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Looking forward to chatting with @hugobowne today (July 27) at 4 pm PT on the Vanishing Gradient livestream on YouTube. Will cover open sour…

agentssebastian-raschka--x

Looking forward to chatting with @hugobowne today (July 27) at 4 pm PT on the Vanishing Gradient livestream on YouTube. Will cover open source, the newest LLMs & trends, agent frameworks, and whatever

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Safety

Minimum Norm Interpolation via the Local Theory of Banach Spaces: The Role of 2-Uniform Convexity

DGX agent

arXiv:2603.28956v2 Announce Type: replace-cross Abstract: The minimum-norm interpolator (MNI) framework has recently attracted considerable attention as a tool for understanding generalization in over

safetyarxiv-cs-lg

27 Jul 2026

SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images

arXiv:2607.22332v1 Announce Type: new Abstract: Interactive deep image segmentation enables efficient medical image annotation by iteratively refining predictions from user prompts, such as positive a

researcharxiv-cs-cv

Statistical mechanics of extensive-width Bayesian neural networks near interpolation

arXiv:2505.24849v2 Announce Type: replace-cross Abstract: For three decades statistical mechanics has been providing a framework to analyse neural networks. However, the theoretically tractable models

tutorialsarxiv-cs-lg

Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations

arXiv:2607.21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA). Th

Applications

A Graph Neural Network approach to zero-shot Digital Twins

arXiv:2607.20535v1 Announce Type: cross Abstract: Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical

applicationsarxiv-cs-ai

Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

model-releasesarxiv-cs-ai

arXiv:2607.21482v1 Announce Type: new Abstract: Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their

An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware

arXiv:2607.20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct

[audio.cpp] Release 0.4: Higgs Audio v3 TTS 4B (10x real time)+ Fish Audio S2 Pro in C++/GGML, full GGUF loading, Q8 speed and VRAM gains

model-releasesr-localllama

audio.cpp again :) Release 0.4 is out. The headline this time is new high-quality TTS coverage plus GGUF becoming a first-class across the project. What’s new: Added Higgs Audio v3 TTS 4B, Fish Audio

Automatic knot selection in smooth additive models

arXiv:2607.21083v1 Announce Type: cross Abstract: B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the numb

researcharxiv-cs-lg

Bound-Founded Semantics for Answer Set Programming with Difference Constraints: Preliminary Report

arXiv:2607.21201v1 Announce Type: new Abstract: While the integration of linear constraints has significantly expanded the reach of Answer Set Programming (ASP), existing hybrid solvers often rely on

Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents

arXiv:2607.21125v1 Announce Type: new Abstract: Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Exist

agentsarxiv-cs-cv

Context-weighted Discrete Flow Matching

arXiv:2607.21427v1 Announce Type: new Abstract: Discrete flow matching provides a flexible framework for generative modeling on discrete structures. However, the standard factorized training objective

researcharxiv-cs-lg

Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing

arXiv:2607.20973v1 Announce Type: new Abstract: This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dy

agentsarxiv-cs-ro

Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval

arXiv:2607.20984v1 Announce Type: new Abstract: This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task

safetyarxiv-cs-cv

DS@GT ARC at ImageCLEFmed GANs 2026: Geometric Filtering for Privacy-Preserving CT Slice Generation

arXiv:2607.20692v1 Announce Type: cross Abstract: We present a privacy-preserving framework for synthetic lung CT slice generation developed for the Image-CLEFmed GANs 2026 challenge. The approach com

Explanation-Based Runtime Verification for Trustworthy ML-driven Optical Networks

arXiv:2607.20675v1 Announce Type: new Abstract: Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, perform

researcharxiv-cs-lg

HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices

arXiv:2607.21019v1 Announce Type: new Abstract: Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisat

agentsarxiv-cs-ai

Loss-Complexity Landscape and Model Structure Functions

arXiv:2507.13543v5 Announce Type: replace-cross Abstract: We develop a framework for dualizing the Kolmogorov structure function h_x(alpha), which then allows using computable complexity proxies. We e

ProCap: Prominence-guided Object Rectification for Faithful and Comprehensive Video Captioning

arXiv:2607.21022v1 Announce Type: new Abstract: Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing t

Applications

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

arXiv:2607.20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is

applicationsarxiv-cs-ai

Applications

Smooth Neural Point Processes via B-Splines

arXiv:2607.21098v1 Announce Type: new Abstract: Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time. Neural networks have been

applicationsarxiv-cs-lg

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation

arXiv:2510.24616v4 Announce Type: replace-cross Abstract: For four decades statistical physics has been providing a framework to analyse neural networks. A long-standing question remained on its capac

tutorialsarxiv-cs-lg

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

arXiv:2607.21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern. Early and accurate diagnosis is critical for enabling timely intervention and improving

