An Explainable Ensemble Framework for Alzheimer's Disease Prediction Using Structured Clinical and Cognitive Data
arXiv:2603.04449v1 Announce Type: new Abstract: Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals...
On Emergences of Non-Classical Statistical Characteristics in Classical Neural Networks
arXiv:2603.04451v1 Announce Type: new Abstract: Inspired by measurement incompatibility and Bell-family inequalities in quantum mechanics, we propose the Non-Classical Network (NCnet), a simple classical neural architecture that stably exhibits non-classical statistical behaviors under typical and interpretable experimental setups. We find...
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
arXiv:2603.04458v1 Announce Type: new Abstract: Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their...
MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
arXiv:2603.04461v1 Announce Type: new Abstract: Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weather data. Deep learning models have shown...
Implicit Bias and Loss of Plasticity in Matrix Completion: Depth Promotes Low-Rankness
arXiv:2603.04703v1 Announce Type: new Abstract: We study matrix completion via deep matrix factorization (a.k.a. deep linear neural networks) as a simplified testbed to examine how network depth influences training dynamics. Despite the simplicity and importance of the problem, prior theory...
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
arXiv:2603.04730v1 Announce Type: new Abstract: Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single...
Multilevel Training for Kolmogorov Arnold Networks
arXiv:2603.04827v1 Announce Type: new Abstract: Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions inherent to such networks. In contrast to multilayer perceptrons (MLPs), Kolmogorov-Arnold networks (KANs) provide more...
Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness
arXiv:2603.04881v1 Announce Type: new Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade performance, introduce fairness issues like disparate impact, and reduce adversarial robustness. The theoretical underpinnings of...
TTSR: Test-Time Self-Reflection for Continual Reasoning Improvement
arXiv:2603.03297v1 Announce Type: cross Abstract: Test-time Training enables model adaptation using only test questions and offers a promising paradigm for improving the reasoning ability of large language models (LLMs). However, it faces two major challenges: test questions are often highly...
Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
arXiv:2603.03309v1 Announce Type: new Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models...
Entropic-Time Inference: Self-Organizing Large Language Model Decoding Beyond Attention
arXiv:2603.03310v1 Announce Type: new Abstract: Modern large language model (LLM) inference engines optimize throughput and latency under fixed decoding rules, treating generation as a linear progression in token time. We propose a fundamentally different paradigm: entropic\-time inference, where decoding is...
Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi
arXiv:2603.03508v1 Announce Type: new Abstract: The dominance of large multilingual foundation models has widened linguistic inequalities in Natural Language Processing (NLP), often leaving low-resource languages underrepresented. This paper introduces LilMoo, a 0.6-billion-parameter Hindi language model trained entirely from scratch to...
RADAR: Learning to Route with Asymmetry-aware DistAnce Representations
arXiv:2603.03388v1 Announce Type: new Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in asymmetric distance...
Towards Improved Sentence Representations using Token Graphs
arXiv:2603.03389v1 Announce Type: new Abstract: Obtaining a single-vector representation from a Large Language Model's (LLM) token-level outputs is a critical step for nearly all sentence-level tasks. However, standard pooling methods like mean or max aggregation treat tokens as an independent...
Optimal trajectory-guided stochastic co-optimization for e-fuel system design and real-time operation
arXiv:2603.03484v1 Announce Type: new Abstract: E-fuels are promising long-term energy carriers supporting the net-zero transition. However, the large combinatorial design-operation spaces under renewable uncertainty make the use of mathematical programming impractical for co-optimizing e-fuel production systems. Here, we present MasCOR,...
When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
arXiv:2603.03491v1 Announce Type: new Abstract: Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottleneck. However, their reliance on emerging non-volatile memory devices introduces device-level non-idealities-such as write...
Solving adversarial examples requires solving exponential misalignment
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks - input perturbations imperceptible to humans that fool neural networks - remain both a persistent failure mode in machine learning, and a phenomenon with mysterious origins. To shed light, we define and analyze...
mlx-snn: Spiking Neural Networks on Apple Silicon via MLX
arXiv:2603.03529v1 Announce Type: new Abstract: We introduce mlx-snn, the first spiking neural network (SNN) library built natively on Apple's MLX framework. As SNN research grows rapidly, all major libraries -- snnTorch, Norse, SpikingJelly, Lava -- target PyTorch or custom backends,...
Adaptive Sensing of Continuous Physical Systems for Machine Learning
arXiv:2603.03650v1 Announce Type: new Abstract: Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning not only from data generated by such systems, but also how to measure...
Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
arXiv:2603.03830v1 Announce Type: new Abstract: Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware...
Sensory-Aware Sequential Recommendation via Review-Distilled Representations
arXiv:2603.02709v1 Announce Type: new Abstract: We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product reviews. Our approach, \textsc{ASEGR} (Attribute-based Sensory Enhanced Generative Recommendation), introduces a two-stage pipeline in which...
ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
arXiv:2603.02216v1 Announce Type: new Abstract: Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the uncertainty inherent in...
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603.02221v1 Announce Type: new Abstract: In healthcare tabular predictions, classical models with feature engineering often outperform neural approaches. Recent advances in Large Language Models enable the integration of domain knowledge into feature engineering, offering a promising direction. However, existing approaches...
Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling
arXiv:2603.02226v1 Announce Type: new Abstract: Real-world sequential signals, such as audio or video, contain critical information that is often embedded within long periods of silence or noise. While recurrent neural networks (RNNs) are designed to process such data efficiently, they...
Neural Paging: Learning Context Management Policies for Turing-Complete Agents
arXiv:2603.02228v1 Announce Type: new Abstract: The proof that Large Language Models (LLMs) augmented with external read-write memory constitute a computationally universal system has established the theoretical foundation for general-purpose agents. However, existing implementations face a critical bottleneck: the finite and...
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due...
Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
arXiv:2603.02232v1 Announce Type: new Abstract: Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal preference data. When human annotators provide graded preferences on a Likert scale...
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...