Search: “research”

Flow-Map Distillation on Relation Manifolds for Image Restoration

arXiv:2608.05769v1 Announce Type: new Abstract: Knowledge distillation for image restoration typically aligns intermediate features or relation matrices between teacher and student networks as static

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Research

From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

DGX agent

arXiv:2608.05203v1 Announce Type: new Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by

researcharxiv-cs-ai

7 Aug 2026

Gender-Based Heterogeneity in Youth Privacy-Protective Behavior for Smart Voice Assistants: Evidence from Multigroup PLS-SEM

arXiv:2603.27117v2 Announce Type: replace-cross Abstract: This paper investigates how gender shapes privacy decision-making in youth smart voice assistant (SVA) ecosystems. Using survey data from 469

Safety

GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

arXiv:2608.05478v1 Announce Type: cross Abstract: Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research

safetyarxiv-cs-cl

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI

arXiv:2608.05258v1 Announce Type: new Abstract: Gradient-weighted Class Activation Mapping (Grad-CAM) is widely used to visualize model decisions, but it was originally formulated for convolutional ne

HERA: Historical Evidence Routing Adapter for Physical Prediction in Latent World Models

arXiv:2608.05523v1 Announce Type: new Abstract: Predictive video models have emerged as promising world models by learning latent visual dynamics from large-scale video. Yet these models remain challe

Hierarchical Flow Matching for 3D Point Cloud Generation

arXiv:2608.05557v1 Announce Type: new Abstract: Generating high-quality 3D point clouds requires capturing both global shape topology and local geometric details. Existing flow-based methods rely on c

Hierarchical Latent Prediction for Language Models

arXiv:2608.05806v1 Announce Type: cross Abstract: While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optima

HOPE: Hand-Object Pressure Estimation from Monocular Videos

arXiv:2608.06192v1 Announce Type: new Abstract: Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estim

How Far Do Simple Transformations Translate Across Text Embedding Models?

arXiv:2608.05980v1 Announce Type: new Abstract: We investigate whether simple transformations can translate representations across heterogeneous text embedding models. Understanding how independently

Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty

arXiv:2608.05454v1 Announce Type: new Abstract: Probabilistic prediction heads in neural networks typically output either a Gaussian mixture or a single conformal region. Neither separates the distinc

HyTBE: Hyperbolic Target-Background Expert Model for Cross-Domain Infrared Small Target Detection

arXiv:2608.05771v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) has achieved substantial progress under domain-consistent evaluation, yet detector performance often degrades

Integrating Human Linguistic Insights into AI: Theory-Driven Representation for Multilingual Text-to-Speech

arXiv:2204.07228v2 Announce Type: replace Abstract: This paper explores the integration of human linguistic insights into multilingual text-to-speech (TTS) systems by evaluating the Featurally Undersp

Invisible Shortcuts: Why Vision Encoders Know Your Camera

arXiv:2608.05424v1 Announce Type: new Abstract: Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

arXiv:2608.06122v1 Announce Type: cross Abstract: Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether simi

KVAE: Family of Tokenizers for Multimodal Generative Models

arXiv:2608.05798v1 Announce Type: new Abstract: Latent diffusion modeling (LDM), a prominent paradigm, utilizes tokenizers to map input signal to compressed representation. This dependency positions t

LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones

arXiv:2608.05610v1 Announce Type: cross Abstract: QAOA training repeatedly queries an objective and all shared gradients, making exact evaluation a feasibility bottleneck even when QUBO terms have bou

Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

arXiv:2608.06060v1 Announce Type: new Abstract: Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-

LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation

arXiv:2601.05192v2 Announce Type: replace Abstract: Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construc

LILAC: An Idempotent Neural Speech Codec

arXiv:2608.05727v1 Announce Type: cross Abstract: Neural Audio Codecs are widely adopted in speech generation and editing. However, existing neural audio codecs are not idempotent: across the paper's

Mapping Armenian Paris: Extracting and Geocoding Commercial Advertisements from the 20th-Century Diaspora Press

arXiv:2608.05911v1 Announce Type: new Abstract: This paper presents an end-to-end, IIIF-based pipeline that turns the digitised Armenian press of France into an interactive map of the 20th-century Par

ML-for-ML

arXiv:2608.06046v1 Announce Type: cross Abstract: AI training workloads are growing rapidly, making their time, energy, and infrastructure costs increasingly important. In shared cloud clusters, train