4DGS360: 360{eg} Gaussian Reconstruction of Dynamic Objects from a Single Video

arXiv:2603.21618v2 Announce Type: replace Abstract: We introduce 4DGS360, a diffusion-free framework for 360^{irc} dynamic object reconstruction from casual monocular video. Existing methods often fai

Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

arXiv:2607.19086v1 Announce Type: new Abstract: Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, cli

tutorialsarxiv-cs-cv

Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction

arXiv:2607.19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons. Existing di

DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning

model-releasesarxiv-cs-ai

arXiv:2604.02694v2 Announce Type: replace-cross Abstract: The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document safet

Local Ai

FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense

arXiv:2607.19674v1 Announce Type: cross Abstract: Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based approaches t

local-aiarxiv-cs-ai

Importance-Aware OBS Pruning for Diffusion Models

arXiv:2607.20048v1 Announce Type: new Abstract: We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically sali

Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds

arXiv:2607.18135v1 Announce Type: cross Abstract: Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body

safetyarxiv-cs-lg

Local Ai

LAVIFT: Latent-Action-Guided Vision Fine-Tuning for Surgical Interaction Recognition

arXiv:2607.19889v1 Announce Type: new Abstract: Understanding instrument-tissue interactions is essential for context-aware surgical AI and autonomous robotic surgery. Pretrained vision-language model

local-aiarxiv-cs-cv

Learning Personalized Safety Interventions for Haptic Human-Robot Shared Control

arXiv:2607.19534v1 Announce Type: new Abstract: Haptic feedback provides an implicit channel for communicating safety intentions during human-robot shared control. Existing haptic guidance systems typ

safetyarxiv-cs-ro

Logic-Guided Data Extraction with Answer Set Programming and Large Language Models

arXiv:2607.19365v1 Announce Type: new Abstract: When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from natural langua

tutorialsarxiv-cs-ai

MagicPrompt: Ultra-Lightweight Prompt Tuning for Video Generation

arXiv:2607.14595v2 Announce Type: replace Abstract: Large-scale video diffusion models deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational

MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing

arXiv:2607.19935v1 Announce Type: new Abstract: Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Sub

safetyarxiv-cs-ai

Multi-stage Dynamic Selection for Cross-Project Defect Prediction

arXiv:2607.20151v1 Announce Type: cross Abstract: Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from t

Hardware

NGPS: GPS-Denied Aerial Geo-Localization and 2.5D Reconstruction via Deep Satellite Image Matching and Multi-Rate Sensor Fusion

arXiv:2607.18936v1 Announce Type: cross Abstract: We present NGPS (Next-Generation Positioning System), a visual geo-localization framework for high-altitude UAVs that provides GPS-free absolute posit

hardwarearxiv-cs-cv

Optimal Placement of Docking Stations and Resident AUVs for Subsea Pipeline Inspection

arXiv:2607.19944v1 Announce Type: cross Abstract: A two-stage mixed-integer linear programming framework is introduced for subsea pipeline incident response planning, jointly optimizing Subsea Docking

agentsarxiv-cs-ro

PathReportEval: A Systematic Benchmark for Pathology Report Generation

arXiv:2607.18448v1 Announce Type: cross Abstract: Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure beca

Reliability-Aware Hard--Soft Physics-Informed Neural Networks for Robust Learning of Challenging Partial Differential Equations

arXiv:2607.19377v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, but their training is often affecte

Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems

arXiv:2607.19650v1 Announce Type: new Abstract: This work presents a decentralized, spoofing-aware trajectory planning framework for small unmanned aerial systems operating under Remote Identification

agentsarxiv-cs-ro

Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning

arXiv:2607.19845v1 Announce Type: cross Abstract: This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent fac

Sound Probabilistic Safety Bounds for Large Language Models

arXiv:2607.20286v1 Announce Type: cross Abstract: We propose a novel framework for computing rigorous bounds on the probability that a large language model (LLM) generates harmful output to a given pr

safetyarxiv-cs-ai

Toward Seasonal Guidelines for Robust Deep-Learning Sentinel-2 Building Detection in Different Area Types

arXiv:2607.19994v2 Announce Type: new Abstract: Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; howeve

researcharxiv-cs-cv

Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

arXiv:2607.19621v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult du

V2F: Vision-Informed Grasp Force Prediction for Damage-Aware Robotic Handling of Date Fruits

arXiv:2607.19804v1 Announce Type: new Abstract: This paper presents a vision-informed grasp force prediction framework for robotic handling of date fruits. Addressing the dual challenge of high detach

researcharxiv-cs-ro

Variance-reduced Domain Adaptation using Paired Sampling

arXiv:2607.20367v1 Announce Type: new Abstract: Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). Ho

safetyarxiv-cs-lg