Search: “research”

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

arXiv:2606.29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluat

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FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images

DGX agent

arXiv:2606.30347v1 Announce Type: cross Abstract: We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more r

researcharxiv-cs-ai

30 Jun 2026

FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization

arXiv:2606.28933v1 Announce Type: new Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand

researcharxiv-cs-cl

First-Order Temporal Logic Tensor Networks

arXiv:2606.29972v1 Announce Type: new Abstract: Most of the existing neuro-symbolic AI methods focus on the scenario of static knowledge where objects do not change according to a temporal dimension.

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning

arXiv:2606.28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenario

Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement

arXiv:2606.29150v1 Announce Type: new Abstract: Discrete flow models have recently shown promising performance on few-step text generation; however, when naively applied to structured reasoning tasks

Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector

arXiv:2606.30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstr

FR-DETR: Frequency and Recurrent Feature Refinement for Robust Object Detection under Adverse Weather

arXiv:2606.30471v1 Announce Type: new Abstract: Object detection under adverse weather remains challenging due to severe visual degradations and domain shifts. Existing enhancer-based approaches attem

Fractional Stochastic Neural Networks

arXiv:2606.29438v1 Announce Type: cross Abstract: In this paper, we develop a fractional stochastic neural network with residual dynamics driven by fractional Brownian motion. By introducing a discret

Frames2Residual: Spatiotemporal Decoupling for Self-Supervised Video Denoising

arXiv:2603.10417v2 Announce Type: replace Abstract: Self-supervised video denoising methods typically extend image-based frameworks into the temporal dimension, yet they often struggle to integrate in

From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution

arXiv:2601.08341v2 Announce Type: replace Abstract: Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resoluti

From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs

arXiv:2606.29461v1 Announce Type: new Abstract: We propose a self-supervised pretraining framework for learning sub-surface scattering (SSS) light transport representations from minimal input. Our met

From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP

arXiv:2601.07988v2 Announce Type: replace-cross Abstract: While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents ar

GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition

arXiv:2403.15212v3 Announce Type: replace Abstract: Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision. The recent state-of-the-art (SOTA) models

Generating in the Limit with Infinitely Many Hallucinations

arXiv:2606.28354v1 Announce Type: new Abstract: The classic paradigm of language identification in the limit models learning as a game between an adversary, who reveals strings from an unknown target

researcharxiv-cs-cl

Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks

arXiv:2409.11972v4 Announce Type: replace-cross Abstract: Enabling robots to autonomously discover high-level spatial concepts (e.g., rooms and walls) from primitive geometric observations (e.g., plan

Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images

arXiv:2606.28510v1 Announce Type: cross Abstract: Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated

Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials

arXiv:2606.29584v1 Announce Type: cross Abstract: Cl(3,0) interatomic potentials, despite their algebraic elegance, predict force magnitudes accurately but force directions poorly. Across ten rMD17 mo

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

arXiv:2606.29161v1 Announce Type: new Abstract: Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical met

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets

arXiv:2505.20928v2 Announce Type: replace Abstract: Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the

Gradient boosting with vector-valued leafs

arXiv:2606.29326v1 Announce Type: cross Abstract: Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems using simple objective functions based

Graph-GSReg: Leveraging 3D Scene Graphs for Gaussian Splatting Registration

arXiv:2606.29782v1 Announce Type: new Abstract: Merging multiple 3D Gaussian Splatting (3DGS) scenes into a single unified Gaussian representation is essential for large-scale 3D mapping and long-term

Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua

arXiv:2606.30117v1 Announce Type: cross Abstract: We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and t

Ground4D: Consistency-Aware 4D Reconstruction from Monocular Video

arXiv:2606.28828v1 Announce Type: new Abstract: Learning a 4D scene representation from a single monocular video that supports dynamic novel-view synthesis while maintaining faithful geometry over tim

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

arXiv:2507.00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy. We apply four generative diffusion

H-GRPO: Permutation-Invariant Reinforcement Learning for Grounded Visual Reasoning

arXiv:2606.29915v1 Announce Type: new Abstract: Vision-Language Models (VLMs) often achieve high performance on benchmarks while remaining 'black boxes', yet they remain prone to hallucination or rely

Harvesting AI Computation at the Edge via Generic Approximation

arXiv:2606.29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.

HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks

arXiv:2606.30516v1 Announce Type: new Abstract: Deploying large convolutional neural networks (CNNs) on resource-constrained devices is challenging due to their high computational cost. While dynamic

Heads, Not Backbones: Output Heads Dominate Architectures on Fat-Tailed Returns

arXiv:2606.30037v1 Announce Type: new Abstract: In a deep forecasting pipeline for fat-tailed financial returns at short horizons, which matters more - the backbone architecture or the output head? We

HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers

arXiv:2603.12222v2 Announce Type: replace Abstract: Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constraint hard

Hierarchical Reinforcement Learning in StarCraft Micromanagement with Influence Maps and Cluster-based Scripts

arXiv:2606.30092v1 Announce Type: new Abstract: Real-time strategy (RTS) games present significant AI challenges, characterized by expansive state-action spaces arising from multi-unit coordination in

High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator

arXiv:2602.13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating

High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling

arXiv:2606.30511v1 Announce Type: new Abstract: Reliable high-resolution flood extent mapping from satellite imagery remains constrained by limited data fidelity and sensor-specific artifacts. Multisp

Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions

arXiv:2606.30310v1 Announce Type: cross Abstract: The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional

HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video

arXiv:2606.29333v1 Announce Type: new Abstract: Uncalibrated volumetric video streaming for human reconstruction is essential for holographic communication and AR/VR, yet remains challenging due to th

HomeDiffusion: Zero-Shot Object Customization with Multi-View Representation Learning for Indoor Scenes

arXiv:2606.29828v1 Announce Type: new Abstract: Recently, zero-shot object customization generation methods have rapidly developed and shown tremendous potential for applications. For instance, in the

HorizonRelight: Relighting Long-horizon Videos Consistently via Diffusion Transformers

arXiv:2606.29095v1 Announce Type: new Abstract: Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short cl

How Token Influence Decays with Distance: A Green-Function View of Trained Language Models

arXiv:2606.29139v1 Announce Type: new Abstract: We study how the next-token prediction of an autoregressive Transformer language model changes under small perturbations of earlier input token embeddin

HSAP: A Hierachical Sequence-aware Parallelism for Hybrid-Context Generative Models

arXiv:2606.30460v1 Announce Type: new Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is t

HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding

arXiv:2602.12957v3 Announce Type: replace Abstract: Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extractio

HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation

arXiv:2606.29744v1 Announce Type: new Abstract: Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary

Hybrid Active-Online Learning Framework for Label-Efficient Concept Drift Adaptation in Optical Network Failure Detection

arXiv:2606.30322v1 Announce Type: new Abstract: We propose a hybrid active-online learning framework for label-efficient concept drift adaptation in optical network failure detection. Using margin-bas

IG-Lens: Exact Additive Probability Attribution Across Transformer Layers via Telescoping Integrated Gradients

arXiv:2606.29693v1 Announce Type: new Abstract: We ask a simple question about decoder-only transformers: between which two layers is the probability of a predicted token actually produced? Existing l

Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation

arXiv:2606.30054v1 Announce Type: new Abstract: The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, parti

Implementation of Hyperelastic Physics-Augmented Neural Networks in the Explicit Finite Element Codes Simcenter Radioss and OpenRadioss with Applications to Impact Events

arXiv:2606.29874v1 Announce Type: cross Abstract: Data-driven material modeling techniques have gained significant attention due to their ability to capture complex constitutive behaviors beyond the l

Improving Large-Scale Weakly Supervised ASR by Filtering and Selection

arXiv:2606.28728v1 Announce Type: cross Abstract: Leveraging large-scale weakly supervised datasets is crucial to train robust end-to-end automatic speech recognition (ASR) models. However, such datas

researcharxiv-cs-cl

IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion

arXiv:2606.28604v1 Announce Type: new Abstract: Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vi

Inexact calculus of variations on the hyperspherical tangent bundle with connections to the attention mechanism

arXiv:2507.15431v4 Announce Type: replace Abstract: We offer a theoretical mathematical background through Lagrangian optimization on the unit hyperspherical manifold and its tangential structure. Our