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arXiv:2510.23463v4 Announce Type: replace Abstract: Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In i

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Research

Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination

DGX agent

arXiv:2610.02868v1 Announce Type: cross Abstract: Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a

researcharxiv-cs-ai

5 Oct 2026

Estimating prevalence with precision and accuracy

arXiv:2507.06061v2 Announce Type: replace-cross Abstract: Unlike classification, whose goal is to estimate the class of each data point, quantification (or prevalence estimation) aims to estimate the

Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty

arXiv:2610.02877v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate a

safetyarxiv-cs-cl

Exploring the Trade-Off Between Structured Pruning and Fault Tolerance in Deep Neural Networks for Space Applications

arXiv:2610.03117v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) inherently exhibit a degree of robustness to bit-level faults due to their distributed representation of information. As a m

Tutorials

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure

tutorialsarxiv-cs-ai

Generalization of Transformer-Based Neural Quantum States via In-Context Learning

arXiv:2610.03463v1 Announce Type: cross Abstract: Neural quantum states based on modern deep learning architectures have emerged as powerful representations for quantum many-body systems. In particula

Applications

High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning

arXiv:2610.02578v1 Announce Type: cross Abstract: To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough

applicationsarxiv-cs-lg

How Robust Is Multimodal Claim Verification to LLM Rewriting?

arXiv:2610.02841v1 Announce Type: new Abstract: LLMs are known to introduce stylistic changes into generated text, yet how these stylistic shifts affect model decisions on scientific tasks remains und

researcharxiv-cs-cl

Tutorials

How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate

arXiv:2610.02557v1 Announce Type: new Abstract: As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate

tutorialsarxiv-cs-ai

Interpretable Deepfake Detection in Videos via Explicit Forensic Features and Temporal Modeling

arXiv:2610.03380v1 Announce Type: new Abstract: Deepfake detection in videos remains challenging, as manipulated content may appear visually consistent at the frame level while exhibiting subtle tempo

Joint Movement and Compression Ratio Design for Mobile Embodied AI Networks (MEAN)

arXiv:2610.02334v1 Announce Type: cross Abstract: Mobile embodied AI networks (MEAN) enable embodied agents to perceive, reason, communicate, and act in wireless environments. In such networks, agent

agentsarxiv-cs-lg

Tutorials

Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming

arXiv:2610.03071v1 Announce Type: new Abstract: Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation. Despite advances in traditi

tutorialsarxiv-cs-lg

MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory

arXiv:2610.02255v1 Announce Type: new Abstract: Time series forecasting remains a critical challenge across numerous domains. Despite significant advancements, existing approaches struggle with comple

agentsarxiv-cs-lg

MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design

model-releasesarxiv-cs-lg

arXiv:2610.02214v1 Announce Type: cross Abstract: Ship structures govern vessel strength, safety, and manufacturability, but their design must satisfy hundreds of classification society requirements,

Mitigating Private Data Leakage in LLMs with Whiteout

arXiv:2610.02418v1 Announce Type: cross Abstract: Modern large language models (LLMs) are trained on massive, largely unfiltered datasets, including content scraped from nearly every accessible websit

safetyarxiv-cs-ai

Morpheus: A Morphology-Aware Neural Tokenizer and Word Embedder for Turkish

arXiv:2606.18717v2 Announce Type: replace-cross Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus

safetyarxiv-cs-ai

Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework

arXiv:2606.26567v1 Announce Type: cross Abstract: High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challen

agentsarxiv-cs-ai

Neuron merging via inverse-activation regression for post-training compression of sigmoid neural networks

arXiv:2610.02559v1 Announce Type: new Abstract: As neural networks continue to grow in scale, model compression is becoming increasingly important for efficient inference under limited computational r

Local Ai

On-Board Anomaly Detection for Efficient Marine Environmental Monitoring

arXiv:2610.03649v1 Announce Type: cross Abstract: Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human a

local-aiarxiv-cs-ai

Optimal Planning in a Dynamic World

arXiv:2610.03312v1 Announce Type: new Abstract: Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path plannin

agentsarxiv-cs-ai

PoCoFL: POlicy-COmpliant Federated Learning

arXiv:2610.03650v1 Announce Type: cross Abstract: Federated Learning (FL) is a privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to partici

Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning

arXiv:2610.02615v1 Announce Type: new Abstract: Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning per

Real-time Event-camera Stereo Visual Odometry via Keytime Gaussian Process Regression

arXiv:2610.02601v1 Announce Type: new Abstract: Event cameras have microsecond-level temporal resolution and high dynamic range which make them more resilient to motion blur and poor illumination than

researcharxiv-cs-ro

Relevant Evidence Decoding for Audio-Visual Hallucination Mitigation

arXiv:2610.02976v1 Announce Type: new Abstract: Audio-Visual Large Language Models (AV-LLMs) remain prone to cross-modal hallucinations, where one modality incorrectly affects predictions about anothe

researcharxiv-cs-ai

Reward Inflation: A Healthy Stimulus for Reinforcement Learning

arXiv:2610.02545v1 Announce Type: new Abstract: Reward serves as the primary learning signal in reinforcement learning (RL). However, while reward magnitudes are typically held fixed throughout traini

