Search: “models”

Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm

arXiv:2606.22466v1 Announce Type: new Abstract: Federated learning (FL) is an emerging distributed machine learning paradigm that enables local devices to jointly train a global model while keeping da

local-aiarxiv-cs-lg

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Few-Shot Hyperspectral Aphid Detection via FastGAN Synthetic Data Generation, Transformer-Based Classification and Explainable AI

DGX agent

arXiv:2606.21267v1 Announce Type: new Abstract: Early detection of aphid infestation in crops is essential for preventing yield loss and reducing unnecessary pesticide use. Hyperspectral imaging combi

researcharxiv-cs-cv

23 Jun 2026

FLM-Occ: Feed-forward Likelihood Maximization for Efficient Indoor Occupancy Prediction

arXiv:2606.21373v1 Announce Type: new Abstract: Recent indoor occupancy prediction methods adopt Gaussian primitives as a sparse 3D representation for computational efficiency. However, their training

Flow Matching for Efficient and Scalable Data Assimilation

arXiv:2508.13313v5 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (

From Gradient Clipping to Structural Refinement: Improving DPSGD for Medical Image Segmentation

arXiv:2606.21763v1 Announce Type: new Abstract: Medical image segmentation is widely used for disease detection but relies on sensitive data, raising privacy concerns as trained models can leak inform

safetyarxiv-cs-cv

FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation

arXiv:2606.13102v2 Announce Type: replace Abstract: Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups.

safetyarxiv-cs-ro

Generative Relightable Avatars

arXiv:2606.22718v1 Announce Type: new Abstract: We present Generative Relightable Avatars (GRA), a person-specific method for photorealistic free-view rendering and environment-map relighting of full-

Generative Robust Optimisation

arXiv:2606.22536v1 Announce Type: new Abstract: Classical uncertainty sets for robust optimisation impose fixed geometric shapes that cannot represent the complex dependencies present in real-world da

applicationsarxiv-cs-lg

Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning

arXiv:2606.22995v1 Announce Type: new Abstract: Group-based Reinforcement Learning (RL) has significantly enhanced Large Language Models (LLMs) in agentic scenarios. To achieve finer-grained policy up

safetyarxiv-cs-lg

Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation

arXiv:2602.03448v2 Announce Type: replace Abstract: Multi-subject image generation aims to synthesize images that faithfully preserve the identities of multiple reference subjects while following text

Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture

arXiv:2606.22834v1 Announce Type: new Abstract: We present homographic navigation, a geometry-centric framework for guiding camera acquisition toward precise capture of planar regions. Rather than tre

Human and AI collaboration for pulmonary nodule segmentation

arXiv:2606.22486v1 Announce Type: new Abstract: Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, parti

HumanHalo -- Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC

arXiv:2510.17525v3 Announce Type: replace Abstract: Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on

safetyarxiv-cs-ro

Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow

arXiv:2606.20655v1 Announce Type: cross Abstract: Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to

InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars

arXiv:2606.22905v1 Announce Type: new Abstract: Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing approaches struggle to mai

Interest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization

arXiv:2606.22353v1 Announce Type: new Abstract: Fidelity and perceptual quality are two inherently competing and conflicting objectives in the image super-resolution (SR) task. Different loss function

Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations

arXiv:2512.09021v3 Announce Type: replace-cross Abstract: Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the interga

Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport

arXiv:2606.21256v1 Announce Type: new Abstract: Quantum pure-state ensembles live on complex projective space, making flat Euclidean generative modeling geometrically mismatched. We introduce Intrinsi

Jacobian-Adaptive Weighting for Stability: Enhancing Long-term Rollout of Neural Partial Differential Equation Solvers via Spatially-Adaptive Regularization

arXiv:2603.05538v3 Announce Type: replace Abstract: Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors d

Jacobian-Aware Posterior Sampling for Inverse Problems

arXiv:2511.18471v3 Announce Type: replace Abstract: Diffusion models provide powerful generative priors for solving inverse problems by sampling from a posterior distribution conditioned on corrupted

Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding

arXiv:2603.05663v2 Announce Type: replace Abstract: Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long videos, making video-language-model prohibitively

Kernel of Partition Paths: A Unified Representation for Tree Ensembles

arXiv:2606.18853v2 Announce Type: replace-cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening rou

Lighting-Consistent Object Transfer Across Radiance Fields

arXiv:2606.22481v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in m

Look Before You Zoom: Adaptive Routing for the Resolution-Context Trade-off in Visual RAG

arXiv:2606.21968v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle as query-relevant objects become smaller. To address this, recent training-free approaches dynamically retrieve a

Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory

arXiv:2606.23195v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade dur

safetyarxiv-cs-lg

Tutorials

MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning

arXiv:2606.21419v1 Announce Type: new Abstract: Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance

tutorialsarxiv-cs-cv

Tutorials

Native space based pipelines outperform template space based pipeline in subcortical segmentation

arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa

tutorialsarxiv-cs-cv

On Quantum Perceptron Learning via Quantum Search

arXiv:2503.17308v2 Announce Type: replace-cross Abstract: With the growing interest in quantum machine learning, the perceptron, a fundamental building block in traditional machine learning, has emerg

