Search: “papers”

RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.29028v1 Announce Type: new Abstract: In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassi

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Script Choice in LLMs: Evidence for Late-Layer Commitment

DGX agent

arXiv:2609.28784v1 Announce Type: new Abstract: In this paper, we investigate how script knowledge is distributed across the layers of LLMs using two complementary interpretability methods: logistic r

researcharxiv-cs-cl

25 Sept 2026

Simple Torque-Observation Alignment for Zero-Shot Sim-to-Real Grasping with a Direct-Drive Gripper

arXiv:2609.29031v1 Announce Type: new Abstract: Torque observations in reinforcement learning remain challenging because simulated and measured torque differ in scale, offset, and noise. In this paper

Singularity Analysis for the Perspective-Four and Five-Line Problems

arXiv:2609.29417v1 Announce Type: new Abstract: This paper deals with image-based visual servoing and pose estimation by observing four and five lines. Our main interest is to determine the relative c

researcharxiv-cs-ro

Temporal Learning for End-Effector Position Estimation under Aerodynamic Disturbances in Aerial Continuum Manipulation

arXiv:2609.28716v1 Announce Type: cross Abstract: This paper investigates temporal neural networks for mbox{end-effector} position mbox{estimation} of an aerial continuum manipulator (ACM) operating u

A Quasi-Direct-Drive Underactuated Asymmetric Hand for Dexterous and Efficient Grasping and Manipulation

arXiv:2609.27240v1 Announce Type: new Abstract: In this paper, we present the Berkeley QUAD (Quasi-direct-drive, Underactuated, Asymmetric Design) Hand, a four-finger anthropomorphic robotic hand with

researcharxiv-cs-ro

Adversarial Attacks and Identity Leakage in De-Identification Systems: An Empirical Study

arXiv:2609.27022v1 Announce Type: new Abstract: In this paper, we investigate the impact of adversarial attacks on identity encoders within a realistic de-identification framework. Our experiments sho

Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court

model-releasesarxiv-cs-cl

arXiv:2609.26945v1 Announce Type: new Abstract: Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

model-releasesarxiv-cs-cv

arXiv:2412.02575v3 Announce Type: replace Abstract: Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question A

Discrete optimal transport is a strong audio adversarial attack

arXiv:2509.14959v4 Announce Type: replace-cross Abstract: In this paper, we investigate discrete optimal transport (DOT) as a black-box attack against modern automatic speaker verification (ASV) and a

Godel's and Scott's Variants of the Ontological Argument in Lean 4

arXiv:2609.26806v1 Announce Type: cross Abstract: This paper presents a complete, structure-preserving port to Lean 4 of the Isabelle/HOL dataset accompanying Benzmuller and Scott's study of Godel's m

Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

model-releasesarxiv-cs-ai

arXiv:2609.26816v1 Announce Type: cross Abstract: This paper develops a data-driven framework for long-term prediction of fluid--structure interaction (FSI) dynamics, focusing on the flow-induced vibr

Passing: An Endless Journey through Reconstructed Spacetime with AI-Generated Sound

arXiv:2609.27489v1 Announce Type: cross Abstract: This paper introduces Passing, an interactive audiovisual installation that generates an endless journey from a single continuous monorail-window reco

Riemannian Structure and Optimization for a Class of Low-Parametric Orthogonal Matrices

model-releasesarxiv-cs-ai

arXiv:2609.27982v1 Announce Type: cross Abstract: In this paper, we are concerned with matrices formed by block-diagonal factors interleaved with fixed permutations -- a flexible family of structured

Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translation

arXiv:2609.28117v1 Announce Type: cross Abstract: In this paper, we introduce a gradient-based head attribution strategy where the Token-level Max-Margin loss is backpropagated to the attention maps.

Simultaneous Latent Budget Trees for Stratified Classification

arXiv:2606.13295v3 Announce Type: replace-cross Abstract: In the era of Explainable Artificial Intelligence, there is a renewed focus on single trees for their ease of interpretation. This paper intro

Spread and Scale: What Determines Whether Test-Time Budget Allocation Pays

arXiv:2609.27917v1 Announce Type: cross Abstract: Neural combinatorial optimization solvers generate many candidate solutions per instance and report the best one found, using the same sample budget f

Statistical Properties of Deep Neural Networks with Dependent Data

arXiv:2410.11113v4 Announce Type: replace-cross Abstract: This paper develops theory for deep neural network (DNN) estimators under dependent data. To provide theory applicable to a variety of DNN-bas

Tutorials

Tail-Aware Geometry Learning for Conformal Ellipsoids

arXiv:2609.27221v1 Announce Type: new Abstract: This paper studies multivariate conformal prediction (CP), a distribution-free uncertainty quantification framework with finite-sample coverage guarante

tutorialsarxiv-cs-lg

Agents

Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward

arXiv:2609.28396v1 Announce Type: new Abstract: This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and s

agentsarxiv-cs-ro

Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations

arXiv:2609.27037v1 Announce Type: new Abstract: Wake word detection is a critical component of virtual assistants, serving as the gateway to seamless user interactions. This paper introduces a novel w

Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning

model-releasesarxiv-cs-ai

arXiv:2603.08930v2 Announce Type: replace-cross Abstract: This paper introduces a benchmark for evaluating whether vision-language models (VLMs) can generate plant simulation configurations from image

A Deployment Study of Identity-Gated Drone Gesture Control

model-releasesarxiv-cs-ro

arXiv:2609.25511v1 Announce Type: new Abstract: Vision-based gesture control accepts commands from any hand in the camera field of view, which is unsafe in shared indoor spaces. This paper presents IG

Control Barrier Functions for Safe Free-Flying Robotic Spacecraft Operations in Tumbling Target Capture

arXiv:2609.25905v1 Announce Type: new Abstract: This paper presents a modular control barrier function (CBF) framework for safe free-flying robotic spacecraft operations during tumbling target capture

Design and Implementation of an Ultra-Low-Cost Wall-Climbing Robot for Infrastructure Crack Detection

arXiv:2609.26130v1 Announce Type: new Abstract: Crack detection is a crucial process to ensure the safety and longevity of buildings and other infrastructure. In this paper, we developed a low-cost, a

Designing an Efficient Excavator Bucket for Lunar ISRU: A Comparative Study with Vision-Based Fill and Displacement Analysis

arXiv:2609.25724v1 Announce Type: new Abstract: This paper present a spiral-cavity wheel for lunar regolith excavation and a sensor-light evaluation stack that jointly estimates fill ratio (vision), s

researcharxiv-cs-ro

Detecting GPT-Assisted Writing Using Interpretable Stylometric Features

arXiv:2609.26687v1 Announce Type: new Abstract: Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discrimina

researcharxiv-cs-cl

Directional Total Variation-Regularized Implicit Neural Representations (DTV-INR) for Continuous Super-Resolution in Degraded Imaging Domains

model-releasesarxiv-cs-cv

arXiv:2609.25429v1 Announce Type: new Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that

Local Ai

Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization

arXiv:2609.26423v1 Announce Type: new Abstract: This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in SE(3) against 3D lidar maps.

local-aiarxiv-cs-ro

End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks

arXiv:2609.25044v1 Announce Type: cross Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom).

