All entries

arXiv:2512.07371v3 Announce Type: replace-cross Abstract: Behavior-cloning based visuomotor policies enable precise manipulation but often inherit the slow, cautious tempo of human demonstrations, lim

applicationsarxiv-cs-ai

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach

DGX agent

arXiv:2601.18399v2 Announce Type: replace Abstract: Separating liquid-liquid dispersions in gravity settlers is critical in chemical, pharmaceutical, and recycling processes. The dense-packed zone hei

model-releasesarxiv-cs-lg

28 Apr 2026

EU countries and lawmakers reach an impasse on a deal watering down the EU's AI Act due to some parties seeking exemptions for already regulated industries (Foo Yun Chee/Reuters)

Foo Yun Chee / Reuters: EU countries and lawmakers reach an impasse on a deal watering down the EU's AI Act due to some parties seeking exemptions for already regulated industries — EU countries and E

safetytechmeme

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604.23466v1 Announce Type: cross Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while ret

Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards

arXiv:2604.23341v1 Announce Type: cross Abstract: The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exp

Evaluating Language Models' Evaluations of Games

arXiv:2510.10930v2 Announce Type: replace-cross Abstract: Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial

Evaluating Large Language Models on Computer Science University Exams in Data Structures

arXiv:2604.23347v1 Announce Type: new Abstract: We present a comprehensive evaluation of Large Language Models (LLMs) on Computer Science (CS) Data Structure examination questions. Our work introduces

Evaluating Remote Sensing Image Captions Beyond Metric Biases

arXiv:2604.22855v1 Announce Type: new Abstract: The core objective of image captioning is to achieve lossless semantic compression from visual signals into textual modalities. However, the reliance on

researcharxiv-cs-cv

Evaluating Temporal Consistency in Multi-Turn Language Models

arXiv:2604.23051v1 Announce Type: new Abstract: Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios,

Evaluating the Search Agent in a Parallel World

arXiv:2603.04751v2 Announce Type: replace Abstract: Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, ev

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models

arXiv:2510.02629v3 Announce Type: replace Abstract: Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, r

researcharxiv-cs-cl

Evaluation of Pose Estimation Systems for Sign Language Translation

arXiv:2604.24609v1 Announce Type: new Abstract: Many sign language translation (SLT) systems operate on pose sequences instead of raw video to reduce input dimensionality, improve portability, and par

researcharxiv-cs-cl

Evaluation of Prompt Injection Defenses in Large Language Models

arXiv:2604.23887v1 Announce Type: cross Abstract: LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that

researcharxiv-cs-ai

Event-based SLAM Benchmark for High-Speed Maneuvers

model-releasesarxiv-cs-ro

arXiv:2604.24033v1 Announce Type: new Abstract: Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution,

Evolve: A Persistent Knowledge Lifecycle for Small Language Models

arXiv:2604.23424v1 Announce Type: cross Abstract: Evolve pairs a small local language model with a persistent, teacher-compiled knowledge store -- refined through sleep consolidation and usage-driven

EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment

arXiv:2604.22842v1 Announce Type: new Abstract: Face Image Quality Assessment is crucial for reliable face recognition systems, yet existing Vision Transformer-based approaches rely exclusively on fin

Local Ai

EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT

arXiv:2604.24146v1 Announce Type: new Abstract: Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imagin

local-aiarxiv-cs-cv

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

arXiv:2406.14075v2 Announce Type: replace Abstract: It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, financ

Excited to support @NVIDIA Nemotron 3 Nano Omni, now available on Fireworks. It's the first open model that handles vision, audio, video, an…

model-releasesfireworks-ai--x

Excited to support @NVIDIA Nemotron 3 Nano Omni, now available on Fireworks. It's the first open model that handles vision, audio, video, and text in a single inference loop. Built for multimodal sub-