On the Use of AI-Driven Immersive Digital Technologies for Designing and Operating UAVs

arXiv:2407.16288v3 Announce Type: replace Abstract: Uncrewed Aerial Vehicles (UAVs) offer agile, cost-effective, and efficient solutions for communication relay networks. However, their modeling and c

applicationsarxiv-cs-ro

One-Shot Data Selection for Medical Image Classification via Graph Coverage

arXiv:2606.22002v1 Announce Type: new Abstract: Training medical image classifiers on entire datasets is wasteful when annotation budgets are limited: not all samples contribute equally, yet acquiring

Online Learning of Robust Legged Odometry with Minimal Exteroceptive Supervision

arXiv:2606.21669v1 Announce Type: new Abstract: Robust locomotion and navigation for legged robots relies heavily on dependable odometry. Traditional multi-sensor fusion for such state estimation requ

researcharxiv-cs-ro

OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation

arXiv:2606.22174v1 Announce Type: new Abstract: Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the up

safetyarxiv-cs-ro

OSOG: A Differentiable, Physics-Informed Synthetic Data Engine for Micro-Optical Environments

arXiv:2606.21381v1 Announce Type: new Abstract: Deep learning in computational microscopy is severely constrained by the scarcity of densely annotated datasets. While synthetic data generation has bri

applicationsarxiv-cs-cv

Agents

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate

arXiv:2606.20621v1 Announce Type: cross Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduc

agentsarxiv-cs-lg

phi-Scene: Physically Grounded Image-to-3D Scene Reconstruction

arXiv:2606.21596v1 Announce Type: new Abstract: Reconstructing compositional 3D scenes from a single image is a fundamental challenge in 3D world modeling. Recent methods can recover high-fidelity, co

safetyarxiv-cs-cv

PIAvatar: Physically Interactive Avatars via Deformation Gradient Decoupling

arXiv:2606.21162v1 Announce Type: cross Abstract: 3D human avatars have shown impressive visual fidelity driven by pose-conditioned models, yet they still lack the physical ability required for intera

Agents

Position: Correct Answer, Wrong Mechanism -- When AI Scientists Defend General Claims Their Own Data Contradicts

arXiv:2606.23175v1 Announce Type: new Abstract: AI scientist systems are described as tools, coauthors, or founders, but we evaluate them as if only the final answer matters. This position paper argue

agentsarxiv-cs-lg

Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning

arXiv:2606.21896v1 Announce Type: cross Abstract: Solar flares, particularly those of the M- and X-class, have a significant impact on human life because of their potential to disrupt critical infrast

local-aiarxiv-cs-lg

Prefix-Guided On-Policy Distillation: Mining Golden Trajectories from Rollouts

arXiv:2606.21994v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by applying dense teacher supervision on student-sampled trajectories. However, scaling OPD to lo

safetyarxiv-cs-lg

PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support

arXiv:2606.23673v1 Announce Type: cross Abstract: Mental health assessment commonly relies on isolated screening instruments or data-driven models that often lack interpretability and multi-dimensiona

applicationsarxiv-cs-lg

Tutorials

Published by @OReillyMedia here: https://www.oreilly.com/radar/drivetrain-approach-data-products/

tutorialsjeremy-howard--x

The Drivetrain Approach is a framework for building effective data products, developed by Jeremy Howard and published by O'Reilly Media, that focuses on defining business objectives, identifying lever

RaysUp: Ultra-light Universal Feature Upsampling via Geometry-Aware Ray Representation

arXiv:2606.22749v1 Announce Type: new Abstract: Pre-trained Vision Foundation Models (VFMs) have become central to modern computer vision due to their powerful semantic representations and strong gene

Hardware

Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation

arXiv:2606.20712v1 Announce Type: new Abstract: Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with

hardwarearxiv-cs-ro

ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis

arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation

Rejections Based on Predictive Uncertainty Enable Reliable Routine Soil Spectroscopy

arXiv:2606.21179v1 Announce Type: new Abstract: Soil properties relevant to agricultural and environmental applications are conventionally measured using elaborate laboratory methods involving physica

Agents

Robusto-2: Benchmarking Humans & VLMs for Autonomous Driving in Lima & New York City

arXiv:2606.20980v1 Announce Type: new Abstract: As Self-Driving Cars continue to expand internationally and use multi-modal systems such as VLMs as a cognitive backbone for their Action models; how we

agentsarxiv-cs-cv

RocketPFN: Accurate Time Series Classification via In-Context Learning

arXiv:2606.21786v1 Announce Type: new Abstract: We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-c

Safe Few-Step Generation via Velocity Editing

arXiv:2606.23267v1 Announce Type: new Abstract: Flow matching has recently emerged as a strong paradigm for state-of-the-art text-to-image (T2I) generation, enabling high-quality generation with a sma

safetyarxiv-cs-cv