Search: “research”

ASAP: Attention Sink Anchored Pruning

arXiv:2605.22372v1 Announce Type: new Abstract: Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token r

Asymmetric Virtual Memory Paging for Hybrid Mamba-Transformer Inference

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arXiv:2605.22416v1 Announce Type: new Abstract: Hybrid language models like Jamba mix attention layers with State Space Models (SSMs), creating two memory cache types with opposite profiles: Key-Value

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23 May 2026

Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

arXiv:2510.16590v2 Announce Type: replace Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised meth

Bandit Convex Optimization with Gradient Prediction Adaptivity

arXiv:2605.22191v1 Announce Type: new Abstract: Bandit convex optimization (BCO) is a fundamental online learning framework with partial feedback, where the learner observes only the loss incurred at

Bringing Stability to Diffusion: Decomposing and Reducing Variance of Training Masked Diffusion Models

arXiv:2511.18159v2 Announce Type: replace Abstract: Masked diffusion models (MDMs) are a promising alternative to autoregressive models (ARMs), but they suffer from inherently much higher training var

Calibration, Uncertainty Communication, and Deployment Readiness in CKD Risk Prediction: A Framework Evaluation Study

arXiv:2605.21566v1 Announce Type: new Abstract: Machine learning models for chronic kidney disease (CKD) risk prediction often post strong discrimination scores on internal test sets. Calibration and

Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes

arXiv:2605.22075v1 Announce Type: new Abstract: Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework t

CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification

arXiv:2605.22043v1 Announce Type: new Abstract: Multivariate time series (MTS) classification is foundational to pervasive computing and financial analysis, yet existing multi-scale paradigms are ofte

CausalGuard: Conformal Inference under Graph Uncertainty

arXiv:2605.21928v1 Announce Type: new Abstract: Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

arXiv:2603.21743v4 Announce Type: replace Abstract: Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug disc

Cluster-Adaptive Feature Extraction and its Theoretical Foundation with Minkowski Weighted k-Means

arXiv:2603.25958v2 Announce Type: replace Abstract: The Minkowski weighted k-means (mwk-means) algorithm extends classical k-means by incorporating feature weights and a Minkowski distance. We first s

CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization

arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within ri

CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation

arXiv:2605.22774v1 Announce Type: new Abstract: Real-time cognitive load assessment is essential for adaptive human-computer interaction but remains challenging due to limited labeled data and poor cr

Community-Aware Vertex Ordering for Reference-Based Graph Compression: A Cross-Encoder Empirical Study

arXiv:2605.21510v1 Announce Type: cross Abstract: Reference-based graph compression encodes each vertex's neighbor list relative to a recent vertex, exploiting locality to compress large directed grap

Compact Lifted Relaxations for Low-Rank Optimization

arXiv:2603.20228v2 Announce Type: replace-cross Abstract: We develop tractable convex relaxations for rank-constrained quadratic optimization problems over n imes m matrices, a setting for which tract

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

arXiv:2605.21499v1 Announce Type: cross Abstract: Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dy

Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification

arXiv:2605.21742v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) have achieved exceptional performance on tabular classification tasks. However, like other classifiers, their performa

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Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning

arXiv:2605.22379v1 Announce Type: cross Abstract: With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based

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Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization

arXiv:2605.22749v1 Announce Type: new Abstract: Modern smart grids rely on dense measurement infrastructures, communication links, and intelligent field devices. Although this improves supervision and

Decision-Aware Quadratic ReLU Replacement for HE-Friendly Inference

arXiv:2605.22237v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) supports only additions and multiplications, so FHE-only neural-network inference typically replaces ReLU with poly

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary

arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re

Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations

arXiv:2605.22242v1 Announce Type: new Abstract: Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the under

Departure from Regularity: Degree Heterogeneity and Eigengap as the Structural Drivers of ASE-LSE Latent Subspace Disagreement

arXiv:2605.22346v1 Announce Type: cross Abstract: Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different r

Dynamic Mixture of Latent Memories for Self-Evolving Agents

arXiv:2605.21951v1 Announce Type: new Abstract: Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting pr

