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Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation

arXiv:2512.10275v2 Announce Type: replace Abstract: Adversarial distillation in the standard min-max adversarial training framework aims to transfer adversarial robustness from a large, robust teacher

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Sampling-Based Control via Entropy-Regularized Optimal Transport

DGX agent

arXiv:2605.02147v1 Announce Type: new Abstract: Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where d

researcharxiv-cs-ro

5 May 2026

Scaling Sequence-to-Sequence Generative Neural Rendering

arXiv:2510.04236v3 Announce Type: replace Abstract: We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on

SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets

arXiv:2605.00944v1 Announce Type: cross Abstract: Sample-level rankings are increasingly used in data-centric NLP for analysis, filtering, debugging, and curation, yet existing pipelines typically sco

ScribbleEdit: Synthetic Data for Image Editing with Scribbles and Text

arXiv:2605.01135v1 Announce Type: new Abstract: Recent progress in generative models has significantly advanced image editing capabilities, yet precise and intuitive user control remains difficult. Sp

Selective Attention-Based Network for Robust Infrared Small Target Detection

arXiv:2605.00886v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) plays a pivotal role in a broad spectrum of mission-critical applications, including maritime surveillance, mili

Selective Prediction from Agreement: A Lipschitz-Consistent Version Space Approach

arXiv:2605.02611v1 Announce Type: new Abstract: We consider selective classification with abstention in the fixed-pool (or transductive) setting, where the unlabeled pool is given beforehand and only

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression

arXiv:2605.01628v1 Announce Type: cross Abstract: Self-normalized martingale inequalities lie at the heart of confidence ellipsoids for online least squares and, more broadly, many bandit and reinforc

Self-Supervised Spatial And Zero-Shot Angular Super-Resolution by Spatial-Angular Implicit Representation For Rotating-View SNR-Efficient Diffusion MRI

arXiv:2605.02575v1 Announce Type: new Abstract: Rotating-view thick-slice acquisition is highly SNR-efficient for mesoscale diffusion MRI (dMRI) but requires numerous rotating views to satisfy Nyquist

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference

arXiv:2511.08303v2 Announce Type: replace-cross Abstract: This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. I

Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models

arXiv:2605.01317v1 Announce Type: new Abstract: This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews. Using a dataset of 10,00

Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks

arXiv:2605.01383v1 Announce Type: new Abstract: Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and phys

Shared Autonomy Assisted by Impedance-Driven Anisotropic Guidance Field

arXiv:2605.02410v1 Announce Type: new Abstract: Shared autonomy (SA) enables robots to infer human intent and assist in its achievement. While most research focuses on improving intent inference, it o

SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis

arXiv:2603.21783v2 Announce Type: replace Abstract: Text-to-image generation powered by Diffusion Transformers (DiTs) has made remarkable strides, yet remote sensing (RS) synthesis lags behind due to

ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization

arXiv:2605.01866v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standa

Short window attention enables long-term memorization

arXiv:2509.24552v3 Announce Type: replace Abstract: Recent works show that hybrid architectures combining local sliding window attention layers and global attention layers outperform either of these a

SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training

arXiv:2605.02737v1 Announce Type: new Abstract: Synthetic training has recently advanced brain MRI segmentation by enabling contrast-agnostic models trained entirely on generated data. However, most e

SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition

arXiv:2605.02094v1 Announce Type: new Abstract: Subtle hand differences make sign language recognition challenging, yet many existing methods rely on encoders pretrained on generic action datasets tha

SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids

arXiv:2605.01427v1 Announce Type: new Abstract: Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilitie

Skipping the Zeros in Diffusion Models for Sparse Data Generation

arXiv:2605.01817v1 Announce Type: new Abstract: Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the

Space Network of Experts: Architecture and Expert Placement

arXiv:2605.00515v1 Announce Type: cross Abstract: Leveraging continuous solar energy harvesting at high efficiency, space data centers are envisioned as a promising platform for executing energy-inten

researcharxiv-cs-ai

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion

arXiv:2604.07421v2 Announce Type: replace Abstract: Full-waveform inversion (FWI) is pivotal for reconstructing high-resolution subsurface velocity models but remains computationally intensive and ill

Sparse Representation Learning for Vessels

arXiv:2605.01382v1 Announce Type: new Abstract: Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often

SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging

arXiv:2605.00887v1 Announce Type: new Abstract: We propose SparseContrast, a new framework that merges dynamic sparse attention with contrastive learning for medical imaging, with a focus on chest X-r

Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models

arXiv:2605.01853v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate extended solutions, yet it remains unclear whether these traces reflect substantive internal computation or merel

