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BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation

arXiv:2605.20084v1 Announce Type: cross Abstract: Large language models (LLMs) can enhance factuality via retrieval-augmented generation (RAG), but applying RAG to every query is unnecessary when the

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Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction

DGX agent

arXiv:2506.17036v2 Announce Type: replace-cross Abstract: Modern industrial systems are often subject to multiple failure modes, and their conditions are monitored by multiple sensors, generating mult

researcharxiv-cs-lg

20 May 2026

Bilateral Teleoperation with Compliant 6-DOF Pose-and-Force Sensing

arXiv:2605.19255v1 Announce Type: new Abstract: Existing bilateral teleoperation platforms still rely on costly rigid six-axis force/torque sensors, tightly coupled leader-follower hardware, and kiloh

researcharxiv-cs-ro

Block-Based Double Decoders

arXiv:2605.18807v1 Announce Type: cross Abstract: Encoder-decoder models offer substantial inference-time savings over decoder-only models, but their pretraining objectives suffer from sparse supervis

Block-Sphere Vector Quantization

arXiv:2605.19972v1 Announce Type: cross Abstract: Vector quantization is a fundamental primitive for scalable machine learning systems, enabling memory-efficient storage, fast retrieval, and compresse

BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics

arXiv:2605.19324v1 Announce Type: new Abstract: Efficient neural network models that generate brain-like dynamic activity can be a valuable resource for generating synthetic data, analyzing difference

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models

arXiv:2605.19929v1 Announce Type: cross Abstract: Low-bit post-training quantization (PTQ) is a pivotal technique for deploying Vision-Language Models (VLMs) on resource-constrained devices. However,

CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations

arXiv:2605.19649v1 Announce Type: new Abstract: Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits appli

CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection

arXiv:2605.20032v1 Announce Type: new Abstract: Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. Howev

CaMo: Camera Motion Grounded Evaluation and Training for Vision-Language Models

arXiv:2605.20165v1 Announce Type: new Abstract: Vision-Language Models (VLMs) achieve strong performance on spatial question answering benchmarks, yet it remains unclear whether such gains reflect gen

Can LLMs Estimate Cognitive Complexity of Reading Comprehension Items?

arXiv:2510.25064v2 Announce Type: replace Abstract: Estimating the cognitive complexity of reading comprehension (RC) items is crucial for assessing item difficulty before it is administered to learne

CLIC: Contextual Language-Informed Cardiac Pathology Classification

arXiv:2605.19132v1 Announce Type: new Abstract: The electrocardiogram (ECG) is the gold standard for non-invasive diagnosis of cardiac pathologies and is a fundamental pillar of cardiovascular medicin

Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond

arXiv:2502.04575v3 Announce Type: replace-cross Abstract: Given an unnormalized probability density piproptoe^{-V}, estimating its normalizing constant Z=int_{R^d}e^{-V(x)}dx or free energy F=-log Z i

Component-Aware Structure-Preserving Style Transfer for Satellite Sim2Real 6D Pose Estimation

arXiv:2605.19624v1 Announce Type: cross Abstract: Monocular 6D pose estimation for non-cooperative satellites depends heavily on annotated training data, yet real satellite images with reliable pose l

Conformal Prediction via Transported Beta Laws

arXiv:2605.19024v1 Announce Type: cross Abstract: Split conformal prediction provides finite-sample marginal coverage under exchangeability, but this guarantee averages over the random calibration sam

Context-Aware Detection and Victim-Centered Response Generation for Online Harassment in Private Messaging

arXiv:2512.14700v2 Announce Type: replace-cross Abstract: Online harassment is a widespread social and public health concern, yet most computational approaches for detecting and addressing harassment

ContextRAG: Extraction-Free Hierarchical Graph Construction for Retrieval-Augmented Generation

arXiv:2605.19735v1 Announce Type: cross Abstract: Graph-structured retrieval-augmented generation (RAG) systems can improve answer quality on multi-hop questions, but many current systems rely on larg

Contextualized Visual Personalization in Vision-Language Models

arXiv:2602.03454v3 Announce Type: replace Abstract: Despite recent progress in vision-language models (VLMs), existing approaches often fail to generate personalized responses based on the user's spec

Convergence of Consensus-Based Particle Methods for Nonconvex Bi-Level Optimization

arXiv:2605.19667v1 Announce Type: cross Abstract: In this paper, we study a consensus-based optimization method for nonconvex bi-level optimization, where the objective is to minimize an upper-level f

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation

arXiv:2102.11840v2 Announce Type: replace Abstract: In recent years, artificial neural networks have developed into a powerful tool for addressing a multitude of problems for which classical solution

CosFly: Plan in the Matrix, Fly in the World

arXiv:2605.19120v1 Announce Type: new Abstract: We present CosFly, a box-structured planning and multimodal simulation pipeline for aerial tracking, together with CosFly-Track, a large-scale UAV datas

researcharxiv-cs-ro

Counterfactual Likelihood Tests for Indirect Influence in Private Reasoning Channels

arXiv:2605.19092v1 Announce Type: cross Abstract: Reasoning systems increasingly separate intermediate computation into private and public channels, creating evaluation cases that look similar in tran

