Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
arXiv:2603.02235v1 Announce Type: new Abstract: Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation significantly hinders their adoption and practical applicability across diverse application domains...
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
arXiv:2603.02265v1 Announce Type: new Abstract: In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent...
Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network
arXiv:2603.02273v1 Announce Type: new Abstract: Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We...
Learning Nested Named Entity Recognition from Flat Annotations
arXiv:2603.00840v1 Announce Type: new Abstract: Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat...
A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification
arXiv:2603.00067v1 Announce Type: new Abstract: Medical time-series data are characterized by irregular sampling, high noise levels, missing values, and strong inter-feature dependencies. Recurrent neural networks (RNNs), particularly gated architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU),...
SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search
arXiv:2603.00099v1 Announce Type: new Abstract: Neural architecture search (NAS) automates the discovery of neural networks that meet specified criteria, yet its evaluation procedures are often hardcoded, limiting the ability to introduce new metrics. This issue is especially pronounced in hardware-aware...
Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare
arXiv:2603.00192v1 Announce Type: new Abstract: In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a...
Scalable Gaussian process modeling of parametrized spatio-temporal fields
arXiv:2603.00290v1 Announce Type: new Abstract: We introduce a scalable Gaussian process (GP) framework with deep product kernels for data-driven learning of parametrized spatio-temporal fields over fixed or parameter-dependent domains. The proposed framework learns a continuous representation, enabling predictions at arbitrary...
Episode 41: Reading Recommendations - EJIL: The Podcast!
France or Spain or Germany or France: A Neural Account of Non-Redundant Redundant Disjunctions
arXiv:2602.23547v1 Announce Type: new Abstract: Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or...
Terminology Rarity Predicts Catastrophic Failure in LLM Translation of Low-Resource Ancient Languages: Evidence from Ancient Greek
arXiv:2602.24119v1 Announce Type: new Abstract: This study presents the first systematic, reference-free human evaluation of large language model (LLM) machine translation (MT) for Ancient Greek (AG) technical prose. We evaluate translations by three commercial LLMs (Claude, Gemini, ChatGPT) of twenty...
NAU-QMUL: Utilizing BERT and CLIP for Multi-modal AI-Generated Image Detection
arXiv:2602.23863v1 Announce Type: cross Abstract: With the aim of detecting AI-generated images and identifying the specific models responsible for their generation, we propose a multi-modal multi-task model. The model leverages pre-trained BERT and CLIP Vision encoders for text and image...
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
arXiv:2602.23400v1 Announce Type: new Abstract: Generative Recommendation (GenRec) typically leverages Large Language Models (LLMs) to redefine personalization as an instruction-driven sequence generation task. However, fine-tuning on user logs inadvertently encodes sensitive attributes into model parameters, raising critical privacy concerns. Existing...
Sample Size Calculations for Developing Clinical Prediction Models: Overview and pmsims R package
arXiv:2602.23507v1 Announce Type: new Abstract: Background: Clinical prediction models are increasingly used to inform healthcare decisions, but determining the minimum sample size for their development remains a critical and unresolved challenge. Inadequate sample sizes can lead to overfitting, poor generalisability,...
Neural Operators Can Discover Functional Clusters
arXiv:2602.23528v1 Announce Type: new Abstract: Operator learning is reshaping scientific computing by amortizing inference across infinite families of problems. While neural operators (NOs) are increasingly well understood for regression, far less is known for classification and its unsupervised analogue: clustering....
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
arXiv:2602.23556v1 Announce Type: new Abstract: Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed, training requires frequent irregular communication that stalls forward progress. Moreover, fetched data...
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
arXiv:2602.23630v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of...
Hierarchical Concept-based Interpretable Models
arXiv:2602.23947v1 Announce Type: new Abstract: Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embedding Models (CEMs) address this by mapping inputs to human-interpretable concept representations...
Intrinsic Lorentz Neural Network
arXiv:2602.23981v1 Announce Type: new Abstract: Real-world data frequently exhibit latent hierarchical structures, which can be naturally represented by hyperbolic geometry. Although recent hyperbolic neural networks have demonstrated promising results, many existing architectures remain partially intrinsic, mixing Euclidean operations with hyperbolic...
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
arXiv:2602.23994v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and dementia. Neuroimaging modalities, such as structural MRI, provide biomarkers of this transition; however, their high...
LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
arXiv:2602.21044v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world reasoning problems admit multiple valid derivations, requiring models to explore diverse...
Group Orthogonalized Policy Optimization:Group Policy Optimization as Orthogonal Projection in Hilbert Space
arXiv:2602.21269v1 Announce Type: cross Abstract: We present Group Orthogonalized Policy Optimization (GOPO), a new alignment algorithm for large language models derived from the geometry of Hilbert function spaces. Instead of optimizing on the probability simplex and inheriting the exponential curvature...
Reliable XAI Explanations in Sudden Cardiac Death Prediction for Chagas Cardiomyopathy
arXiv:2602.22288v1 Announce Type: new Abstract: Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients not classified as high risk. While AI and machine learning models improve risk stratification, their...
Global River Forecasting with a Topology-Informed AI Foundation Model
arXiv:2602.22293v1 Announce Type: new Abstract: River systems operate as inherently interconnected continuous networks, meaning river hydrodynamic simulation ought to be a systemic process. However, widespread hydrology data scarcity often restricts data-driven forecasting to isolated predictions. To achieve systemic simulation and...
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
arXiv:2602.22298v1 Announce Type: new Abstract: Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introduce AviaSafe, a hierarchical, physics-informed neural forecaster that produces global, six-hourly predictions of these...
Learning geometry-dependent lead-field operators for forward ECG modeling
arXiv:2602.22367v1 Announce Type: new Abstract: Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast ECG simulations while preserving full geometric fidelity. Achieving high anatomical accuracy in torso representation is,...
SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks
arXiv:2602.21307v1 Announce Type: new Abstract: Symbolic distillation replaces neural networks, or components thereof, with interpretable, closed-form mathematical expressions. This approach has shown promise in discovering physical laws and mathematical relationships directly from trained deep learning models, yet adoption remains limited...
Interleaved Head Attention
arXiv:2602.21371v1 Announce Type: new Abstract: Multi-Head Attention (MHA) is the core computational primitive underlying modern Large Language Models (LLMs). However, MHA suffers from a fundamental linear scaling limitation: $H$ attention heads produce exactly $H$ independent attention matrices, with no communication...
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
arXiv:2602.21442v1 Announce Type: new Abstract: The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman-Ford, a phenomenon known as algorithmic alignment. At the same time, recent advances in...
When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training
arXiv:2602.21454v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR) filtering operation governed by both feedforward weights and recurrent poles. While, in principle, all parameters...