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When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

arXiv:2604.19206v1 Announce Type: new Abstract: The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely ha

safetyarxiv-cs-cv

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Human DGX agent

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When Does Verification Pay Off? A Closer Look at LLMs as Solution Verifiers

DGX agent

arXiv:2512.02304v2 Announce Type: replace Abstract: Large language models (LLMs) can act as both problem solvers and solution verifiers, where the latter select high-quality answers from a pool of sol

researcharxiv-cs-cl

22 Apr 2026

When Graph Structure Becomes a Liability: A Critical Re-Evaluation of Graph Neural Networks for Bitcoin Fraud Detection under Temporal Distribution Shift

arXiv:2604.19514v1 Announce Type: cross Abstract: The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not be

researcharxiv-cs-ai

When Langevin Monte Carlo Meets Randomization: Non-asymptotic Error Bounds beyond Log-Concavity and Gradient Lipschitzness

arXiv:2509.25630v2 Announce Type: replace-cross Abstract: Efficient sampling from complex and high dimensional target distributions turns out to be a fundamental task in diverse disciplines such as sc

When Safety Fails Before the Answer: Benchmarking Harmful Behavior Detection in Reasoning Chains

model-releasesarxiv-cs-cl

arXiv:2604.19001v1 Announce Type: new Abstract: Large reasoning models (LRMs) produce complex, multi-step reasoning traces, yet safety evaluation remains focused on final outputs, overlooking how harm

Where Fake Citations Are Made: Tracing Field-Level Hallucination to Specific Neurons in LLMs

arXiv:2604.18880v1 Announce Type: cross Abstract: LLMs frequently generate fictitious yet convincing citations, often expressing high confidence even when the underlying reference is wrong. We study t

researcharxiv-cs-ai

Whispers in the Machine: Confidentiality in Agentic Systems

arXiv:2402.06922v5 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents combine LLMs with external tools to automate tasks such as scheduling meetings, managing documents, or

agentsarxiv-cs-lg

Tutorials

Who Benefits from AI? Self-Selection, Skill Gap, and the Hidden Costs of AI Feedback

arXiv:2409.18660v2 Announce Type: replace-cross Abstract: Feedback from artificial intelligence (AI) is increasingly easy to access and research has already established that people learn from it. But

tutorialsarxiv-cs-ai

Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem

arXiv:2604.18586v1 Announce Type: cross Abstract: Vaccination remains a cornerstone of global public health, yet the COVID-19 pandemic exposed how online misinformation, political polarization, and de

researcharxiv-cs-ai

Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?

model-releasesarxiv-cs-cl

arXiv:2603.24472v2 Announce Type: replace Abstract: Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. Howeve

Wrench-Aware Admittance Control for Unknown-Payload Manipulation

arXiv:2604.19469v1 Announce Type: new Abstract: Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center poin

researcharxiv-cs-ro

Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

model-releasesarxiv-cs-ai

arXiv:2604.02368v4 Announce Type: replace Abstract: As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficienc

ZC-Swish: Stabilizing Deep BN-Free Networks for Edge and Micro-Batch Applications

arXiv:2604.19453v1 Announce Type: new Abstract: Batch Normalization (BN) is a cornerstone of deep learning, yet it fundamentally breaks down in micro-batch regimes (e.g., 3D medical imaging) and non-I

Local Ai

Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments

arXiv:2509.12516v2 Announce Type: replace Abstract: Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes,

local-aiarxiv-cs-ro

A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification

arXiv:2604.16546v1 Announce Type: new Abstract: Three-dimensional (3D) fingerprint recognition and identification offer several advantages over traditional two-dimensional (2D) recognition systems. Th

researcharxiv-cs-cv

A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery

model-releasesarxiv-cs-cv

arXiv:2604.16663v1 Announce Type: new Abstract: Landslide detection from high resolution satellite imagery is a critical task for disaster response and risk assessment, yet the relative effectiveness

A Community-Based Approach for Stance Distribution and Argument Organization

arXiv:2604.16852v1 Announce Type: new Abstract: The proliferation of online debate platforms and social media has led to an unprecedented volume of argumentative content on controversial topics from m

researcharxiv-cs-cl

A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2604.18205v1 Announce Type: new Abstract: Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual qua

A Computational Method for Measuring 'Open Codes' in Qualitative Analysis

arXiv:2411.12142v4 Announce Type: replace Abstract: Qualitative analysis is critical to understanding human datasets in many social science disciplines. A central method in this process is inductive c

researcharxiv-cs-cl

A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection

arXiv:2604.01798v2 Announce Type: replace Abstract: Breast cancer is a highly heterogeneous disease with diverse molecular profiles. The PAM50 gene signature is widely recognized as a standard for cla

researcharxiv-cs-cv

A Discordance-Aware Multimodal Framework with Multi-Agent Clinical Reasoning

arXiv:2604.16333v1 Announce Type: new Abstract: Knee osteoarthritis frequently exhibits discordance between structural damage observed in imaging and patient-reported symptoms such as pain. This misma

agentsarxiv-cs-lg

A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks

arXiv:2510.05608v2 Announce Type: replace Abstract: Agents based on large language models (LLMs) struggle with brainless trial-and-error and generating hallucinatory actions due to a lack of global pl

