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Fast and Faithful: Principled Conditional Flow Matching for Inverse Problems

arXiv:2609.12953v1 Announce Type: new Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-der

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Fast BIB simulation at a future Muon Collider with generative machine learning

DGX agent

arXiv:2609.12054v1 Announce Type: cross Abstract: Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider. In order to devel

researcharxiv-cs-lg

14 Sept 2026

Fewer Words, Not Fewer Tokens: Measuring the Sanskrit Tokenization Penalty per Proposition

arXiv:2609.12960v1 Announce Type: new Abstract: Sanskrit fuses case, number, person and tense into word endings and chains clauses into compounds, so it is information-dense per word. Whether that den

GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models

arXiv:2504.19023v2 Announce Type: replace-cross Abstract: Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing

Global Path Planner with Multi-Model Switching

arXiv:2609.13015v1 Announce Type: new Abstract: This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse te

researcharxiv-cs-ro

Guided Adversarial Robust Transfer Learning with Source Mixing

arXiv:2309.06534v2 Announce Type: replace Abstract: Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on

HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology

arXiv:2609.12835v1 Announce Type: new Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories.

High-Probability Convergence of SGD via Batched Updates

arXiv:2609.12765v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is the primary workhorse for large-scale optimization. While the average behavior of its iterates, typically charact

HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation

arXiv:2609.12151v1 Announce Type: new Abstract: The HSI-Road dataset provides paired RGB and 25-channel NIR (600--960~nm) images with binary masks but no surface-level labels.~This paper introduces a

Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval

arXiv:2510.20486v2 Announce Type: replace Abstract: Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event retrieval. In rain-rate retrieva

Hyperion: An AI-powered HPC cluster for sciences and humanities research that utilizes ML for predicting job turnaround time

arXiv:2609.11946v1 Announce Type: cross Abstract: Hyperion is an innovative high-performance computing (HPC) cluster developed for researchers in both science and humanities disciplines at the Univers

I Am No One: Style-Aware Paraphrasing for Text Anonymization

arXiv:2609.12341v1 Announce Type: new Abstract: Authorship attribution models can re-identify users from seemingly anonymized text by exploiting stable stylistic fingerprints, even after explicit iden

Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference

arXiv:2609.11969v1 Announce Type: cross Abstract: As the population ages, the number of patients with cardiovascular diseases continues to increase, highlighting the need for early detection before pr

IMPLY: Physically Anchored Consistency for World-Model Rollouts

arXiv:2609.12441v1 Announce Type: cross Abstract: A world model asked what happens if an object is pushed at several speeds produces several futures. If the model has the object in mind, those futures

In-Hospital Stroke Risk-State Classification from PPG-Derived Hemodynamic Features

arXiv:2602.09328v2 Announce Type: replace Abstract: The scarcity of temporally aligned pre-event physiological data limits the study of stroke risk states before documented clinical recognition. We fo

Inference for Newton Methods with Accelerated Sketch-and-Project via Random Scaling

arXiv:2609.12421v1 Announce Type: cross Abstract: We study an online sketched Newton method that approximates the Newton direction at each step via a state-of-the-art sketching solver, called the gene

Information-Induced Training Geometry: Exact Reduction, Canonical Completion, and Structured Expressivity

arXiv:2609.12991v1 Announce Type: new Abstract: Training data constrains optimizer geometry through the covectors visible to a declared information channel. We study how such partial information deter

Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translation

arXiv:2609.12993v1 Announce Type: cross Abstract: We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WSL

Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation

arXiv:2510.18019v3 Announce Type: replace Abstract: Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite

Judging by the Cover: Cleaning LLM Truthfulness Benchmarks to Avoid Surface-Level Feature Leakage

arXiv:2609.13003v1 Announce Type: new Abstract: Binary-choice truth benchmarks ask models to choose between a correct and an incorrect answer, but if the two answers differ systematically in surface-l

Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning

arXiv:2609.13045v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced significantly with speech LLMs, offering the potential for joint optimization and preserving non-lingui

Language Is an Insufficient Substrate for Quantitative Reasoning, and Consequential Domains Need Large Quantitative Models

arXiv:2609.12105v1 Announce Type: cross Abstract: The prevailing assumption in applied machine learning is that progress on consequential quantitative decisions such as pricing risk, allocating capita

LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations

arXiv:2609.12364v1 Announce Type: new Abstract: Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly

Learning Interaction Kernels from Collective Steady States

arXiv:2609.12004v1 Announce Type: cross Abstract: We propose a learning procedure for system identification in interacting particle systems from single-snapshot observations of collective behaviors, u

Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging

arXiv:2606.14957v4 Announce Type: replace Abstract: Brain MRIs are routinely acquired as multiple complementary sequences with unique contrast weighting, including T1-weighed imaging (T1w) anatomic an

Learning the Geometry of Collider Events with Metric-Aware Deep Sets

arXiv:2609.12024v1 Announce Type: cross Abstract: Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distanc

LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification

arXiv:2609.12915v1 Announce Type: new Abstract: Large-scale multi-label text classification assigns a small subset of relevant labels to each document from a vocabulary containing thousands or tens of

Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision

arXiv:2504.04903v3 Announce Type: replace Abstract: We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-tasks

Measuring Pragmatic Influence in Large Language Model Instructions

arXiv:2602.21223v2 Announce Type: replace Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we ask them. Phrases like ``This is urgent'' or ``As your s

Membership Inference via Pairwise Likelihood Ratios

arXiv:2609.12367v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) are the standard tool for auditing the privacy risks of machine learning models. Given a query point, an MIA aims

MGAvatar: Mesh-Bound Gaussians for Head Avatar Geometry and Appearance Modeling

arXiv:2609.12850v1 Announce Type: new Abstract: Accurate head modeling requires a stable yet expressive geometric representation. Existing Gaussian-based head avatars commonly rely on parametric templ

MPT: Missing Prototype Tracking via Barycentric Reconstruction in Vehicular Federated Learning

arXiv:2609.12771v1 Announce Type: cross Abstract: Cross-vehicle federated learning enables vehicles to collaboratively improve perception models while keeping locally collected driving data private. H

Observation-Anchored Selective Assimilation for Longitudinal Tumor-State Proxy Forecasting in Post-Treatment Glioma

arXiv:2609.12435v1 Announce Type: new Abstract: Post-treatment MRI in patients with glioma provides serial observations for updating patient-specific tumor-state proxy estimates, but variable appearan

On Identifying Adversarial Intent Injection in AI-Native 6G Networks

arXiv:2609.12144v1 Announce Type: cross Abstract: AI-native 6G networks have brought Intent-Based Networking (IBN) to the forefront, enabling high-level goals to be translated into network configurati

On the Residual Scaling of Looped Transformers: Stability and Transferability

arXiv:2606.18524v2 Announce Type: replace Abstract: Looped (weight-tied) Transformers apply a shared residual block N times (h leftarrow h + arepsilon,f(h), same f at each step), increasing effective

On Universality of Non-Separable Approximate Message Passing Algorithms

arXiv:2506.23010v2 Announce Type: replace-cross Abstract: Mean-field characterizations of first-order iterative algorithms -- including Approximate Message Passing (AMP), stochastic and proximal gradi

One thing will defeat the AI Doomers easily: Time. Little by little things will change and AI will diffuse into everyday people's lives and …

One thing will defeat the AI Doomers easily: Time. Little by little things will change and AI will diffuse into everyday people's lives and they'll realize it wasn't the scary bedtime story Harry Pott

researchyann-lecun--x

[P] Wine synthesis using VAE [P]

researchr-machinelearning

I have created a VAE model using PyTorch on White Wine dataset. Basically, the main goal is to discover a brand-new white wine recipe. It puts all the wines into a latent space, finds the best part wh

Parallel Training Using a CNN-DNN Architecture for Accelerated Development of Diagnostic Models

arXiv:2609.12902v1 Announce Type: new Abstract: Artificial intelligence has shown promise in assisting radiologists in imaging-based diagnosis across a wide range of diseases. Efficient training of la

Patient-Reported Survey Data Improve Prediction of Opioid Use Disorder

arXiv:2609.12224v1 Announce Type: new Abstract: Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD). We evaluated whether surve

PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning

arXiv:2403.05864v3 Announce Type: replace Abstract: In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cl

PhD branding question [R]

researchr-machinelearning

I'm starting a PhD where I will be doing Graph ML (somewhere along the lines of graph signal processing/ graph deep learning.) My eventual goal is research scientist at big tech, or whichever company

Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion

arXiv:2609.12899v1 Announce Type: new Abstract: Full waveform inversion (FWI) estimates subsurface velocity from seismic recordings, but its ill-posedness and nonlinearity make accurate reconstruction

Physics-Guided Synthetic High-Frequency Ultrasound Generation for Skin Layer Segmentation

arXiv:2609.12735v1 Announce Type: cross Abstract: High-frequency ultrasound (HFUS) enables noninvasive visualization of superficial skin structures, but automated skin-layer analysis is limited by the

Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators

arXiv:2609.11935v1 Announce Type: new Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDE

PhysioAI: Clinical Knowledge-Guided Semantic Supervision for Skeleton-Based Physiotherapy Action Recognition

arXiv:2609.12491v1 Announce Type: new Abstract: Skeleton-based action recognition can support automated tracking of physiotherapy exercises, particularly in remote rehabilitation settings where contin

Pixel Decodability Is Not a Compression Signal: Causally Evaluating Importance Proxies for Visual KV-Cache Eviction

arXiv:2609.13012v1 Announce Type: new Abstract: Vision-language models retain a substantial amount of pixel-decodable visual content in their visual key-value cache. We show, in our setting, that this

PLSP (Pre-hoc Liminal Space Profiling): OOD Prediction over Detection -- An Anticipatory Approach for Machine Learning Model Reliability

arXiv:2609.12225v1 Announce Type: cross Abstract: Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing O