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Evaluating Intellectual Property Guardrails of Generative Image Models: A Technical Report

arXiv:2607.02582v1 Announce Type: new Abstract: Generative image models are capable of producing images that bear a strong resemblance to, or replicate, recognizable intellectual property (IP). In thi

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Evaluating Large Language Models for Antisemitic Incident Classification

DGX agent

arXiv:2607.04890v1 Announce Type: new Abstract: Addressing hate and violence in society requires timely detection of hateful events from public reporting, but automated identification of hateful event

model-releasesarxiv-cs-cl

7 Jul 2026

Evaluating LLM-Based Regression Test Generation

arXiv:2501.11086v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-t

Evaluating LLM Uncertainty in Long-Form Generation Using Deterministic Ground Truth

arXiv:2607.03870v1 Announce Type: new Abstract: As LLMs generate increasingly long outputs, effective uncertainty estimation must identify errors at fine-grained levels rather than discard entire resp

Evaluating the Effect of Linguistic Relatedness on Cross-Lingual Transfer in Large Multilingual Automatic Speech Recognition

arXiv:2607.04814v1 Announce Type: cross Abstract: Extending automatic speech recognition (ASR) to low-resource African languages is constrained by the prohibitive demands of data collection at scale.

Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

arXiv:2607.02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary

EVAS: Efficient Multimodal Temporal Forgery Localization via Audio-Visual Synergy and Steered Boundary Calibration

arXiv:2607.04472v1 Announce Type: new Abstract: The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classificatio

Event Detection in Videos: A Framework for the Development of New Methods

arXiv:2607.04372v1 Announce Type: new Abstract: Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-l

researcharxiv-cs-cv

EventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization

arXiv:2607.04872v1 Announce Type: cross Abstract: Reasoning temporal localization (RTL) requires a model to generate an answer that itself contains the time interval supporting it, so high-level reaso

EvoAgentBench: Benchmarking Agent Self-Evolution via Ability Transfer

arXiv:2607.05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for

Evolutionary Guided Decoding: Iterative Value Refinement for LLMs

arXiv:2503.02368v4 Announce Type: replace-cross Abstract: While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs wit

safetyarxiv-cs-ai

Tutorials

EvoOtter: Evolutionary Reproduction Test Generator

arXiv:2607.02854v1 Announce Type: cross Abstract: Before fixing an issue, it is useful to first reproduce it by generating a bug reproduction test (BRT). However, generating a BRT is itself a challeng

tutorialsarxiv-cs-lg

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512.22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biologi

Exp2VLA: Enabling Vision-Language-Action for Drone Navigation from Expert Demonstrations

arXiv:2607.03146v1 Announce Type: new Abstract: Vision-language-action (VLA) models open a new path toward intuitive robot control by directly linking perception, language, and action in a single end-

safetyarxiv-cs-ro

Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data

arXiv:2607.04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.17 mental-health professionals per 100,000 population and only six child psychiatrists nationwide. No Bengali-language, cu

Tutorials

Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks

arXiv:2503.10496v2 Announce Type: replace-cross Abstract: Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from

tutorialsarxiv-cs-ai

Explainable Flood Segmentation on Sentinel-1 SAR1 Imagery Using CNN and Transformer Architectures

arXiv:2606.16302v2 Announce Type: replace Abstract: Rapid and accurate flood prediction is essential for disaster response and mitigation planning. Synthetic Aperture Radar (SAR) sensors in satellites

Explainable Novel Category Discovery in Semantic Concept Space

arXiv:2607.04548v1 Announce Type: cross Abstract: Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing meth

Explainable Reinforcement Learning for Adaptive Traffic Signal Control

arXiv:2607.03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traf

safetyarxiv-cs-ai

Agents

Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning

arXiv:2601.21523v2 Announce Type: replace Abstract: To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards th

agentsarxiv-cs-lg

Exploring Context-aware and LLM-driven Locomotion for Immersive Virtual Reality

arXiv:2504.17331v3 Announce Type: replace-cross Abstract: Locomotion plays a crucial role in shaping the user experience within virtual reality environments. In particular, hands-free locomotion offer

Exploring Convolutional Neural Processes for Weather Downscaling

arXiv:2607.04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularl

local-aiarxiv-cs-lg

Applications

Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity

arXiv:2607.03754v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) target segmentation remains challenging due to the small size of objects, appearance variations, cluttered backgrounds, an

applicationsarxiv-cs-cv

Exploring the Rashomon Set for Concept-Based Models

arXiv:2511.19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on funda

ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion

arXiv:2607.03110v1 Announce Type: new Abstract: Multi-Exposure Fusion (MEF) effectively extends dynamic range, but practical deployment is hindered by motion-induced ghosting and the scarcity of high-

Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens

arXiv:2508.04928v5 Announce Type: replace-cross Abstract: We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images. Despite being

safetyarxiv-cs-ai

Tutorials

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention

arXiv:2508.16771v3 Announce Type: replace-cross Abstract: Code Language Models (CodeLLMs) learn token importance from data correlations, whereas human developers attend selectively to semantically sal

tutorialsarxiv-cs-ai

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

model-releasesarxiv-cs-lg

arXiv:2607.04698v1 Announce Type: new Abstract: The rapid proliferation of Internet of things (IoT) devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preser

Failures and Successes to Learn a Core Conceptual Distinction from the Statistics of Language

arXiv:2607.04523v1 Announce Type: cross Abstract: Generic statements like 'tigers are striped' and 'cars have radios' communicate information that is, in general, true. However, while the first statem

Fair-GPTQ: Bias-Aware Quantization for Large Language Models

arXiv:2509.15206v3 Announce Type: replace Abstract: The high memory demands of generative language models have drawn attention to quantization, which reduces memory usage by mapping model weights to l

safetyarxiv-cs-cl

FairFlow: Demystifying and Mitigating Stereotype Bias in Text-to-Image Diffusion Transformers

arXiv:2607.03180v1 Announce Type: new Abstract: Multimodal diffusion transformers (MM-DiTs) have emerged as the prevalent backbone for modern text-to-image generation systems. However, they exhibit cr

Faithfulness to Refusal: A Causal Audit of Neuron Selectors

arXiv:2607.05355v1 Announce Type: new Abstract: Attribution scores increasingly identify which neuron rows of a language model matter for applications such as pruning, interpretability, and editing fo

safetyarxiv-cs-cl

Agents

Fast 3D Foundation Model Initialized Gaussian Splatting

arXiv:2607.03209v1 Announce Type: new Abstract: This paper introduces a fast method for high-quality 3D Gaussian Splatting (3DGS) reconstruction without traditional Structure-from-Motion (SfM). The pr

agentsarxiv-cs-cv

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

arXiv:2607.05095v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis,

local-aiarxiv-cs-lg

Hardware

Fast Asymptotically Optimal Kinodynamic Planning via Vectorization

arXiv:2607.03987v1 Announce Type: new Abstract: Sampling-based motion planners have been shown to be effective for systems with complex kinodynamic constraints and high dimensionality. However, these

hardwarearxiv-cs-ro

Fast, Parallel, Query-Efficient Binary Classification

arXiv:2607.04062v1 Announce Type: cross Abstract: We study the fundamental classification problem of computing a separating hyperplane for a binary-labeled dataset of size n with normalized d-dimensio

FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms

arXiv:2508.02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics. However, CSP remains ch

FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction

arXiv:2607.03822v1 Announce Type: new Abstract: Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation. Current paradigms predominantly utilize explicit physical

local-aiarxiv-cs-cv

Feature weighting for data analysis via evolutionary simulation

arXiv:2511.06454v3 Announce Type: replace-cross Abstract: We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. The algo

FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity

arXiv:2607.03763v1 Announce Type: cross Abstract: Federated Transformer training increasingly relies on local AdamW, whose adaptive updates can provide much stronger local progress than SGD-based trai

FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy

arXiv:2607.03334v1 Announce Type: cross Abstract: The federated learning (FL) paradigm fosters distributed pervasive computing combined with artificial intelligence techniques, allowing for optimized

local-aiarxiv-cs-ai

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data

arXiv:2607.02636v1 Announce Type: cross Abstract: Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, o

local-aiarxiv-cs-ai

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering

arXiv:2607.04170v1 Announce Type: new Abstract: Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to clien

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

arXiv:2607.04158v1 Announce Type: cross Abstract: Federated learning (FL) is severely hindered by statistical heterogeneity due to variations in scanners, acquisition protocols, and patient population

FedSPM: Routing-Enabled Federated Learning under Dual Heterogeneity via Semiparametric Mixture

arXiv:2607.04085v1 Announce Type: cross Abstract: Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligen

local-aiarxiv-cs-ai

Feeling the Unexpected: ResTacVLA for Contact-Rich Manipulation via Residual Tactile Representation

arXiv:2607.03387v1 Announce Type: new Abstract: Tactile perception is indispensable for contact-rich manipulation, yet integrating it into Vision-Language-Action (VLA) models often induces modality co

researcharxiv-cs-ro

Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems

arXiv:2510.15686v2 Announce Type: replace Abstract: Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial tr

safetyarxiv-cs-ro

Fidelity-Diversity Metrics for Text

arXiv:2607.04563v1 Announce Type: new Abstract: As language modeling technology matures, there is an increasing research focus on the composition and curation of datasets used to train these models. F

researcharxiv-cs-cl