Search: “research”

SDTalk: Structured Facial Priors and Dual-Branch Motion Fields for Generalizable Gaussian Talking Head Synthesis

arXiv:2605.09956v1 Announce Type: cross Abstract: High-quality, real-time talking head synthesis remains a fundamental challenge in computer vision. Existing reconstruction- and rendering-based method

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Research

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

DGX agent

arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia

researcharxiv-cs-ai

12 May 2026

SeasonScapes: Learning Large-scale Re-lightable 3D Landscapes with Seasonal Variation from Sparse Webcams

arXiv:2605.09039v1 Announce Type: new Abstract: We introduce SeasonScapes framework and a the SeasonScapes dataset: Swiss Sparse-view Mountain Scenes with Seasonal Changes that covers over 50 km x 60

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge

arXiv:2407.11906v3 Announce Type: replace Abstract: Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes,

SEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations

arXiv:2602.04054v2 Announce Type: replace-cross Abstract: Understanding how neural representations respond to geometric transformations is essential for evaluating whether learned features preserve me

Self-Attention as a Covariance Readout: A Unified View of In-Context Learning and Repetition

arXiv:2605.10466v1 Announce Type: new Abstract: Large language models (LLMs) exhibit two striking and ostensibly unrelated behaviours: in-context learning (ICL) and repetitive generation. In both, the

Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models

arXiv:2605.08145v1 Announce Type: cross Abstract: Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues ca

Semantic Voting: Execution-Grounded Consensus for LLM Code Generation

arXiv:2605.08680v1 Announce Type: cross Abstract: LLM code-generation pipelines often sample multiple candidates and select one final answer without access to a complete oracle. Existing pipelines mix

Sequential Membership Inference Attacks

arXiv:2602.16596v2 Announce Type: replace Abstract: Modern AI models are not static. They go through multiple updates in their lifecycles. We propose to design Sequential Membership Inference (SeMI) a

Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI

arXiv:2605.10571v1 Announce Type: cross Abstract: Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue characterization but relies on robust motion correction w

Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks

arXiv:2605.10395v1 Announce Type: cross Abstract: We study the information-theoretic limits of learning a one-hidden-layer teacher network with hierarchical features from noisy queries, in the context

ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding

arXiv:2605.08482v1 Announce Type: cross Abstract: Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and i

Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards

arXiv:2605.10313v1 Announce Type: new Abstract: We study contextual bandits with nonlinear and path-dependent rewards through a novel signature-transform-based approach. Leveraging the universal nonli

SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization

arXiv:2605.08809v1 Announce Type: cross Abstract: Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddin

SLayerGen: a Crystal Generative Model for all Space and Layer Groups

arXiv:2605.08262v1 Announce Type: cross Abstract: Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as

Sliced Inner Product Gromov-Wasserstein Distances

arXiv:2605.08546v1 Announce Type: cross Abstract: The Gromov-Wasserstein (GW) problem provides a framework for aligning heterogeneous datasets by matching their intrinsic geometry, but its statistical

Sliding Window Informative Canonical Correlation Analysis

arXiv:2507.17921v2 Announce Type: replace-cross Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a

SlimQwen: Exploring the Pruning and Distillation in Large MoE Model Pre-training

arXiv:2605.08738v1 Announce Type: cross Abstract: Structured pruning and knowledge distillation (KD) are typical techniques for compressing large language models, but it remains unclear how they shoul

SlimSpec: Low-Rank Draft LM-Head for Accelerated Speculative Decoding

arXiv:2605.10453v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model

Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities

arXiv:2605.10029v1 Announce Type: new Abstract: Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global c

SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization

arXiv:2601.22131v2 Announce Type: replace Abstract: Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the bud

Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs

arXiv:2605.09073v1 Announce Type: new Abstract: Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate

researcharxiv-cs-ro

Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts

arXiv:2605.08230v1 Announce Type: new Abstract: Background: Fentanyl overdose deaths are still increasing across the U.S. We do not fully understand which county-level social and structural conditions

SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs

arXiv:2605.09449v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have made remarkable progress in visual understanding and language-based reasoning, yet they lack a pers

Sparse Layers are Critical to Scaling Looped Language Models

arXiv:2605.09165v1 Announce Type: cross Abstract: Looped language models repeat a set of transformer layers through depth, reducing memory costs and providing natural early-exit points at loop boundar

