:2608.11815v1 Announce Type: cross Abstract: Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generation, transferability is fundamentally governed…
arXiv:2608.11534v1 Announce Type: cross Abstract: In medical imaging, the clinical value of Computed Tomography (CT) lies not only in depicting current disease status, but crucially in…
arXiv:2608.11460v1 Announce Type: new Abstract: Large Language Model-powered agents are increasingly used in the workplace via human-artificial intelligence (AI) collaboration. In this new era of work,…
arXiv:2603.20895v3 Announce Type: replace Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.…
arXiv:2608.11441v1 Announce Type: new Abstract: Low-resource automatic speech recognition (ASR) commonly relies on cross-lingual transfer, where models are adapted from higher-resource donor languages. However, selecting donors…
arXiv:2608.12313v1 Announce Type: cross Abstract: Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A…
arXiv:2608.11433v1 Announce Type: new Abstract: Online communities increasingly provide spaces where survivors of sexual violence can share their experiences and seek support. Although prior research has…
arXiv:2608.11994v1 Announce Type: cross Abstract: We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework…
arXiv:2608.11426v1 Announce Type: new Abstract: The lack of diversity in LM content is widely attributed to the alignment process, but how and where exactly in the…
arXiv:2608.11755v1 Announce Type: cross Abstract: Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards increasingly important for…
arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge…