Search: “models”

Hard or Just Unreached? Diagnosing the Sampling Blind Spot in Math-Reasoning Difficulty Estimation

arXiv:2606.19636v2 Announce Type: replace Abstract: Math and science reasoning benchmarks rely on pass@k, the fraction of sampled chains that reach gold, as the canonical per-example difficulty signal

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Safety

Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination

DGX agent

arXiv:2606.20962v1 Announce Type: new Abstract: High-performing human-human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams

safetyarxiv-cs-ro

23 Jun 2026

Horizon Adaptive Offline Policy Learning via Value Stitching

arXiv:2606.21136v1 Announce Type: new Abstract: Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional tempor

Agents

Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI

arXiv:2606.22971v1 Announce Type: cross Abstract: Occupancy prediction at voxel-level granularity is essential for safe robotic navigation and interaction in complex environments. Existing occupancy d

agentsarxiv-cs-cv

Influencer Cartels

arXiv:2405.10231v3 Announce Type: replace-cross Abstract: Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in

Integrated Marketing Attribution: A Bayesian Framework for Privacy-Safe Granular Measurement Anchored in MMM

arXiv:2606.16878v3 Announce Type: replace Abstract: Retail marketing measurement increasingly requires granular campaign-level insights without relying on user-level tracking. However, the two dominan

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence

arXiv:2606.19538v2 Announce Type: replace-cross Abstract: Convolutional networks, recurrent networks, and transformers each encode different inductive biases -- locality, sequential memory, and conten

local-aiarxiv-cs-lg

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

arXiv:2509.22307v2 Announce Type: replace Abstract: Lightweight 3D medical image segmentation remains constrained by a fundamental extit{``efficiency / robustness conflict''}, particularly when proces

local-aiarxiv-cs-cv

KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation

arXiv:2606.22113v1 Announce Type: new Abstract: Generalizing manipulation policies across robot embodiments remains difficult because standard policies entangle task reasoning with embodiment-specific

safetyarxiv-cs-ro

Tutorials

LEViL: Label-Efficient Video Learning via Zero-Shot Distillation over VLM-Generated Pseudo-Label Spaces

arXiv:2606.21358v1 Announce Type: new Abstract: Supervised video pretraining is a common transfer learning practice for improving downstream action recognition performance. However, it requires large-

tutorialsarxiv-cs-cv

Agents

LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms

arXiv:2606.20729v1 Announce Type: cross Abstract: Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed app

agentsarxiv-cs-lg

LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN

arXiv:2606.23110v1 Announce Type: cross Abstract: Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrad

LSTM Variants for Chaotic Dynamical Systems: An Empirical Study on the Lorenz Attractor

arXiv:2606.22662v1 Announce Type: new Abstract: Forecasting chaotic dynamical systems such as the Lorenz attractor is notoriously difficult: small numerical errors are amplified exponentially over lon

LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

arXiv:2304.12319v2 Announce Type: replace-cross Abstract: Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distort

MeshFlow: Mesh Generation with Equivariant Flow Matching

arXiv:2606.23489v1 Announce Type: cross Abstract: Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning

arXiv:2606.21943v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a nar

Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion

arXiv:2606.21135v1 Announce Type: new Abstract: Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into sing

Omega: Operator-based Mixture Ensemble for Generative Assimilation

arXiv:2606.20920v1 Announce Type: new Abstract: Characterizing non-Gaussian posterior distributions in partially observed high-dimensional nonlinear systems remains a fundamental challenge in data ass

On-Manifold Variational Learning with Heat-Kernel Priors

arXiv:2606.18658v2 Announce Type: replace Abstract: Learning unsupervised representations of medical imaging cohorts can reveal clinically meaningful prototypes without expert labels, which are often

On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

arXiv:2606.22352v1 Announce Type: new Abstract: Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regulariz

OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

arXiv:2606.22101v1 Announce Type: cross Abstract: Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrie

Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning

arXiv:2606.21396v1 Announce Type: new Abstract: Robot-Assisted Surgery is integral to modern minimally invasive procedures, with automation emerging as the next frontier to enhance precision and reduc

safetyarxiv-cs-ro

P-JEPA: Procedural Video Representation Learning via Joint Embedding Predictive Architecture

arXiv:2606.23256v1 Announce Type: new Abstract: The increasing maturity of embodied AI platforms has driven a growing interest in procedural video representation learning to support intelligent assist

local-aiarxiv-cs-cv

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks

arXiv:2603.24196v2 Announce Type: replace-cross Abstract: Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-pr

local-aiarxiv-cs-lg

Hardware

RAVEN: Agentic RAG for Automated Vulnerability Repair

arXiv:2606.22647v1 Announce Type: cross Abstract: Automated vulnerability repair has emerged as a promising direction to mitigate the growing number of software vulnerabilities. Recent advances in Lar

hardwarearxiv-cs-lg

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission

arXiv:2606.23361v1 Announce Type: new Abstract: Backdoor attacks on molecular graph neural networks (GNNs) are typically evaluated as abstract graph edits, but real molecular learning pipelines do not

