Search: “research”

RCStat: A Statistical Framework for using Relative Contextualization in Transformers

arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a

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RDM: Recurrent Diffusion Model for Human Motion Generation

DGX agent

arXiv:2406.07169v3 Announce Type: replace Abstract: Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion method

researcharxiv-cs-cv

14 Oct 2026

Readout Scaling Shapes Sparse Feature Recovery

arXiv:2610.18462v1 Announce Type: new Abstract: Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data. We use readout-canonicalized decisio

Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits

arXiv:2610.18488v1 Announce Type: new Abstract: Many related prediction problems, which we call questions, come with few labels each. What information can be shared across them, and what is actually n

Recovering from Pose-Selection Failures in Repeated Visual Localization

arXiv:2610.16409v1 Announce Type: new Abstract: Repetitive structures can give an incorrect pose candidate more inlier support than an accurate one, causing visual localization to select it. History-G

ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing

arXiv:2610.18388v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when t

Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data

arXiv:2610.14205v1 Announce Type: new Abstract: Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient

Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling

arXiv:2610.16248v1 Announce Type: new Abstract: We study sampling from pi(x)proptoe^{-V(x)} on R^d, where V is alpha-strongly convex and eta-smooth with condition number kappa=eta/alpha. For this prob

Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts

arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions

Region Aware Squeeze-and-Excitation Networks for Image Segmentation

arXiv:2610.15079v1 Announce Type: new Abstract: Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial s

RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning

arXiv:2610.17623v1 Announce Type: new Abstract: Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the ru

Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling

arXiv:2610.18292v1 Announce Type: new Abstract: Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that

Removing the Compute Bottleneck for Event-based Visual Place Recognition with Spiking Neural Networks

arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe

Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning

arXiv:2610.16427v1 Announce Type: new Abstract: Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and

Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning

arXiv:2610.16347v1 Announce Type: new Abstract: Forward-Forward learning replaces end-to-end backpropagation with local, layer-wise training objectives. Its variants are evaluated primarily by classif

Revisiting Error Feedback for Model Quantization

arXiv:2610.18139v1 Announce Type: new Abstract: We study causal error-feedback coefficients as design variables for model quantization. Modeling instantaneous quantization errors as independent, zero-

RoboFake: Are Deepfake Detectors Ready for Humanoid Face Robots?

arXiv:2610.14269v1 Announce Type: new Abstract: Humanoid face robots can now move their lips in sync with speech and show detailed facial expressions. Give such a robot the face of a real person and d

Robust Marathi Sentiment Analysis using Domain-Adaptive Pre-training and Data Augmentation Approaches

arXiv:2610.15761v1 Announce Type: cross Abstract: For languages with limited resources, Sentiment Analysis remains a challenging problem due to limited annotated data and strong domain variability acr

Rotate the Loop: Blockwise Conformality for Looped Transformers

arXiv:2610.14030v1 Announce Type: new Abstract: Looped transformers reuse one shared block across many recurrent steps, so depth becomes a quantity that can be spent at runtime rather than a stack of

RSAJEV: Training-Free Abstaining Prototype Memory for Eliminating Silent Hallucination in JEV-Based Remote Sensing Classification

arXiv:2610.15742v1 Announce Type: new Abstract: Scene classification of remote sensing imagery with multimodal large language models (MLLMs) is formulated as a single-forward-pass decision problem: ca

RTISSLAM: In-air Imaging Sonar SLAM using a Robust Pose Graph and Occupancy Grid Mapping

arXiv:2610.18479v1 Announce Type: new Abstract: Sonar was one of the first sensors with which mobile robots built maps, and in-air sonar keeps working where cameras and lidar fail, in darkness, smoke,

researcharxiv-cs-ro

S^3-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors

arXiv:2610.18458v1 Announce Type: new Abstract: Modern image restoration methods increasingly rely on large-scale supervised training, yet clean targets are often prohibitively difficult to obtain in

SAFE-KV: Causal Suffix Repair for Behavior-Preserving Non-Prefix KV Cache Reuse in RAG

arXiv:2610.15186v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reuses the same documents across many requests, making non-prefix KV-cache reuse attractive. Yet a cached documen

Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks

arXiv:2610.17998v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when lear

Same Goal, Different Words: Spatial Instruction Sensitivity in World-Action Models

arXiv:2610.16242v1 Announce Type: new Abstract: World-action models should preserve the intended manipulation across equivalent descriptions of a spatial goal. We evaluate this consistency in 864 roll

researcharxiv-cs-ro

Sample-Optimal Multi-Group Learning with an ERM Oracle

arXiv:2610.15916v1 Announce Type: new Abstract: In multi-group learning, a single predictor must perform nearly as well as the best hypothesis in a class on every group in a rich, possibly infinite an

Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling

arXiv:2610.16820v1 Announce Type: cross Abstract: In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets b

Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening

arXiv:2601.21590v2 Announce Type: replace Abstract: Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growin

Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation

arXiv:2409.12333v2 Announce Type: replace-cross Abstract: Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-inde

Scaling Laws for Multimodal Data Mixtures

arXiv:2610.14423v1 Announce Type: cross Abstract: Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the sci

Scaling Online Optimal Transport Minibatches for Flow Matching

arXiv:2610.18838v1 Announce Type: cross Abstract: A central challenge in scaling flow models is maintaining stability in training and integration. In flow matching, this stability depends critically o

Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression

arXiv:2610.18039v1 Announce Type: new Abstract: Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions. Training-free token reduction metho

ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map

arXiv:2610.14866v1 Announce Type: new Abstract: Cross-modal Thermal Geo-localization (TG) provides an illumination-robust positioning solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation

Screening Collapse under Recursive Synthetic Data

arXiv:2610.15617v1 Announce Type: cross Abstract: Recursive synthetic-data training can degrade a fitted model, but its effects on the variable-screening stage that precedes model fitting are less und

Sea-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification

arXiv:2610.17939v1 Announce Type: cross Abstract: Traditional time-series classification methods often operate as black boxes, providing a class prediction without indicating which time point or time

SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning

arXiv:2610.16104v1 Announce Type: new Abstract: Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from mu

Self-improving data synthesis can scale embedding models across domains and tasks

arXiv:2610.14730v1 Announce Type: cross Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual dat

Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

arXiv:1307.2965v3 Announce Type: replace Abstract: The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilag

Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps

arXiv:2610.15467v1 Announce Type: new Abstract: Image-computable low-level image saliency has shaped perceptual psychology and computer science, but is limited by its inability to capture human high-l

Semantically Steerable Image Tokenization

arXiv:2610.15515v1 Announce Type: new Abstract: Adaptive image tokenizers compress an image into a variable number of tokens matched to its complexity, and recent work makes this allocation controllab

SemTree: Judge-Aware Semantic Decision Trees for Inspectable and Repairable Rule Decisions

arXiv:2610.13999v1 Announce Type: new Abstract: Rule decisions such as content moderation, advertising compliance, and eligibility checks combine several conditions, exemptions, and missing-informatio

Sensor-Aware Joint Data Fusion and State Estimation under Sparse Multi-rate Distribution Grid Sensing

arXiv:2610.15156v1 Announce Type: new Abstract: Distribution-system state estimation is a growing necessity for the modern power grid as the addition of local storage and generation add system complex

Sequence Composition in EHR Foundation Model Pretraining

arXiv:2610.16878v1 Announce Type: new Abstract: Autoregressive electronic health record (EHR) foundation models predict the next event token in patient trajectories: sequences of structured medical ev

SERQ: Saliency-Aware Low-Rank Error Reconstruction for LLM Quantization

arXiv:2603.08185v2 Announce Type: replace Abstract: Post-training quantization (PTQ) has emerged as a prevailing technique for deploying large language models (LLMs) efficiently in terms of both memor

Shared-Control Teleoperation of Omnidirectional Aerial Robots for Continuous Valve Rotation

arXiv:2610.14478v1 Announce Type: new Abstract: Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although

researcharxiv-cs-ro

SharedSAE: a unified sparse dictionary across u-net blocks and denoising time for diffusion models

arXiv:2610.13997v1 Announce Type: new Abstract: Concept representations in diffusion models drift across network depth and denoising time, yet existing sparse autoencoders offer only per-block or per-

Sharper Frames, Worse Rooms: Viewpoint Coverage Dominates Image Quality in Feed-Forward 3D Reconstruction

arXiv:2610.15854v1 Announce Type: new Abstract: Grounded spatial reasoning about indoor scenes rests on a geometric reconstruction that the multimodal system does not itself produce. Feed-forward mult

Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection

arXiv:2507.23449v3 Announce Type: replace Abstract: One-class learning is an effective methodology for time series anomaly detection, yet kernel one-class approaches often compare sequences in a point