Search: “research”

HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation

arXiv:2605.11596v1 Announce Type: new Abstract: Closed-loop driving simulation requires real-time interaction beyond short offline clips, pushing current driving world models toward autoregressive (AR

Hyperbolic Concept Bottleneck Models

DGX agent

arXiv:2605.06440v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) have become a popular approach to enable interpretability in neural networks by constraining classifier input

researcharxiv-cs-cv

13 May 2026

Hypernetworks for Dynamic Feature Selection

arXiv:2605.12278v1 Announce Type: new Abstract: Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constrai

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications

arXiv:2605.11855v1 Announce Type: new Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term de

Information-Theoretic Generalization Bounds for Sequential Decision Making

arXiv:2605.12190v1 Announce Type: cross Abstract: Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis i

Integral Imprecise Probability Metrics

arXiv:2505.16156v3 Announce Type: replace-cross Abstract: Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistic

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

arXiv:2402.16860v2 Announce Type: replace Abstract: The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expan

Interactive State Space Model with Cross-Modal Local Scanning for Depth Super-Resolution

arXiv:2605.11934v1 Announce Type: new Abstract: Guided depth super-resolution (GDSR) reconstructs HR depth maps from LR inputs with HR RGB guidance. Existing methods either model each modality indepen

Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation

arXiv:2605.10947v1 Announce Type: new Abstract: EEG microstate analysis segments continuous brain electrical activity into brief, quasi-stable topographic configurations that reflect discrete function

Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes

arXiv:2605.11179v1 Announce Type: cross Abstract: Many three-dimensional spatial fields are anisotropic, with directions of rapid and slow variation that need not align with the coordinate axes. Stand

Interpretable rainfall modelling reveals rapid reorganisation of Amazonian rainfall under vegetation loss

arXiv:2605.10948v1 Announce Type: cross Abstract: Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitati

Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper

arXiv:2605.11714v1 Announce Type: new Abstract: Robotic manipulation of flexible objects is widely required in both industrial and service applications. Among such objects, paper-like materials exhibi

Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition

arXiv:2605.12047v1 Announce Type: new Abstract: Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate

Is Monotonic Sampling Necessary in Diffusion Models?

arXiv:2605.11773v1 Announce Type: new Abstract: Diffusion models generate samples by iteratively denoising a Gaussian prior, traversing a sequence of noise levels that, in every published sampler, dec

Kairos: A Scalable Serving System for Physical AI

arXiv:2605.11381v1 Announce Type: new Abstract: Physical AI is experiencing rapid growth with frontier foundation models increasing its capabilities across general environments. Physical AI tasks are

Language Modeling with Hyperspherical Flows

arXiv:2605.11125v1 Announce Type: new Abstract: Discrete Diffusion Language Models progressed rapidly as an alternative to autoregressive (AR) models, motivated by their parallel generation abilities.

Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence

arXiv:2605.11348v1 Announce Type: new Abstract: During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physic

Latent Chain-of-Thought Improves Structured-Data Transformers

arXiv:2605.11262v1 Announce Type: new Abstract: Chain-of-thought and more broadly test-time compute are known to augment the expressive capabilities of language models and have led to major innovation

Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation

arXiv:2605.11832v1 Announce Type: new Abstract: This paper tackles spatial perception and manipulation challenges in Vision-Language-Action (VLA) models. To address depth ambiguity from monocular inpu

Learning Ego-Centric BEV Representations from a Perspective-Privileged View: Cross-View Supervision for Online HD Map Construction

arXiv:2605.12218v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations derived from multi-camera input have become a central interface for online high-definition (HD) map construction.

Learning U-Statistics with Active Inference

arXiv:2605.11638v1 Announce Type: cross Abstract: U-statistics play a central role in statistical inference. In many modern applications, however, acquiring the labels required for U-statistics is cos

Learning Weakly Communicating Average-Reward CMDPs: Strong Duality and Improved Regret

arXiv:2605.11586v1 Announce Type: new Abstract: We study infinite-horizon average-reward constrained Markov decision processes (CMDPs) under the weakly communicating assumption. Our contributions are

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection

arXiv:2605.11231v1 Announce Type: new Abstract: Synthetic data is useful only when the added samples fill missing parts of the training distribution that matter for the downstream task. We introduce L

Limits of Learning Linear Dynamics from Experiments

arXiv:2605.12010v1 Announce Type: new Abstract: Learning governing dynamics from data is a common goal across the sciences, yet it is only well-posed when the underlying mechanisms are identifiable. I

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models

arXiv:2605.11011v1 Announce Type: new Abstract: Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typi

Lower bounds for one-layer transformers that compute parity

arXiv:2605.12171v1 Announce Type: new Abstract: This note shows that no self-attention layer post-processed by a rational function can sign-represent the parity function unless the product of the numb

Machine Learning for neutron source distributions

arXiv:2605.12165v1 Announce Type: cross Abstract: In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation

MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining

arXiv:2605.11034v1 Announce Type: cross Abstract: We present MambaNetBurst, a compact tokenizer-free byte-level sequence classifier for network burst classification based on a Mamba-2 backbone. In con

Manifold Sampling via Entropy Maximization

arXiv:2605.12338v1 Announce Type: new Abstract: Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes conver

Mapping Embodied Affective Touch Strategies on a Humanoid Robot

arXiv:2605.11825v1 Announce Type: new Abstract: Affective touch in human-robot interaction is shaped not only by emotional intent, but also by robot embodiment, including touch location, physical cons

Martingale-Consistent Self-Supervised Learning

arXiv:2605.11846v1 Announce Type: new Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images.

MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functions

arXiv:2605.12368v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) with machine learning typically requires training a new neural network for every new equation. This optimi

MieDB-100k: A Comprehensive Dataset for Medical Image Editing

arXiv:2602.09587v2 Announce Type: replace Abstract: The scarcity of high-quality data remains a primary bottleneck in adapting multimodal generative models for medical image editing. Existing medical

Minimax Rates and Spectral Distillation for Tree Ensembles

arXiv:2605.11841v1 Announce Type: cross Abstract: Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theore

Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs

arXiv:2605.12262v1 Announce Type: cross Abstract: We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of

MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound

arXiv:2605.11617v1 Announce Type: new Abstract: Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decis

Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance

arXiv:2605.11900v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However,

Modeling Narrative Structure in Latin Epic Poetry with Automatically Generated Story Grammars

arXiv:2502.12276v2 Announce Type: replace Abstract: Computational methods for analyzing prose and poetry utilize word embeddings and other abstract representations that sometimes obscure context-rich

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

arXiv:2602.15451v3 Announce Type: replace-cross Abstract: Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. H

Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning

arXiv:2411.19093v4 Announce Type: replace Abstract: Access to drinking water and sanitation services is essential for health and well-being, yet large global disparities persist. Sustainable Developme

more demos on Interaction Models collaboratively doing system design, reading papers, fact-checking with live generative UI

researchsoumith-chintala--x

more demos on Interaction Models collaboratively doing system design, reading papers, fact-checking with live generative UI 1. (System design) - The Interaction Models see your screen and collaborates

Much of Geospatial Web Search Is Beyond Traditional GIS

arXiv:2605.11336v1 Announce Type: cross Abstract: Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what peo

Multi-Timescale Conductance Spiking Networks: A Sparse, Gradient-Trainable Framework with Rich Firing Dynamics for Enhanced Temporal Processing

arXiv:2605.11835v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) promise low-power event-driven computation for temporally rich tasks, but commonly used neuron models often trade off g

Not Worth Mentioning? A Pilot Study on Salient Proposition Annotation

arXiv:2603.27358v2 Announce Type: replace Abstract: Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work

On Predicting the Post-training Potential of Pre-trained LLMs

arXiv:2605.11978v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. Howev

On the Approximation Complexity of Matrix Product Operator Born Machines

arXiv:2605.11471v1 Announce Type: new Abstract: Matrix product operator Born machines (MPO-BMs) are tractable tensor-network models for probabilistic modeling, but their efficient approximation capabi

One-Step Generative Modeling via Wasserstein Gradient Flows

arXiv:2605.11755v1 Announce Type: cross Abstract: Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it

Operator Spectroscopy of Trained Lattice Samplers

arXiv:2605.11199v1 Announce Type: cross Abstract: Trained lattice samplers are usually judged by the ensembles they generate. Here we instead analyze the trained field-space function itself: a flow-ma