agentsarxiv-cs-cl

A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation

arXiv:2604.17679v1 Announce Type: new Abstract: Autonomous navigation requires planning to reach a goal safely and efficiently in complex and potentially dynamic environments. Graph search-based algor

safetyarxiv-cs-ro

A High-Accuracy Optical Music Recognition Method Based on Bottleneck Residual Convolutions

arXiv:2604.16446v1 Announce Type: new Abstract: Optical Music Recognition (OMR) aims to convert printed or handwritten music score images into editable symbolic representations. This paper presents an

safetyarxiv-cs-cv

Local Ai

A Lightweight Transformer for Pain Recognition from Brain Activity

arXiv:2604.16491v1 Announce Type: new Abstract: Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objectiv

local-aiarxiv-cs-cv

A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

arXiv:2307.12409v3 Announce Type: replace Abstract: We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now vari

A Mechanism Study of Delayed Loss Spikes in Batch-Normalized Linear Models

arXiv:2604.16809v1 Announce Type: cross Abstract: Delayed loss spikes have been reported in neural-network training, but existing theory mainly explains earlier non-monotone behavior caused by overly

A Model and Estimation of the Bitcoin Transaction Fee

arXiv:2604.17183v1 Announce Type: cross Abstract: Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone becau

A Multi-Agent Approach for Claim Verification from Tabular Data Documents

arXiv:2604.17225v1 Announce Type: new Abstract: We present a novel approach for claim verification from tabular data documents. Recent LLM-based approaches either employ complex pretraining/fine-tunin

agentsarxiv-cs-cl

Applications

A multimodal and temporal foundation model for virtual patient representations at healthcare system scale

arXiv:2604.18570v1 Announce Type: cross Abstract: Modern medicine generates vast multimodal data across siloed systems, yet no existing model integrates the full breadth and temporal depth of the clin

applicationsarxiv-cs-cl

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work

arXiv:2604.18555v1 Announce Type: new Abstract: This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-

A novel LSTM music generator based on the fractional time-frequency feature extraction

arXiv:2604.17823v1 Announce Type: cross Abstract: In this paper, we propose a novel approach for generating music based on an artificial intelligence (AI) system. We analyze the features of music and

researcharxiv-cs-cl

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations

arXiv:2604.17494v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Exis

A proposal for PU classification under Non-SCAR using clustering and logistic model

arXiv:2604.17130v1 Announce Type: cross Abstract: The present study aims to investigate a cluster cleaning algorithm that is both computationally simple and capable of solving the PU classification wh

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development

arXiv:2604.17763v1 Announce Type: cross Abstract: This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with impro

safetyarxiv-cs-lg

A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models

arXiv:2604.17258v1 Announce Type: new Abstract: Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model

agentsarxiv-cs-ro

A Real-Time Bike-Pedestrian Safety System with Wide-Angle Perception and Evaluation Testbed for Urban Intersections

arXiv:2604.17046v1 Announce Type: new Abstract: Collisions between cyclists and pedestrians at urban intersections remain a persistent source of injuries, yet few systems attempt real-time warnings to

safetyarxiv-cs-cv

A Real-World Grasping-in-Clutter Performance Evaluation Benchmark for Robotic Food Waste Sorting

model-releasesarxiv-cs-ro

arXiv:2602.18835v2 Announce Type: replace Abstract: Food waste management is critical for sustainability, yet inorganic contaminants hinder recycling potential. Robotic automation accelerates sorting

A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization

arXiv:2508.17412v4 Announce Type: replace Abstract: Conventional regularization is designed to control variance, but in small-data regression it can also aggravate underfitting when predictive signal

A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations

arXiv:2502.13570v4 Announce Type: replace-cross Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics

A Sensitivity Approach to Causal Inference Under Limited Overlap

arXiv:2511.22003v2 Announce Type: replace-cross Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance

safetyarxiv-cs-lg

A Sugeno Integral View of Binarized Neural Network Inference

arXiv:2604.17967v1 Announce Type: cross Abstract: In this article, we establish a precise connection between binarized neural networks (BNNs) and Sugeno integrals. The advantage of the Sugeno integral

Tutorials

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions

arXiv:2604.17312v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). H

tutorialsarxiv-cs-lg

A Survey of Spatial Memory Representations for Efficient Robot Navigation

model-releasesarxiv-cs-cv

arXiv:2604.16482v1 Announce Type: new Abstract: As vision-based robots navigate larger environments, their spatial memory grows without bound, eventually exhausting computational resources, particular

A Survey on the Security of Long-Term Memory in LLM Agents: Toward Mnemonic Sovereignty

arXiv:2604.16548v1 Announce Type: cross Abstract: Research on large language model (LLM) security is shifting from 'will the model leak training data' to a more consequential question: can an agent wi

agentsarxiv-cs-cl

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era

arXiv:2604.16586v1 Announce Type: new Abstract: Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with physicochemical and b

A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems

arXiv:2509.24478v2 Announce Type: replace Abstract: Modern neural networks have greatly improved performance across speech recognition benchmarks. However, gains are often driven by frequent words wit

safetyarxiv-cs-cl

A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection

model-releasesarxiv-cs-cl

arXiv:2503.11838v2 Announce Type: replace Abstract: Sarcasm detection, with its figurative nature, poses unique challenges for affective systems designed to perform sentiment analysis. While these sys