MOSAIK: Multi-Patch Content-Aware Spatial Allocation of Image Tokens for Efficient Generation

arXiv:2608.05450v1 Announce Type: new Abstract: Pixel-space diffusion models avoid the reconstruction ceiling of latent diffusion models by generating directly in image space. However, their substanti

Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality

arXiv:2608.05979v1 Announce Type: new Abstract: Machine learning, and deep networks in particular, are increasingly used to derive higher-level Earth observation (EO) products such as annual land-cove

Multivariate Time Series Forecasting needs Cross Variable Loss

arXiv:2608.05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing

Muon on the Stiefel Manifold Admits an Exact Closed-Form Update

arXiv:2608.06218v1 Announce Type: cross Abstract: We study Muon, a recently proposed matrix-aware optimization method, in the context of the Stiefel manifold. This manifold consists of matrices with o

Near-sensor Computing for Rapid Visuotactile Perception

arXiv:2608.05725v1 Announce Type: new Abstract: Visuotactile sensors reconstruct dense contact geometry from measured surface gradients, but host-based processing increases power consumption and intro

researcharxiv-cs-ro

On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities

arXiv:2608.06182v1 Announce Type: cross Abstract: We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set. Although extragradien

On the Anisotropy of Score-Based Generative Models

arXiv:2510.22899v2 Announce Type: replace Abstract: We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce t

Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration

arXiv:2608.05741v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational mi

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization

arXiv:2608.05240v1 Announce Type: cross Abstract: One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matri

One Ranking, Any Budget: Matryoshka Evidence-to-Context Frame Selection for Long-Video Understanding

arXiv:2608.05707v1 Announce Type: new Abstract: Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Sinc

Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning

arXiv:2604.01170v2 Announce Type: replace-cross Abstract: While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute

Optimal Rates for Learning with Monotone Adversaries

arXiv:2608.06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.i.d. labeled sample and appends a finite number of further examples of its choice, every one of them labeled correc

OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations

arXiv:2608.06264v1 Announce Type: new Abstract: The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors fr

Patient Pose Assessment Using a CT-Based Framework for Synthetic Data Generation

arXiv:2608.06126v1 Announce Type: new Abstract: An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one o

Perturbation Sensitivity at Convergence: A Simple Signal for Identifying Spuriously Correlated Samples

arXiv:2608.05419v1 Announce Type: cross Abstract: Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations w

Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity

arXiv:2608.05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite th

Pixel-TTS: Image based Text Rendering for Robust Text-to-Speech

arXiv:2606.14750v2 Announce Type: replace-cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understa

Posture and Sustainment Optimization Under Adversarial Uncertainty

arXiv:2608.05256v1 Announce Type: new Abstract: Pre-commitment posture, the assignment of military assets to theater locations before conflict scenarios resolve, is a critical and formally unsolved pr

Potential Matching Optimal Transport: Continuous Normalizing Flows for Exact p-Wasserstein Dynamics

arXiv:2608.05666v1 Announce Type: new Abstract: We introduce Potential Matching Optimal Transport (PMOT), a potential-flow framework for general p-cost optimal transport with c_p(x,y)=|x-y|^p. PMOT pa

Predicting Social Media User Actions: A Hybrid Approach for Common and Rare Behavior Prediction on Bluesky

arXiv:2511.17241v2 Announce Type: replace Abstract: Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing appro

ProDVI: Programmatic Dynamics Priors for Value Network Initialization

arXiv:2608.06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch,

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism

arXiv:2507.00673v2 Announce Type: replace-cross Abstract: Image segmentation is central to automated medical image analysis, enabling precise identification of anatomical structures and pathological r

Provably Efficient Self-Calibrating Quantum Fault Tolerance

arXiv:2608.05686v1 Announce Type: cross Abstract: Quantum error correction protects logical information only when every physical operation remains below the fault-tolerance threshold, a condition that

QEvict: Recoverable Quantized KV Eviction for Attention-Drift-Robust Long-Context Decoding

arXiv:2608.05326v1 Announce Type: cross Abstract: Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache. A dominant line of work

Quality Diversity for Reliable Data Driven Time-Use Optimization

arXiv:2608.05230v1 Announce Type: cross Abstract: The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can

Reasoning Errors Have a Region and a Direction in the Residual-Stream Trajectory of LLMs

arXiv:2608.05660v1 Announce Type: cross Abstract: As language models are increasingly used for tasks that require verifiable reasoning, reliably distinguishing sound reasoning from flawed reasoning ha