Search: “research”

Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models

arXiv:2606.09861v1 Announce Type: cross Abstract: While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the ga

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Time series forecasting from partial observations via Non-negative Matrix Factorization

DGX agent

arXiv:2102.05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values. In this paper, we introduce the

researcharxiv-cs-lg

10 Jun 2026

Towards Critical Branching Mechanism in Recurrent Neural Networks

arXiv:2606.10384v1 Announce Type: cross Abstract: Criticality has been proposed as a key organizing principle in biological neural systems, yet its origin and relevance in artificial neural networks r

Towards Diverse Scientific Hypothesis Search with Large Language Models

arXiv:2606.10587v1 Announce Type: cross Abstract: Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientifi

Towards Robust Arabic Speech Emotion Recognition with Deep Learning

arXiv:2606.10278v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals. While recent advances in deep learning have signific

Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization

arXiv:2606.10179v1 Announce Type: cross Abstract: Quantum Circuit Born Machines (QCBMs) offer a natural approach to generative machine learning by leveraging the Born rule. Recent work has provided a

Training Set Augmentation and Biology-Aware Harmonization Improve Radiomic Models for Lung Cancer Prediction in Indeterminate Nodules

arXiv:2412.16758v3 Announce Type: replace-cross Abstract: CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509.25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination a

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

arXiv:2509.25017v2 Announce Type: replace-cross Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wi

Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows

arXiv:2606.09857v1 Announce Type: new Abstract: Reduced-order models (ROMs) provide an efficient surrogate for complex multiscale systems, but their predictive accuracy is often compromised by truncat

UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data

arXiv:2606.10683v1 Announce Type: cross Abstract: Dexterous hands are essential for fine-grained manipulation, but their hardware designs vary substantially across embodiments. Differences in kinemati

UniSVQ: 2-bit Unified Scalar-Vector Quantization

arXiv:2606.10520v1 Announce Type: new Abstract: Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration for large language models (LLMs). Scalar quantizati

Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices

arXiv:2606.10547v1 Announce Type: cross Abstract: Energy Dispersive X-ray (EDX) tomography in Scanning Transmission Electron Microscopy (STEM) enables 3D compositional and elemental mapping at the nan

Upper Bounds for Local Learning Coefficients of Three-Layer Neural Networks

arXiv:2603.12785v2 Announce Type: replace Abstract: Three-layer neural networks are known to form singular learning models, and their Bayesian asymptotic behavior is governed by the learning coefficie

VFUSE: Virulent Feature Understanding with Sparse autoEncoders

arXiv:2606.10080v1 Announce Type: cross Abstract: Generative models have shown remarkable progress in a variety of domains such as protein design, but such power enables the opaque generation of hazar

What Do Deepfake Speech Detectors Actually Hear?

arXiv:2606.10912v1 Announce Type: cross Abstract: Deepfake speech detectors often output a single score without explaining why an audio sample is flagged, where in the signal the evidence lies, or wha

What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects

arXiv:2501.14717v2 Announce Type: replace Abstract: Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly t

When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking

arXiv:2606.10287v1 Announce Type: cross Abstract: Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR,

Where You Inject Diversity Matters: A Unified Framework for Diverse Generation

arXiv:2606.10302v1 Announce Type: new Abstract: Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing

Which LoRA? An Empirical Study on the Effectiveness of LoRA Techniques During Multilingual Instruction Tuning

arXiv:2606.10428v1 Announce Type: new Abstract: We investigate whether commonly available LoRA variants have an advantage over basic LoRA in multilingual instruction tuning. Experiments involving LoRA

Whisfusion: Parallel ASR Decoding with Masked Diffusion

arXiv:2508.07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency sca

Whisper-GPT -- Continuous Discrete Hybrid Representation Language Models For Speech And Music

arXiv:2412.11449v2 Announce Type: replace-cross Abstract: We propose WHISPER-GPT: A generative large language model (LLM) for speech and music that allows us to work with continuous audio representati

Who Wrote the Book? Detecting and Attributing LLM Ghostwriters

arXiv:2603.28054v2 Announce Type: replace Abstract: In this paper, we introduce GhostWriteBench, a dataset for LLM authorship attribution. It comprises long-form texts (50K+ words per book) generated

WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds

arXiv:2606.10934v1 Announce Type: new Abstract: A common assumption holds that enough observational and interventional data, given to a strong enough predictor, suffices. We report a failure mode that

WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling

arXiv:2512.14614v2 Announce Type: replace Abstract: This paper presents WorldPlay, a streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consist

ZODS-RS -- Zero-training Oriented Detection & Segmentation for Remote Sensing

arXiv:2606.10769v1 Announce Type: new Abstract: Remote-sensing and UAV applications need models that generalize across platforms and viewpoints without task-specific training. Yet training-free pipeli

3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning

arXiv:2606.07907v1 Announce Type: cross Abstract: In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed. The model directly predicts explicit 3D point clou

A Geometric Measure of Linear Separability for Neural Representations

arXiv:2606.08721v1 Announce Type: new Abstract: Modern neural classifiers commonly rely on linear readouts, yet predictive metrics alone do not characterize the class-wise geometry of the representati

A Geometric Theory of Cognition for Machine Intelligence

arXiv:2512.12225v3 Announce Type: replace Abstract: Developing artificial agents that unify representation, memory, adaptation, and prediction remains a fundamental challenge in artificial intelligenc

A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation

arXiv:2602.08785v2 Announce Type: replace Abstract: Generalization and approximation capabilities of message passing graph neural networks (MPNNs) are often studied by defining a compact metric on a s

A Hierarchical Feature Engineering Framework for Automated Classification of Phonotraumatic and Non-Phonotraumatic Vocal Hyperfunction

arXiv:2606.07673v1 Announce Type: cross Abstract: Ambulatory neck-surface acceleration enables non-invasive monitoring of vocal hyperfunction, yet robust biomarkers for its subtypes remain limited. Th

A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

arXiv:2601.02424v2 Announce Type: replace-cross Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and ph

A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media

arXiv:2603.11250v2 Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction

A Regret Minimization Framework on Preference Learning in Large Language Models

arXiv:2606.09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provi

A Robust widetilde{O}(1/sqrt{T}) Rate for Unprojected TD Learning with Linear Function Approximation

arXiv:2506.01052v3 Announce Type: replace Abstract: We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone of rein

A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning

arXiv:2606.08583v1 Announce Type: new Abstract: Deep learning on physiological time series is interpreted through domain-specific features -- oscillatory rhythms in EEG, morphological complexes in ECG

A Theoretical Analysis of Memory and Overfitting Phenomena in Stochastic Interpolation Models

arXiv:2606.08554v1 Announce Type: new Abstract: This paper provides a theoretical account of memorization in stochastic interpolation models. By leveraging closed-form expressions for the optimal velo

A Unifying View of Attention Sinks: Two Algorithms, Two Solutions

arXiv:2606.08105v1 Announce Type: new Abstract: When attention concentrates on a single token, a sink, what is the model actually computing? Attention sinks are ubiquitous in softmax transformers, yet

A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis

arXiv:2606.08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users. Formal Concept

AccioScene: Compositional 3D Scene Generation via Graph Diffusion and Interaction-driven Critics

arXiv:2502.06819v2 Announce Type: replace Abstract: This paper presents a framework for generating 3D indoor scenes from text prompts. Existing methods often formulate scene synthesis as an object lay

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

arXiv:2412.06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) h

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives

arXiv:2601.01665v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Neverthe

Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints

arXiv:2606.07622v1 Announce Type: new Abstract: Accurate passenger queue forecasting in airport terminals is essential for efficient departure operations, as it enables proactive congestion management

Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret

arXiv:2606.09668v1 Announce Type: new Abstract: Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochas

All-in-One Augmented Reality Guided Head and Neck Tumor Resection

arXiv:2603.29495v2 Announce Type: replace Abstract: Positive margins are common in head and neck squamous cell carcinoma, yet intraoperative re-resection is often imprecise because margin locations ar

Always back to the basics: LatentMoE was probably inspired by MLA, which was inspired by LoRA, which was inspired by SVD, which was inspired…

researchsebastian-raschka--x

This post traces the historical lineage of machine learning techniques, suggesting that LatentMoE builds upon a chain of progressively developed methods including MLA, LoRA, and SVD, emphasizing how m

An Effective Router for Vision-Language Model Selection

arXiv:2606.08970v1 Announce Type: new Abstract: Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most a

An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification

arXiv:2606.09123v1 Announce Type: cross Abstract: Multispectral point cloud (MPC) is composed of 3D spatial-spectral information, which holds tremendous potential for accurate land-cover classificatio