Efficient Higher-order Subgraph Attribution via Message Passing

arXiv:2605.22385v1 Announce Type: new Abstract: Explaining graph neural networks (GNNs) has become more and more important recently. Higher-order interpretation schemes, such as GNN-LRP (layer-wise re

End-to-End Semantic ID Generation for Generative Advertisement Recommendation

arXiv:2602.10445v3 Announce Type: replace-cross Abstract: Generative Recommendation (GR) has excelled by framing recommendation as next-token prediction. This paradigm relies on Semantic IDs (SIDs) to

Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model

arXiv:2605.21568v1 Announce Type: new Abstract: In this work, we extend the Equilibrium Propagation framework to skew-gradient systems and show an equivalence between deep Energy-Based Models and Hami

Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery

arXiv:2605.22613v1 Announce Type: new Abstract: Recent LLM-guided evolutionary search methods have shown that iterative program mutation can discover strong algorithms, but they typically optimize eac

Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs

arXiv:2605.21994v1 Announce Type: new Abstract: GraphRAG conditions language models on subgraphs retrieved from knowledge graphs, encoded via message-passing GNNs. Because these encoders entangle node

Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

arXiv:2605.22243v1 Announce Type: new Abstract: Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to desi

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

arXiv:2605.22795v1 Announce Type: cross Abstract: We propose and analyze a conservative drifting method for one-step generative modeling. The method replaces the original displacement-based drifting v

Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants

arXiv:2511.02043v4 Announce Type: replace Abstract: Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example,

From Betting to Empirical Bernstein LIL

arXiv:2605.22124v1 Announce Type: cross Abstract: This is a verbatim copy of a technical report I wrote in 2017-2018 to obtain the law of the iterated logarithm using the guarantee on the wealth of an

Graph neural network explanations reveal a topological signature of disease-associated hubs in biological networks

arXiv:2605.21502v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering mea

Holographic functions and neural networks

arXiv:2605.22666v1 Announce Type: cross Abstract: A fuzzy Boolean function is a map f:ube^no [0,1], where ninmathbb N. We introduce and compare three ways of saying that such a function has bounded co

How Many Different Outputs Can a Transformer Generate?

arXiv:2605.22223v1 Announce Type: new Abstract: We study how we can leverage only a handful of characteristics of a transformer's architecture to closely predict the number of different sequences it c

How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability

arXiv:2605.21972v1 Announce Type: new Abstract: Unstructured magnitude pruning at high sparsity can reduce neural network accuracy to near-random performance, while labeled retraining may be unavailab

Implicit Regularization of Mini-Batch Training in Graph Neural Networks

arXiv:2605.22480v1 Announce Type: new Abstract: Mini-batch training of Graph Neural Networks (GNNs) is fundamentally different from training on i.i.d. data: sampling a subgraph alters the topology and

Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms

arXiv:2605.22604v1 Announce Type: cross Abstract: The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security

Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks

arXiv:2602.22719v2 Announce Type: replace Abstract: State-space models (SSMs) have emerged as an efficient strategy for building powerful language models, avoiding the quadratic complexity of computin

Large-scale Score-based Variational Posterior Inference for Bayesian Deep Neural Networks

arXiv:2602.05873v2 Announce Type: replace Abstract: Bayesian (deep) neural networks (BNN) are often more attractive than the vanilla point-estimate deep learning in various aspects including uncertain

Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms

arXiv:2601.05157v2 Announce Type: replace-cross Abstract: In this work, we give a {rm poly}(d,k) time and sample algorithm for efficiently learning the parameters of a mixture of k spherical distribut

LEMUR: Learned Multi-Vector Retrieval

arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vecto

Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition

arXiv:2605.21565v1 Announce Type: new Abstract: Multimodal Emotion Recognition in Conversations (MERC) is a crucial task for understanding human interactions, where multimodal approaches integrating l

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts

arXiv:2506.21035v5 Announce Type: replace Abstract: Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based M

Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans

arXiv:2605.21762v1 Announce Type: new Abstract: Non-contrast computed tomography calcium scoring (CTCS) is a cost-effective imaging modality widely used to detect coronary artery calcifications. This

MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation

arXiv:2605.21783v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to predictio

Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis

arXiv:2605.20747v1 Announce Type: cross Abstract: Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D).