FMMD: A multimodal multidisciplinary dataset of open peer reviews from F1000Research

arXiv:2602.14285v2 Announce Type: replace-cross Abstract: Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) sys

safetyarxiv-cs-cl

Local Ai

GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation

arXiv:2609.26729v1 Announce Type: new Abstract: In this paper, we proposed GAD-MambaUNet, a lightweight medical image segmentation network that combines efficient local modeling, direction--group stat

local-aiarxiv-cs-cv

Generalized Deep Regression for Repeated Measurements

arXiv:2609.25605v1 Announce Type: cross Abstract: In this paper, we study the estimation of a marginal regression function from independent units with repeated binary, count, or continuous responses u

GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement

arXiv:2609.26361v1 Announce Type: cross Abstract: With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly

GRIP: Gaussian Rendering as a Cross-Modal Bridge for Image-to-Point Cloud Registration

arXiv:2609.25966v1 Announce Type: new Abstract: This paper introduces GRIP, a pose-conditioned refinement framework for pixel-to-point matching and 2D to 3D registration. Given an initial coarse pose

Hierarchical Sparse Bayesian Multitask Learning for Disease Prediction in Pooled Microbiome Studies

model-releasesarxiv-cs-lg

arXiv:2502.02552v2 Announce Type: replace Abstract: This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning pro

Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian

arXiv:2609.25163v1 Announce Type: new Abstract: The paper proposes a novel data-driven framework for designing and training a feedback linearizing controller by explicitly incorporating relative degre

MECT: Mixture of Experts with CNN-Transformer Network for Speaker verification

arXiv:2609.24061v1 Announce Type: cross Abstract: In this paper, we propose MECT, a speaker verification model that integrates the Mixture-of-Experts (MoE) mechanism into a CNN-Transformer backbone wi

NPU Accelerator: Quantized Real-Time Vehicle Detection on PYNQ-Z1 Using FINN

arXiv:2609.24757v1 Announce Type: cross Abstract: This paper presents the design, optimization, implementation, and on-board validation of a neural processing unit (NPU) accelerator for real-time vehi

ROBE: Reversed-Order-Biased-Experts for Extracting Extreme Long-tail Events from Historical Texts

arXiv:2608.24268v2 Announce Type: replace Abstract: This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries. The methods we p

researcharxiv-cs-cl

Situation Aware Locomotion for Dual Mobile Cobots in Shared Environments

arXiv:2609.26083v1 Announce Type: new Abstract: This paper presents a situation aware locomotion framework for two mobile collaborative robots operating in shared industrial environments. The proposed

Structured Decomposition for Reliable LLM-Generated Access Control Policies

arXiv:2609.24036v1 Announce Type: new Abstract: This paper presents an LLM-based system that translates natural-language access control policies (NLACPs) into executable Rego code for Open Policy Agen

The Probabilistic Structure of Large Language Models

arXiv:2609.25134v1 Announce Type: new Abstract: This paper presents a probabilistic perspective on large language models (LLMs), developed with the aim of bringing together, in a single self-contained

ToW3D: Consistency-aware Interactive Point-based Mesh Editing on GANs

arXiv:2609.26078v1 Announce Type: new Abstract: In this paper, we propose ToW3D that enables precise and consistent control over 3D generative adversarial networks (GANs) with the Tug-of-War competiti

A multi-temporal dataset for mapping burned areas in the Brazilian Cerrado using time series of remote sensing imagery

arXiv:2609.23687v1 Announce Type: cross Abstract: This paper introduces a multi-temporal tabular dataset derived from satellite images to map burned areas in the Chapada dos Veadeiros National Park, i

Agents

A Tutorial on Prompt Engineering: From Messy Thoughts to AI Workflows

arXiv:2609.22249v1 Announce Type: new Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice

agentsarxiv-cs-cl

Balancing Reasoning and Hardware Constraints in RAG Pipelines for Ukrainian Multi-Domain Document Understanding

model-releasesarxiv-cs-cl

arXiv:2609.22124v1 Announce Type: new Abstract: This paper describes the system submitted to the UNLP 2026 Shared Task on Multi-Domain Document Understanding. The challenge required extracting precise

Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT

arXiv:2609.22291v1 Announce Type: new Abstract: This paper presents a comprehensive experimental evaluation and detailed analysis of state-of-the-art multi-object tracking algorithms, with an emphasis