Search: “research”

From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data

arXiv:2606.02268v1 Announce Type: new Abstract: Geometric analysis fundamentally distinguishes between extit{extrinsic} and extit{intrinsic} perspectives. The dominant paradigm in current 3D represent

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From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization

DGX agent

arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-rang

researcharxiv-cs-cl

2 Jun 2026

From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

arXiv:2606.02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted

From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems

arXiv:2606.01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct o

From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning

arXiv:2606.01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline

From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection

arXiv:2602.03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes al

From Zero to Hero: Training-Free Custom Concept Spawning in World Models

arXiv:2606.02575v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated envir

Fundamental bounds on efficiency-confidence trade-off for transductive conformal prediction

arXiv:2509.04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is

GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning

arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales,

researcharxiv-cs-cl

GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

arXiv:2606.02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images

General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling

arXiv:2606.00110v1 Announce Type: new Abstract: Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordi

Generalization Limits in Vehicle Re-Identification

arXiv:2606.01981v1 Announce Type: new Abstract: Vehicle re-identification focuses on retrieving images of the same vehicle from a gallery given a query image. Upon closer inspection of commonly used d

Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime

arXiv:2510.06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low trai

Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry

arXiv:2511.01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density p by the best match q in a family of tractable distributions. The best variational ap

Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation

arXiv:2606.00514v1 Announce Type: cross Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distribu

Generative AI and Sales Productivity: Field Experiments in Online Retail

arXiv:2510.12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomize

Geodesic Flow Matching for Denoising High-Dimensional Structured Representations

arXiv:2606.00248v1 Announce Type: new Abstract: Vector Symbolic Algebras (VSAs) enable robust neurosymbolic reasoning by encoding symbolic information into high-dimensional distributed representations

Geodesics with Unified Tangent-constrained Priors and Curvature Regularization

arXiv:2606.00139v1 Announce Type: cross Abstract: Curvature-penalized geodesic models have proven their effectiveness in image segmentation by computing globally optimal curves. Unfortunately, these m

Geometric Latent Reasoning Induces Shorter Generations in LLMs

arXiv:2606.02248v1 Announce Type: new Abstract: Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive,

researcharxiv-cs-cl

GeoSAM-3D: Geodesic Prompt Propagation for Open-Vocabulary 3D Scene Segmentation from Monocular Video

arXiv:2606.00447v1 Announce Type: cross Abstract: Open-vocabulary 3D scene segmentation usually assumes RGB-D video, calibrated multi-view imagery, or a reconstructed mesh. GeoSAM-3D studies a lighter

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

arXiv:2606.01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges

Global Convergence of a Line-Search Filter Differential Dynamic Programming Method

arXiv:2606.01487v1 Announce Type: cross Abstract: In this article, we establish the global convergence properties of the FilterDDP algorithm, which extends the discrete-time differential dynamic progr

researcharxiv-cs-ro

Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

arXiv:2505.10882v2 Announce Type: replace Abstract: Principal component analysis classically requires full d-dimensional samples, yet in various applications hardware limits acquisition to a few scala

Global Geometry Is Not Enough for Vision Representations

arXiv:2602.03282v2 Announce Type: replace-cross Abstract: A common assumption in representation learning is that globally well-distributed embeddings support robust and generalizable representations.

GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction

arXiv:2606.02498v1 Announce Type: new Abstract: This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We p

GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation

arXiv:2606.01412v1 Announce Type: new Abstract: Post-training quantization is widely used for compressing large neural networks, but aggressive low-bit quantization can significantly degrade model qua

GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework

arXiv:2504.17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighborin

Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning

arXiv:2508.06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structure

Graph Transfer Learning via Shared Latent Geometry: Theory and Applications

arXiv:2606.00716v1 Announce Type: new Abstract: Inference and control in engineered physical systems pay a heavy physics cost at deployment: state estimators, inverse-problem solvers, model-predictive

Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG

arXiv:2606.00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large lang

GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning

arXiv:2601.22651v2 Announce Type: replace-cross Abstract: Training-data attribution for vision generative models aims to identify which training data influenced a given output. While most methods scor

Hallucination-Aware Diffusion Sampling for Inverse Problems via Robust Prior Updates

arXiv:2606.02331v1 Announce Type: new Abstract: Diffusion-based inverse problem solvers can produce realistic reconstructions, but realism alone does not ensure that the recovered details are supporte

(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction

arXiv:2606.00349v1 Announce Type: cross Abstract: Reconstructing spatiotemporal fields from partial observations is fundamental to scientific inference, from inferring atmospheric states from satellit

Head-Pose-Aware Visual Speech Recognition with FiLM Modulation

arXiv:2606.00751v1 Announce Type: new Abstract: Visual Speech Recognition (VSR) aims to recognize speech from visual cues such as lip movements, but its performance is fundamentally limited by viseme

Hidden Thoughts Are Not Secret: Reasoning Trace Exposure in LLMs

arXiv:2606.00642v1 Announce Type: new Abstract: Reasoning traces have become a valuable form of learning signals for improving and transferring the capabilities of large language models. In particular

HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings

arXiv:2502.15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Busines

Highly Deformable Proprioceptive Membrane for Real-Time 3D Shape Reconstruction

arXiv:2601.13574v2 Announce Type: replace Abstract: Reconstructing the three-dimensional (3D) geometry of object surfaces is essential for robot perception, yet vision-based approaches degrade under l

researcharxiv-cs-ro

HiGS: A Hierarchical Rendering Architecture for Real-Time 3D Gaussian Splatting

arXiv:2606.00352v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has become the standard for real-time novel view synthesis on commodity GPUs. Its pipeline ties spatial partitioning and ra

Hoeffding Concept Bottleneck Models with Applications to Overhead Images

arXiv:2606.00082v1 Announce Type: cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions. Concept bottleneck models (CBM) hav

Honey, I Shrunk the Arc de Triomphe!

arXiv:2606.02379v1 Announce Type: new Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from

How Accurately Can a Gaussian Approximate Stochastic Approximation Iterates?

arXiv:2602.13906v2 Announce Type: replace-cross Abstract: Stochastic approximation (SA) is a method for finding the root of an operator perturbed by noise. The focus of this paper is studying the dist

How (and when) can you fit examples to logic-based hypothesis classes over infinite structures?

arXiv:2606.01107v1 Announce Type: cross Abstract: We study fitting problems, sometimes called ``training problems'', where we have a finite sample consisting of inputs and outputs, and we want to know

How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings

arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary

researcharxiv-cs-cl

How Much Orthogonalization Does Muon Need?

arXiv:2606.00371v1 Announce Type: new Abstract: Muon optimizers improve neural-network training by replacing ill-conditioned momentum updates with approximately semi-orthogonal updates. This motivates

How Neural Losses Shape VAE Latents

arXiv:2606.00635v1 Announce Type: new Abstract: Modern VAEs are rarely trained with the pointwise likelihood implied by the standard eta-VAE objective. In practice, pointwise reconstruction is often c

HumanNOVA: Photorealistic, Universal and Rapid 3D Human Avatar Modeling from a Single Image

arXiv:2606.02573v1 Announce Type: new Abstract: In this paper, we present HumanNOVA, a photorealistic, universal, and rapid model for generating 3D human avatars from a single RGB image. Achieving bot

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing

arXiv:2606.00834v1 Announce Type: cross Abstract: Accurate malaria forecasting remains a major challenge in sub-Saharan Africa, where strong seasonality, reporting uncertainty, and non-stationary tran

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models

arXiv:2606.00275v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks through scaled architectures and extensive training.