Search: “research”

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

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Graph Transfer Learning via Shared Latent Geometry: Theory and Applications

DGX agent

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

researcharxiv-cs-lg

2 Jun 2026

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.

HyperVQ: Enabling Hyperprior Entropy Modeling for VQ-Based Generative Image Compression

arXiv:2512.07192v2 Announce Type: replace Abstract: Vector Quantization (VQ) based generative image compression has achieved remarkable perceptual quality. However, existing VQ codecs suffer from two

IDEAFix: Evaluation Framework for Creative Defixation Prompting in LLMs

arXiv:2606.00875v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for tasks involving creative problem solving and idea generation. However, there is a lack of consens

researcharxiv-cs-cl

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

arXiv:2503.07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for

Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families

arXiv:2606.02231v1 Announce Type: cross Abstract: Temporal systems often exhibit non-stationary behaviour, such as seasonal climate variation or glucose fluctuations in patients with type-1 diabetes.

IDLM: Inverse-distilled Diffusion Language Models

arXiv:2602.19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation. However, their multi-step sampling leads to slow in

iLRM: An Iterative Large 3D Reconstruction Model

arXiv:2507.23277v3 Announce Type: replace Abstract: Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explic

Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Images

arXiv:2606.00872v1 Announce Type: new Abstract: AI-generated image detection is a moving-target problem: detectors trained on one generator often fail when a new generator appears, and only a few labe

Improved Belief-Attention in Vision Task

arXiv:2606.00077v1 Announce Type: cross Abstract: Recently, Belief-Attention ite{Guoqiang25BeliefAttention} has been proposed by first performing an orthogonal projection of the softmax-based weighted

Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation

arXiv:2606.02532v1 Announce Type: new Abstract: Analyzing microstructural defects in transmission electron microscopy (TEM) images, particularly in irradiated metal alloys, is often limited by the ava

Improving IoT Intrusion Detection Through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data

arXiv:2606.00161v1 Announce Type: cross Abstract: The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the big

Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting

arXiv:2606.00556v1 Announce Type: new Abstract: Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. I

Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference

arXiv:2606.01711v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computation

IMWM: Intuition Models Complement World Models for Latent Planning

arXiv:2606.01626v1 Announce Type: new Abstract: Planning with a learned latent world model is a promising route to control from raw pixels, but a strong world model alone is not enough. We show this e

In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise

arXiv:2606.00520v1 Announce Type: cross Abstract: Many stochastic gradient methods are believed not to converge when the noise in stochastic gradients has only a finite p-th moment for pinleft(1,2righ

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

arXiv:2606.00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- alg

Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior

arXiv:2606.02453v1 Announce Type: cross Abstract: Despite the remarkable fidelity of generative models, they frequently suffer from mode collapse. Existing strategies for enhancing diversity predomina

Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms

arXiv:2606.00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks. Many downstrea

Intercepting the Future: Latent-Space Predictive World Model for Dynamic VLA Manipulation

arXiv:2606.02486v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models generalize across static manipulation but fail when objects move during task execution. They map the current observa

researcharxiv-cs-ro

Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data

arXiv:2503.22939v4 Announce Type: replace Abstract: The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational mod

Interventional Processes for Causal Uncertainty Quantification

arXiv:2410.14483v3 Announce Type: replace-cross Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an e

Is Zero-Shot Super-Resolution Possible in Operator Learning?

arXiv:2606.00296v1 Announce Type: cross Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate pre

It does what it says on the tin: safe synthetic data from coarsened margins

arXiv:2606.02101v1 Announce Type: cross Abstract: This paper proposes a method of creating synthetic data (SD) that will have two important advantages for the user compared to other methods currently

Iterated Population Based Training with Task-Agnostic Restarts

arXiv:2511.09190v2 Announce Type: replace Abstract: Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based T

KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning

arXiv:2606.00532v1 Announce Type: new Abstract: Context engineering can improve large language models without updating their weights, but mathematical reasoning exposes a key limitation: feedback accu

LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis

arXiv:2603.20176v3 Announce Type: replace Abstract: Recent work has shown that neural networks can perform 3D tasks such as Novel View Synthesis (NVS) without explicit 3D reconstruction. Even so, we a

LastAct: Trajectory-Guided Latest-Activity Localization for Real-Time Smart-Home Activity Recognition

arXiv:2606.00260v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deploy

Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs

arXiv:2606.00726v1 Announce Type: new Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation. Existing methods of