All entries

NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation

arXiv:2406.13086v2 Announce Type: replace Abstract: Lightweight autonomous unmanned aerial vehicles (UAV) are emerging as a central component of a broad range of applications. However, autonomous navi

agentsarxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Nearest Neighbor Projection Removal Adversarial Training

DGX agent

arXiv:2509.07673v4 Announce Type: replace Abstract: Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adv

researcharxiv-cs-cv

10 Apr 2026

Tutorials

Needle in a Haystack -- One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology

arXiv:2604.07722v1 Announce Type: new Abstract: In computational cytology, detecting malignancy on whole-slide images is difficult because malignant cells are morphologically diverse yet vanishingly r

tutorialsarxiv-cs-cv

Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling

model-releasesarxiv-cs-lg

arXiv:2508.05423v2 Announce Type: replace Abstract: Although artificial neural networks are often described as brain-inspired, their representations typically rely on continuous activations, such as t

Negotiating Privacy with Smart Voice Assistants: Risk-Benefit and Control-Acceptance Tensions

arXiv:2604.06235v1 Announce Type: cross Abstract: Smart Voice assistants (SVAs) are widely adopted by youth, yet privacy decision-making in these environments is often characterized by competing consi

Hardware

NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining

arXiv:2604.06956v1 Announce Type: cross Abstract: Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from

hardwarearxiv-cs-lg

Safety

Neural Computers

arXiv:2604.06425v1 Announce Type: cross Abstract: We propose a new frontier: Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Un

safetyarxiv-cs-ai

Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

arXiv:2604.01204v2 Announce Type: replace-cross Abstract: Primitive-based methods such as 3D Gaussian Splatting have recently become the state-of-the-art for novel-view synthesis and related reconstru

Neural parametric representations for thin-shell shape optimisation

model-releasesarxiv-cs-lg

arXiv:2604.06612v1 Announce Type: cross Abstract: Shape optimisation of thin-shell structures requires a flexible, differentiable geometric representation suitable for gradient-based optimisation. We

Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

arXiv:2507.09503v2 Announce Type: replace-cross Abstract: This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem

Applications

Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism

arXiv:2510.26083v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address

applicationsarxiv-cs-ai

Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms

arXiv:2604.04868v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize across heterogeneous tabular da

Non-Expansive Mappings in Two-Time-Scale Stochastic Approximation: Finite-Time Analysis

arXiv:2501.10806v4 Announce Type: replace-cross Abstract: Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, an

Non-identifiability of Explanations from Model Behavior in Deep Networks of Image Authenticity Judgments

arXiv:2604.07254v1 Announce Type: cross Abstract: Deep neural networks can predict human judgments, but this does not imply that they rely on human-like information or reveal the cues underlying those

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal

arXiv:2411.19653v2 Announce Type: replace-cross Abstract: We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variab

Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models

arXiv:2604.01840v2 Announce Type: replace Abstract: While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks su

Applications

Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

arXiv:2405.11619v2 Announce Type: replace-cross Abstract: Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in exi

applicationsarxiv-cs-ai

Novel View Synthesis as Video Completion

arXiv:2604.08500v1 Announce Type: new Abstract: We tackle the problem of sparse novel view synthesis (NVS) using video diffusion models; given K (approx 5) multi-view images of a scene and their

NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

arXiv:2604.06945v2 Announce Type: replace Abstract: This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recoverin

Hardware

Object-Centric Stereo Ranging for Autonomous Driving: From Dense Disparity to Census-Based Template Matching

arXiv:2604.07980v1 Announce Type: new Abstract: Accurate depth estimation is critical for autonomous driving perception systems, particularly for long range vehicle detection on highways. Traditional

hardwarearxiv-cs-cv

Tutorials

OceanMAE: A Foundation Model for Ocean Remote Sensing

arXiv:2604.08171v1 Announce Type: new Abstract: Accurate ocean mapping is essential for applications such as bathymetry estimation, seabed characterization, marine litter detection, and ecosystem moni

tutorialsarxiv-cs-cv

Tutorials

ODE-free Neural Flow Matching for One-Step Generative Modeling

arXiv:2604.06413v1 Announce Type: new Abstract: Diffusion and flow matching models generate samples by learning time-dependent vector fields whose integration transports noise to data, requiring tens

tutorialsarxiv-cs-lg

ODYN: An All-Shifted Non-Interior-Point Method for Quadratic Programming in Robotics and AI

arXiv:2602.16005v2 Announce Type: replace-cross Abstract: We introduce ODYN, a novel all-shifted primal-dual non-interior-point quadratic programming (QP) solver designed to efficiently handle challen

OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering

arXiv:2604.08209v1 Announce Type: new Abstract: To extend the reinforcement learning post-training paradigm to omni-modal models for concurrently bolstering video-audio understanding and collaborative

OmniTabBench: Mapping the Empirical Frontiers of GBDTs, Neural Networks, and Foundation Models for Tabular Data at Scale

arXiv:2604.06814v1 Announce Type: cross Abstract: While traditional tree-based ensemble methods have long dominated tabular tasks, deep neural networks and emerging foundation models have challenged t

On Emotion-Sensitive Decision Making of Small Language Model Agents

arXiv:2604.06562v1 Announce Type: new Abstract: Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a caus

On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins

arXiv:2603.25898v2 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and senso

On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning

model-releasesarxiv-cs-ro

arXiv:2604.07944v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction a

Safety

On the Global Photometric Alignment for Low-Level Vision

arXiv:2604.08172v1 Announce Type: new Abstract: Supervised low-level vision models rely on pixel-wise losses against paired references, yet paired training sets exhibit per-pair photometric inconsiste

safetyarxiv-cs-cv

On the Price of Privacy for Language Identification and Generation

arXiv:2604.07238v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly trained on sensitive user data, understanding the fundamental cost of privacy in language learning beco

On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors

arXiv:2604.05743v2 Announce Type: replace-cross Abstract: Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level

On the Step Length Confounding in LLM Reasoning Data Selection

arXiv:2604.06834v1 Announce Type: cross Abstract: Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised

On the Uphill Battle of Image frequency Analysis

arXiv:2604.07563v1 Announce Type: new Abstract: This work is a follow up on the newly proposed clustering algorithm called The Inverse Square Mean Shift Algorithm. In this paper a special case of algo

Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators

arXiv:2508.20340v4 Announce Type: replace-cross Abstract: Satisfiability Modulo Theory (SMT) solvers are foundational to modern systems and programming languages research, providing the foundation for

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration

arXiv:2510.12088v2 Announce Type: replace Abstract: Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused

agentsarxiv-cs-ai

Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data

arXiv:2604.06230v1 Announce Type: cross Abstract: The reuse of atomistic simulation data is often limited by heterogeneous formats, incomplete metadata, and a lack of standardized representations of w

Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles

arXiv:2604.08031v1 Announce Type: cross Abstract: Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an int

Hardware

OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing

arXiv:2604.07423v1 Announce Type: new Abstract: Physical Reservoir Computing (PRC) leverages the intrinsic nonlinear dynamics of physical substrates, mechanical, optical, spintronic, and beyond, as fi

hardwarearxiv-cs-ro

Applications

OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

arXiv:2604.07296v2 Announce Type: replace Abstract: Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specif

applicationsarxiv-cs-cl

OpenTrack3D: Towards Accurate and Generalizable Open-Vocabulary 3D Instance Segmentation

arXiv:2512.03532v2 Announce Type: replace Abstract: Generalizing open-vocabulary 3D instance segmentation (OV-3DIS) to diverse, unstructured, and mesh-free environments is crucial for robotics and AR/

Safety

OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks

arXiv:2604.08539v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal

safetyarxiv-cs-cl

Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves

arXiv:2604.06433v1 Announce Type: cross Abstract: Wave setup plays a significant role in transferring wave-induced energy to currents and causing an increase in water elevation. This excess momentum f

Optimal Decay Spectra for Linear Recurrences

arXiv:2604.07658v1 Announce Type: cross Abstract: Linear recurrent models offer linear-time sequence processing but often suffer from suboptimal long-range memory. We trace this to the decay spectrum:

researcharxiv-cs-cl

Optimal Rates for Pure {arepsilon}-Differentially Private Stochastic Convex Optimization with Heavy Tails

model-releasesarxiv-cs-lg

arXiv:2604.06492v1 Announce Type: new Abstract: We study stochastic convex optimization (SCO) with heavy-tailed gradients under pure epsilon-differential privacy (DP). Instead of assuming a bound on t

ORACLE-SWE: Quantifying the Contribution of Oracle Information Signals on SWE Agents

arXiv:2604.07789v1 Announce Type: cross Abstract: Recent advances in language model (LM) agents have significantly improved automated software engineering (SWE). Prior work has proposed various agenti

agentsarxiv-cs-cl

OrgForge: A Multi-Agent Simulation Framework for Verifiable Synthetic Corporate Corpora

arXiv:2603.14997v2 Announce Type: replace Abstract: Building and evaluating enterprise AI systems requires synthetic organizational corpora that are internally consistent, temporally structured, and c

agentsarxiv-cs-cl

Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models

arXiv:2604.08266v1 Announce Type: new Abstract: Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems

oslash Source Models Leak What They Shouldn't nrightarrow: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization

arXiv:2604.08238v1 Announce Type: new Abstract: The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may