Scaling up fine-grained intracranial vessel annotations in computed tomography angiography

arXiv:2606.21756v1 Announce Type: cross Abstract: In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the d

Selective Time Series Forecasting via Metalearning

arXiv:2606.23448v1 Announce Type: new Abstract: Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instance

Short-Term Electricity Demand Forecasting for New England Using a Hybrid Transformer-XGBoost Framework with Weather, Calendar, and COVID-19 Indicators

arXiv:2606.20918v1 Announce Type: new Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimizat

ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging

arXiv:2606.21099v1 Announce Type: new Abstract: Variational inference (VI) is a powerful method for principled posterior inference for scientific inverse imaging. VI learns the posterior distribution,

Simplex-Constrained Sparse Bagging: Transitioning from Uniform Priors to Sparse Posteriors in Ensemble Learning

arXiv:2606.13589v2 Announce Type: replace Abstract: We present Simplex-Constrained Sparse Bagging (SCSB), a mathematically rigorous framework for post-training compression and probability calibration

local-aiarxiv-cs-lg

Specificity- and Calibration-Aware Breast Ultrasound Segmentation via Entropy-Guided Boundary Supervision

arXiv:2606.22308v1 Announce Type: cross Abstract: Lesion segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior ac

Hardware

SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning

arXiv:2606.21973v1 Announce Type: new Abstract: Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractiv

hardwarearxiv-cs-lg

SteerVTE: Seamless Video Text Editing with Style and Glyph Control

arXiv:2606.23254v1 Announce Type: new Abstract: Visual text editing aims to precisely modify text in images and videos while preserving stylistic consistency and visual realism. Despite significant ad

local-aiarxiv-cs-cv

Stochastic Signed Distance Processes

arXiv:2606.20856v1 Announce Type: new Abstract: Multi-view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distanc

Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization

arXiv:2507.03318v3 Announce Type: replace Abstract: Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular s

Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions

arXiv:2606.23477v1 Announce Type: cross Abstract: Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity

arXiv:2606.21866v1 Announce Type: new Abstract: Co-design of legged robots with elastic elements is challenging due to the non-differentiability of contact dynamics and mechanism engagement. This pape

safetyarxiv-cs-ro

Synthetic Network Packet Generation through Statistical Learning and Genetic Algorithms

arXiv:2606.20864v1 Announce Type: cross Abstract: Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions ac

T-Rex: Tactile-Reactive Dexterous Manipulation

arXiv:2606.17055v2 Announce Type: replace Abstract: The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based

researcharxiv-cs-ro

TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations

arXiv:2606.21189v1 Announce Type: new Abstract: Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution

Agents

Towards Adaptive Categories: Dimensional Governance for Agentic AI

arXiv:2505.11579v3 Announce Type: replace-cross Abstract: As AI systems evolve from static tools to dynamic agents, traditional categorical governance frameworks -- based on fixed risk tiers, levels o

agentsarxiv-cs-lg

Universal priors: solving empirical Bayes via Bayesian inference and pretraining

arXiv:2602.15136v2 Announce Type: replace-cross Abstract: We theoretically justify the recent empirical finding of [Teh et al., 2025] that a transformer pretrained on synthetically generated data achi

UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation

arXiv:2606.23503v1 Announce Type: new Abstract: Vision Transformers (ViT) dominate computer vision. However, their reliance on rigid patch projectors hinders transfer to Earth Observation (EO), where

Applications

Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction

arXiv:2606.22787v1 Announce Type: new Abstract: Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption: the scenes is entire di

applicationsarxiv-cs-cv

ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors

arXiv:2606.22371v1 Announce Type: cross Abstract: Recent generative video compression methods leverage powerful generative priors to achieve perceptually pleasing reconstructions. However, most existi

Hardware

A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting

arXiv:2606.11390v1 Announce Type: new Abstract: Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world. However, they are often limited in scale and re

hardwarearxiv-cs-cv

11 Jun 2026

Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems

arXiv:2606.12337v1 Announce Type: cross Abstract: Inverse problems governed by partial differential equations (PDEs) are central to computational mechanics and are commonly solved by adjoint-based opt