CounterFlow: A Two-Phase Inference-Time Sampling for Counterfactual Video Foley Generation

arXiv:2605.18916v1 Announce Type: cross Abstract: We investigate Counterfactual Video Foley Generation, which aims to adopt a sound-source identity that contradicts the visual evidence while remaining

CPC-VAR:Continual Personalized and Compositional Generation in Visual Autoregressive Models

arXiv:2605.19750v1 Announce Type: new Abstract: Visual autoregressive (VAR) models have recently emerged as an efficient paradigm for text-to-image generation. Despite their strong generative capabili

Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications

arXiv:2605.19299v1 Announce Type: new Abstract: The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data charact

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

arXiv:2605.18897v1 Announce Type: cross Abstract: Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these

Cubit: Token Mixer with Kernel Ridge Regression

arXiv:2605.06501v2 Announce Type: replace-cross Abstract: Since its introduction in 2017, the Transformer has become one of the most widely adopted architectures in modern deep learning. Despite exten

D-Convexity: A Unified Differentiable Convex Shape Prior via Quasi-Concavity for Data-driven Image Segmentation

arXiv:2605.19210v1 Announce Type: new Abstract: Convexity is a fundamental geometric prior that underlies many natural and man-made structures, yet remains challenging to impose effectively in end-to-

D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting

arXiv:2605.18810v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffus

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning

arXiv:2605.18892v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative learning of computer vision models, where privacy and regulatory constraints prevent centralizing data a

Deep Learning-Assisted Improved Differential Fault Attacks on Lightweight Stream Ciphers

arXiv:2603.29382v2 Announce Type: replace-cross Abstract: Lightweight cryptographic primitives are widely deployed in resource-constrained environments, particularly in Internet of Things (IoT) device

Deep Neural Network for Musical Instrument Recognition using MFCCs

arXiv:2105.00933v3 Announce Type: replace-cross Abstract: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the

Deep Tech to Space: Space Data Centers and AI Revolution at the Edge

arXiv:2605.19892v1 Announce Type: cross Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding su

Delta Attention Residuals

arXiv:2605.18855v1 Announce Type: cross Abstract: Attention Residuals replace standard additive residual connections with learned softmax attention over previous layer outputs, enabling selective cros

Density-Ratio Losses for Post-Hoc Learning to Defer

arXiv:2605.19557v1 Announce Type: cross Abstract: We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation

arXiv:2602.00545v2 Announce Type: replace Abstract: The eigenvalue distribution of the Hessian matrix plays a crucial role in understanding the optimization landscape of deep neural networks. Prior wo

DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift

arXiv:2605.19231v1 Announce Type: new Abstract: We introduce DeRegiME -- Deep Regime Mixture of Experts -- a direct multi-horizon probabilistic forecaster that separates latent uncertainty regimes fro

Difficulty-Controllable Cloze Question Distractor Generation

arXiv:2511.01526v2 Announce Type: replace Abstract: Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors r

Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victims

arXiv:2509.12288v2 Announce Type: replace-cross Abstract: Domestic Violence (DV) is a pervasive public health problem characterized by patterns of coercive and abusive behavior within intimate relatio

Distribution Matching Distillation without Fake Score Network

arXiv:2605.19256v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) provides an effective distribution-level correction for few-step generation, while relying on an auxiliary fake

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

arXiv:2602.03139v2 Announce Type: replace Abstract: Distribution matching distillation (DMD) facilitates few-step image generation by aligning a distilled student with a reference multi-step teacher.

Do Better Volatility Forecasts Lead to Better Portfolios? Evidence from Graph Neural Networks

arXiv:2605.19278v1 Announce Type: cross Abstract: This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using

Does Your Wildfire Prediction Model Actually Work, or Just Score Well?

arXiv:2605.18911v1 Announce Type: cross Abstract: Wildfire prediction is important for early warning and resource allocation, yet existing Earth foundation models (Earth FMs) are pretrained for genera

Dr.LLM: Dynamic Layer Routing in LLMs

arXiv:2510.12773v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and i

ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation

arXiv:2602.04279v2 Announce Type: replace Abstract: Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) rem

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers

arXiv:2601.15014v2 Announce Type: replace-cross Abstract: We study in-context learning for nonparametric regression with alpha-Holder smooth regression functions, for some alpha>0. We prove that, with

Efficient Elicitation of Collective Disagreements

arXiv:2605.19521v1 Announce Type: new Abstract: We analyze the structure of the disagreement among a population of voters over a set of alternatives. Surveys typically ask either for pairwise comparis

Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention

arXiv:2605.19726v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) enable globally coherent, bidirectional, and controllable text generation, offering advantages over traditional autoreg