SpecKV: Adaptive Speculative Decoding with Compression-Aware Gamma Selection

arXiv:2605.02888v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target mo

Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks

arXiv:2605.01136v1 Announce Type: new Abstract: Spectral graph sparsification is a classical tool for reducing graph complexity while preserving Laplacian quadratic forms. In graph neural networks (GN

SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources

arXiv:2605.01459v1 Announce Type: new Abstract: Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed

Stability and Generalization for Decentralized Markov SGD

arXiv:2605.01701v1 Announce Type: new Abstract: Stochastic gradient methods are central to large-scale learning, yet their generalization theory typically relies on independent sampling assumptions. I

Stability of Control Lyapunov Function Guided Reinforcement Learning

arXiv:2605.01978v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become the de facto method for achieving locomotion on humanoid robots in practice, yet stability analysis of the corr

Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation

arXiv:2605.01492v1 Announce Type: cross Abstract: We study high-dimensional LASSO under differential privacy via objective perturbation with heterogeneous covariate scales. In practical scenarios, cov

Stable Blanket with Hidden Variables and Cycles

arXiv:2605.01856v1 Announce Type: cross Abstract: Stabilized regression aims to identify a set of predictors whose conditional relationship with a response variable remains invariant across different

Stable Localized Conformal Prediction via Transduction

arXiv:2605.01452v1 Announce Type: cross Abstract: Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibra

STAR: Decode-Phase Rescheduling for LLM Inference

arXiv:2510.13668v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalan

Statistical Consistency and Generalization of Contrastive Representation Learning

arXiv:2605.02116v1 Announce Type: new Abstract: Contrastive representation learning (CRL) underpins many modern foundation models. Despite recent theoretical progress, existing analyses suffer from se

Structured Analytic Coherent Point Drift for Non-Rigid Point Set Registration

arXiv:2605.00934v1 Announce Type: new Abstract: We introduce Analytic-CPD, a structured analytic variant of coherent point drift for non-rigid point set registration. The method retains the CPD poster

Stylistic Attribute Control in Latent Diffusion Models

arXiv:2605.02583v1 Announce Type: new Abstract: Text-to-image diffusion models have revolutionized image synthesis and editing, but precise control over stylistic attributes remains a challenge, often

Synthetic Designed Experiments for Diagnosing Vision Model Failure

arXiv:2605.00832v1 Announce Type: new Abstract: Current synthetic data pipelines for computer vision generate images without diagnosing what the downstream model actually needs. This open-loop paradig

Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversations

arXiv:2605.02624v1 Announce Type: new Abstract: There is growing interest in exploring user simulation as an alternative to gathering and scoring real user-chatbot interactions for AI chatbot evaluati

Temporally Consistent Object 6D Pose Estimation for Robot Control

arXiv:2605.02708v1 Announce Type: cross Abstract: Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control

Tensegrity crutches with compliance from a pre-stressed self-tensile module improve ground reaction force profiles, speed, effort, comfort, and perceived stability

arXiv:2605.02710v1 Announce Type: new Abstract: Purpose: Six million people use crutches as mobile aids in the US. Rigid designs with no axial mobility limit sensory feedback and lead to secondary inj

TetraJet-v2: Accurate NVFP4 Training for Large Language Models with Oscillation Suppression and Outlier Control

arXiv:2510.27527v2 Announce Type: replace Abstract: Large Language Models (LLMs) training is prohibitively expensive, driving interest in low-precision fully-quantized training (FQT). While novel 4-bi

The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions

arXiv:2605.01637v1 Announce Type: new Abstract: We introduce the Banach-Butterfly Invariant (BBT), an influence-adaptive Banach geometry on the Walsh-Hadamard butterfly factorization. For a Boolean fu

The brain does not predict every upcoming word during language comprehension with maximum precision, contradicting the assumption that next-…

The brain does not predict every upcoming word during language comprehension with maximum precision, contradicting the assumption that next-word prediction serves as the central computational objectiv

researchyann-lecun--x

The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy

arXiv:2605.02177v1 Announce Type: cross Abstract: Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative versio

The Company You Keep: How LLMs Respond to Dark Triad Traits

arXiv:2603.04299v3 Announce Type: replace Abstract: Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this behavi

The Cylindrical Representation Hypothesis for Language Model Steering

arXiv:2605.01844v1 Announce Type: new Abstract: Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical

The Download: inside the Musk v. Altman trial, and AI for democracy

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Week one of the Musk v. Altman trial: what it was like in the

researchmit-tech-review