arXiv:2604.01264v1 Announce Type: cross Abstract: Medical imaging techniques, especially Magnetic Resonance Imaging (MRI), are accepted as the gold standard in the diagnosis and treatment planning of neurological diseases. However, the manual analysis of MRI images is a time-consuming process for radiologists and is prone to human error due to fatigue. In this study, two different […]
Omni123: Exploring 3D Native Foundation Models with Limited 3D Data by Unifying Text to 2D and 3D Generation
arXiv:2604.02289v1 Announce Type: cross Abstract: Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D imagery, high-quality 3D assets are scarce, making 3D synthesis under-constrained. Existing methods often rely on indirect […]
Sven: Singular Value Descent as a Computationally Efficient Natural Gradient Method
arXiv:2604.01279v1 Announce Type: cross Abstract: We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss functions into a sum over individual data points, rather than reducing the full loss to a single scalar before computing a parameter update. Sven treats each data point’s residual as […]
Do Large Language Models Mentalize When They Teach?
arXiv:2604.01594v1 Announce Type: new Abstract: How do LLMs decide what to teach next: by reasoning about a learner’s knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study human teaching strategies. On each trial, a teacher LLM sees a hypothetical learner’s trajectory through a reward-annotated directed […]
Preference learning in shades of gray: Interpretable and bias-aware reward modeling for human preferences
arXiv:2604.01312v1 Announce Type: cross Abstract: Learning human preferences in language models remains fundamentally challenging, as reward modeling relies on subtle, subjective comparisons or shades of gray rather than clear-cut labels. This study investigates the limits of current approaches and proposes a feature-augmented framework to better capture the multidimensional nature of human judgment. Using the Anthropic […]
Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function
arXiv:2412.04172v3 Announce Type: replace Abstract: Neurons rely on two interdependent mechanisms, homeostasis and neuromodulation, to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms in conductance-based models often produces unreliable outcomes, particularly when […]
Regularizing Attention Scores with Bootstrapping
arXiv:2604.01339v1 Announce Type: cross Abstract: Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision-making process. However, attention scores are almost always non-zero, resulting in noisy and diffused attention maps and limiting interpretability. Can we quantify uncertainty measures of attention scores […]
FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification
arXiv:2511.10841v2 Announce Type: replace-cross Abstract: Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled framework for such tasks, yet their performance is highly sensitive to the choice of control path constructed from discrete observations. Existing methods commonly employ fixed interpolation schemes, which impose simplistic […]
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
arXiv:2603.27529v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited message-passing pathways. This bottleneck limits their ability to capture essential global context and decreases their performance, particularly in dense and heterophilic regions […]
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
arXiv:2604.02198v1 Announce Type: new Abstract: While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence to rigorous certification standards. Current EASA guidelines mandate demonstrating complete coverage of the AI/ML constituent’s Operational Design Domain (ODD) — a requirement that demands proof that no critical gaps […]
Phase estimation with autoregressive padding (PEAP): addressing inaccuracies and biases in EEG analysis
arXiv:2604.02212v1 Announce Type: new Abstract: Accurate phase estimation at the edge of data segments is crucial for EEG applications such as EEG-TMS in offline and real-time data analysis. Our research evaluates the phase estimation performance of four commonly used methods (Phastimate, SSPE, ETP, and PhastPadding) for accuracy and systemic biases, using data from young and […]
Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing
arXiv:2401.15855v1 Announce Type: cross Abstract: Remote sensing images present unique challenges to image analysis due to the extensive geographic coverage, hardware limitations, and misaligned multi-scale images. This paper revisits the classical multi-scale representation learning problem but under the general framework of self-supervised learning for remote sensing image understanding. We present Cross-Scale MAE, a self-supervised model […]