Spatial-Frequency Gated Swin Transformer for Remote Sensing Single-Image Super-Resolution

arXiv:2605.09687v1 Announce Type: new Abstract: Remote Sensing (RS) single-image super-resolution aims to reconstruct high-resolution imagery from low-resolution observations while preserving fine spa

Spectral Condition for muP under Width-Depth Scaling

arXiv:2603.00541v2 Announce Type: replace Abstract: Generative foundation models are increasingly scaled in both width and depth, posing significant challenges for stable feature learning and reliable

Spectrally-Guided Diffusion Noise Schedules

arXiv:2603.19222v2 Announce Type: replace Abstract: Denoising diffusion models are widely used for high-quality image and video generation. Their performance depends on noise schedules, which define t

Speech-based Psychological Crisis Assessment using LLMs

arXiv:2605.10027v1 Announce Type: cross Abstract: Psychological support hotlines provide critical support for individuals experiencing mental health emergencies, yet current assessments largely rely o

Spherical Flows for Sampling Categorical Data

arXiv:2605.05629v2 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically

Split CNN Inference on Networked Microcontrollers

arXiv:2605.09357v1 Announce Type: cross Abstract: Running deep neural networks on microcontroller units (MCUs) is severely constrained by limited memory resources. While TinyML techniques reduce model

SSA: Improving Performance With a Better Scoring Function

arXiv:2508.14685v4 Announce Type: replace Abstract: While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We anal

Stability of the Monge Map in Semi-Dual Optimal Transport

arXiv:2605.05569v2 Announce Type: replace-cross Abstract: This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numeric

Statistical Inference and Quality Measures of KV Cache Quantisations Inspired by TurboQuant

arXiv:2605.08114v1 Announce Type: new Abstract: We analyse three KV cache quantization schemes under a fair bit budget: extbf{KV} (scalar MSE baseline), extbf{KQV} (WHT + MSE on K; WHT + MSE + QJL on

Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness

arXiv:2503.09336v4 Announce Type: replace Abstract: Backdoor attacks pose a severe threat to deep neural networks (DNNs) by implanting hidden backdoors that can be activated with predefined triggers t

Stellar Age Compression Reshapes Interpretations of the Milky Way Thick-Disk Formation History

arXiv:2605.10220v1 Announce Type: cross Abstract: The formation timescale of the Milky Way thick disk is one of the central debates in Galactic archaeology. The age-metallicity relation (AMR), formati

Streaming Sliced Optimal Transport

arXiv:2505.06835v4 Announce Type: replace Abstract: Sliced optimal transport (SOT), or sliced Wasserstein (SW) distance, is widely recognized for its statistical and computational scalability. In this

Structure-Centric Graph Foundation Model via Geometric Bases

arXiv:2605.08689v1 Announce Type: cross Abstract: Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible nod

Structured Recurrent Mixers for Massively Parallelized Sequence Generation

arXiv:2605.08696v1 Announce Type: new Abstract: Over the last two decades, language modeling has experienced a shift from predominantly recurrent architectures that process tokens sequentially during

Supersampling Stable Diffusion and More: An Approach for Interpolating Neural Networks Using Common Interpolation Methods

arXiv:2605.08698v1 Announce Type: new Abstract: Stable Diffusion (SD) has evolved DDPM (Denoising Diffusion Probabilistic Model) based image generation significantly by denoising in latent space inste

Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look

arXiv:2604.03928v2 Announce Type: replace-cross Abstract: Frozen pretrained image representations are widely used for transfer learning: a backbone is kept fixed, feature vectors are extracted, and a

Supervised Guidance Training for Infinite-Dimensional Diffusion Models

arXiv:2601.20756v2 Announce Type: replace Abstract: Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from pa

SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

arXiv:2605.08835v1 Announce Type: new Abstract: The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low ta

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM

arXiv:2605.09536v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy-parallelism tra

TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network

arXiv:2506.01352v2 Announce Type: replace Abstract: Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participant

TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers

arXiv:2605.08440v1 Announce Type: cross Abstract: Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservatio

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

arXiv:2605.10165v1 Announce Type: cross Abstract: Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grou

Task-Aware Calibration: Provably Optimal Decoding in LLMs

arXiv:2605.10202v1 Announce Type: cross Abstract: LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating