Search: “research”

FPTQuant: Function-Preserving Transforms for LLM Quantization

arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective

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Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

DGX agent

arXiv:2607.07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantizati

researcharxiv-cs-lg

9 Jul 2026

From Data Completeness to Data Sufficiency: A Task-Driven Imaging Framework for Intraoperative CBCT under Quality-Time-Dose Trade-offs

arXiv:2607.07039v1 Announce Type: cross Abstract: Mobile C-arm cone-beam computed tomography (CBCT) has been widely used for real-time intraoperative 3D imaging. However, current practice often mechan

G-PROBE: Cross-FOV Place Recognition and Certainty-Coupled Localization for 3D Point Clouds

arXiv:2607.06782v1 Announce Type: cross Abstract: Global localization from 3D point clouds remains challenging under limited or asymmetric fields of view (FOV), which fail to provide the dense, symmet

Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces

arXiv:2607.07032v1 Announce Type: new Abstract: Spectral positional encodings (PEs) for directed graphs face two obstacles: magnetic Laplacians require an O(n^3) Hermitian eigendecomposition per poten

Generating Personalized Lower-Limb Kinematics Across Walking Speeds Using Subject-Conditioned Diffusion

arXiv:2607.07533v1 Announce Type: new Abstract: Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks, yet collecting this data demands repeated motion capt

Geometric Collapse: When Vision Models Fail to Verify Physical Causality

arXiv:2607.06871v1 Announce Type: new Abstract: Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields infe

Geometry-Aware Single-Image 4D Synthesis via Dense Trajectory Generation

arXiv:2512.05044v2 Announce Type: replace Abstract: Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and recons

GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes

arXiv:2604.02915v2 Announce Type: replace Abstract: We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeli

Gradient-free Riemannian Langevin Sampler

arXiv:2607.07519v1 Announce Type: new Abstract: We address the problem of efficiently sampling multimodal probability distributions, where standard Markov Chain Monte Carlo methods often suffer from p

Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance

arXiv:2607.07665v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the standard way to strengthen class-conditioning in diffusion and flow-matching samplers, yet at large guidance it ov

Hardware-aware Graph Neural Networks prunning for embedded event-based vision

arXiv:2607.06739v1 Announce Type: new Abstract: Event-based cameras are gaining popularity as the sensor of choice for mobile robotics, due to their high performance in dynamic environments. However,

Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds

arXiv:2607.06696v1 Announce Type: cross Abstract: Weighted empirical measures on compact manifolds arise in importance sampling, particle approximations, posterior summaries, quadrature, and represent

Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation

arXiv:2607.07401v1 Announce Type: cross Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acq

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

arXiv:2607.07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidenc

How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization

arXiv:2607.07678v1 Announce Type: new Abstract: Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-

How Learning Dynamics Drive Adversarially Robust Generalization?

arXiv:2410.07719v4 Announce Type: replace Abstract: Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing emp

How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles

arXiv:2607.07114v1 Announce Type: cross Abstract: Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adv

HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization

arXiv:2607.07233v1 Announce Type: new Abstract: Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation

arXiv:2607.06972v1 Announce Type: new Abstract: Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing method

Hybrid Least Squares/Gradient Descent Methods for MIONets

arXiv:2607.06976v1 Announce Type: cross Abstract: In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes

Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation

arXiv:2607.07330v1 Announce Type: cross Abstract: Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored.

Immersive Social Interaction with VR and LLM-Assisted Humanoids

arXiv:2607.07430v1 Announce Type: new Abstract: Humanoid robots can extend human presence to remote, constrained, or hazardous environments, but existing teleoperation interfaces often require physica

Intrinsic Green's Learning: Supervised Learning on Manifolds via Inverse PDE

arXiv:2607.07034v1 Announce Type: cross Abstract: We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a manifold as the solution to a linear PDE whose source te

Is Randomness Necessary for Adaptive Data Analysis?

arXiv:2607.07085v1 Announce Type: cross Abstract: The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused. F

Large Behavior Model: A Promptable Digital Twin of the Retail Customer

arXiv:2607.06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy withou

Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607.07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frame

Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification

arXiv:2607.07518v1 Announce Type: new Abstract: Deformable shape representations have proven to be robust complements to texture features in cardiac image classification, offering geometric priors tha

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain

Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes: A Deep Logical Learning Approach using Graph Tsetlin Machines

arXiv:2607.06719v1 Announce Type: cross Abstract: This paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed mo

MMDiff: Extending Diffusion Transformers for Multi-Modal Generation

arXiv:2606.16673v2 Announce Type: replace Abstract: Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising

Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

arXiv:2510.25370v2 Announce Type: replace Abstract: Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Commu

researcharxiv-cs-cl

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

arXiv:2607.07573v1 Announce Type: new Abstract: Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual ste

Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders

arXiv:2607.07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot

Network Dynamics-Based Framework for Understanding Deep Neural Networks

arXiv:2501.02436v5 Announce Type: replace Abstract: Advancements in artificial intelligence call for a deeper understanding of the fundamental mechanisms underlying deep learning. In this work, we pro

Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization

arXiv:2607.07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-co

NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction

arXiv:2607.07168v1 Announce Type: new Abstract: Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance

Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

arXiv:2603.23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known envi

On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces

arXiv:2607.07375v1 Announce Type: cross Abstract: Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, inp

Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers

arXiv:2607.06637v1 Announce Type: new Abstract: In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our metho

PeTeR: Post-Training Robustification of Probabilistic Circuits

arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However,

Physically Grounded Monocular Depth via Nanophotonic Wavefront Encoding

arXiv:2503.15770v3 Announce Type: replace-cross Abstract: Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to a

Physics-guided spatiotemporal neural models for fuel density prediction

arXiv:2607.06999v1 Announce Type: cross Abstract: This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowled

Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

arXiv:2607.06585v1 Announce Type: new Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in econ

PLED-VINS: A Point-Line Event-Based Visual Inertial SLAM for Dynamic Environments

arXiv:2607.07374v1 Announce Type: new Abstract: Dynamic environments remain a fundamental challenge for visual SLAM, where unreliable observations from moving objects and rapid motion degrade state es

Practicing with Language Models Cultivates Human Empathic Communication

arXiv:2603.15245v2 Announce Type: replace Abstract: Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) ge

researcharxiv-cs-cl

Prior-matched evaluation of operational Earth-observation classifiers: a three-number reporting method demonstrated on Sentinel-1 internal-wave detection

arXiv:2607.07146v1 Announce Type: cross Abstract: The Internal Waves Service screens the Sentinel-1 Wave-mode archive for internal solitary waves, routing detections to experts whose adjudication time

ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities

arXiv:2607.06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or