RxnOptBench: Benchmarking LLMs for Reaction-Condition Optimization in Organic Methodology

arXiv:2610.02242v1 Announce Type: cross Abstract: Chemical reaction-condition optimization -- choosing the catalyst, ligand, solvent, reagent, temperature, time, and atmosphere that jointly maximize y

Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds

model-releasesarxiv-cs-lg

arXiv:2610.03165v1 Announce Type: new Abstract: Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved O(epsilon^{-3}) sample complexity to find an epsilon-sta

Shrome at Touche: Soft-Vote Ensembling and Counter-Causal Augmentation for Causality Extraction

arXiv:2610.03268v1 Announce Type: new Abstract: Touche 2026 extends causality extraction to counter-causal claims: news sentences whose surface form appears causal but whose meaning denies the causati

researcharxiv-cs-cl

Local Ai

SideKernel: A Usable microVM Sandbox for AI Coding Agents on macOS

arXiv:2610.02456v1 Announce Type: cross Abstract: AI coding agents are untrusted system components, yet they require autonomy on the developer machines they run on. This contradiction is a security pr

local-aiarxiv-cs-ai

Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection

arXiv:2610.03082v1 Announce Type: new Abstract: Bridges contribute significantly to transportation connectivity and urban development. Therefore, reliable bridge monitoring is crucial for protecting p

StanceEval 2026: The Second Stance Detection Shared Task

arXiv:2610.03215v1 Announce Type: new Abstract: StanceEval 2026 is the second edition of the StanceEval shared task series on stance detection in Arabic social media text. Stance detection aims to ide

researcharxiv-cs-cl

State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting

arXiv:2610.02248v1 Announce Type: new Abstract: Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded ir

Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability

arXiv:2610.03000v1 Announce Type: cross Abstract: Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training withi

Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

arXiv:2407.12927v4 Announce Type: replace Abstract: Systems for multimodal emotion recognition (ER) are commonly trained to extract features from different modalities (e.g., visual, audio, and textual

Threat-Preserving Representation Sensitivity in Agent-Security Benchmarks

arXiv:2610.03585v1 Announce Type: cross Abstract: Security benchmarks for LLM-based agents often report the attack success rate (ASR) as a measure of model robustness and use these scores to compare d

Towards Unified Music Emotion Recognition across Dimensional and Categorical Models

arXiv:2502.03979v3 Announce Type: replace-cross Abstract: One of the most significant challenges in Music Emotion Recognition (MER) comes from the fact that emotion labels can be heterogeneous across

researcharxiv-cs-ai

Traversing the Satisfaction-Diversity Frontier in Text-to-Image Diffusion

arXiv:2610.02372v1 Announce Type: new Abstract: Text-to-image generation enables users to explore several images generated from the same prompt. For these generated images to be useful, each one must

researcharxiv-cs-ai

Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models

model-releasesarxiv-cs-cl

arXiv:2610.03077v1 Announce Type: new Abstract: Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. Ho

ViTok: Improving Dense Semantics in AM-RADIO-Style Multi-Teacher Distillation with PHI-S and Masked Image Modelling

arXiv:2610.02903v1 Announce Type: new Abstract: We study how to consolidate the current VITOK progress into a single multi-teacher distillation recipe that jointly preserves global recognition and den

ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis

arXiv:2610.02263v1 Announce Type: cross Abstract: Patient-level lung ultrasound (LUS) diagnosis requires integrating findings acquired across multiple anatomical zones, yet clinical examinations frequ

researcharxiv-cs-cv

Applications

A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders

arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are t

applicationsarxiv-cs-ai

A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform

arXiv:2610.00926v1 Announce Type: cross Abstract: Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative

agentsarxiv-cs-cv

Affine-Aligned Atlas for Canonical Gaussian Construction in Video Representation

arXiv:2610.01114v1 Announce Type: new Abstract: Gaussian splatting has recently emerged as an efficient representation for images and videos due to its explicit structure and fast rendering capability

Anomaly Detection and Localization for the Pantograph-Catenary System

arXiv:2610.01721v1 Announce Type: new Abstract: Monitoring the Pantograph-Catenary System (PCS) provides insight into the health conditions of the pantograph and the railway infrastructure. Recent ind

AVSD-Scenes: A Dataset for Audio-Visual Description of Urban Scenes

arXiv:2610.01861v1 Announce Type: new Abstract: Natural language descriptions can provide rich semantic representations of audio-visual urban scenes, yet datasets that jointly describe both auditory a

Before Agents Decide: Epistemic Action in LLM-Based Systems

arXiv:2610.00511v1 Announce Type: new Abstract: Before a difficult decision, people often act simply to understand the situation better. We turn an object to see another side, place alternatives next

agentsarxiv-cs-ai

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

arXiv:2610.01116v1 Announce Type: new Abstract: As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the mach

safetyarxiv-cs-ai