Applications

Expert Evaluation of LLM's Open-Ended Legal Reasoning on the Japanese Bar Exam Writing Task

arXiv:2604.23730v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capaci

applicationsarxiv-cs-ai

Tutorials

Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook

arXiv:2406.05984v2 Announce Type: replace-cross Abstract: Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In t

tutorialsarxiv-cs-ai

Tutorials

Explainable AI in Speaker Recognition -- Making Latent Representations Understandable

arXiv:2604.23354v1 Announce Type: cross Abstract: Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Expl

tutorialsarxiv-cs-ai

Tutorials

Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review

arXiv:2504.10527v2 Announce Type: replace Abstract: Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numero

tutorialsarxiv-cs-ai

Explaining Sources of Uncertainty in Automated Fact-Checking

arXiv:2505.17855v2 Announce Type: replace Abstract: Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using

researcharxiv-cs-cl

Local Ai

Explaining Temporal Graph Predictions With Shapley Values

arXiv:2604.24078v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both s

local-aiarxiv-cs-lg

Explanation Quality Assessment as Ranking with Listwise Rewards

arXiv:2604.24176v1 Announce Type: new Abstract: We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single 'b

Explicit integral representations and quantitative bounds for two-layer ReLU networks

arXiv:2604.23260v1 Announce Type: cross Abstract: An approach to construct explicit integral representations for two-layer ReLU networks is presented, which provides relatively simple representations

Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems

arXiv:2604.24706v1 Announce Type: cross Abstract: Learning-based control techniques use data from past trajectories to control systems with uncertain dynamics. However, learning-based controllers are

Exploring Audio Hallucination in Egocentric Video Understanding

arXiv:2604.23860v1 Announce Type: cross Abstract: Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly whe

researcharxiv-cs-ai

Exploring Hierarchical Consistency and Unbiased Objectness for Open-Vocabulary Object Detection

arXiv:2604.23344v1 Announce Type: new Abstract: Conventional object detectors typically operate under a closed-set assumption, limiting recognition to a predefined set of base classes seen during trai

Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency

arXiv:2501.02673v4 Announce Type: replace Abstract: Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determin

Exploring the Secondary Risks of Large Language Models

arXiv:2506.12382v5 Announce Type: replace-cross Abstract: Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical application

Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors

arXiv:2603.21768v3 Announce Type: replace-cross Abstract: Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of

Extreme bandits

arXiv:2604.24545v1 Announce Type: cross Abstract: In many areas of medicine, security, and life sciences, we want to allocate limited resources to different sources in order to detect extreme values.

safetyarxiv-cs-lg

EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration

arXiv:2604.23562v1 Announce Type: cross Abstract: The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language

researcharxiv-cs-ai

Local Ai

Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising

arXiv:2604.24104v1 Announce Type: new Abstract: Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issu

local-aiarxiv-cs-cl

Failure-Centered Runtime Evaluation for Deployed Trilingual Public-Space Agents

arXiv:2604.23990v1 Announce Type: new Abstract: This paper presents PSA-Eval, a failure-centered runtime evaluation framework for deployed trilingual public-space agents. The central claim is that, wh

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment

arXiv:2604.23786v1 Announce Type: new Abstract: In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental healt

FastAT Benchmark: A Comprehensive Framework for Fair Evaluation of Fast Adversarial Training Methods

arXiv:2604.22853v1 Announce Type: new Abstract: Fast Adversarial Training (FastAT) seeks to achieve adversarial robustness at a fraction of the computational cost incurred by standard multi-step metho

FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data

arXiv:2604.24572v1 Announce Type: new Abstract: The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), maintained by the Observational Health Data Sciences and Informatics (OHDSI

FDIM: A Feature-distance-based Generic Video Quality Metric for Versatile Codecs

arXiv:2604.24123v1 Announce Type: new Abstract: Video technology is advancing toward Ultra High Definition (UHD) and High Dynamic Range (HDR), which intensifies the need for higher compression efficie

Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation

arXiv:2604.24103v1 Announce Type: new Abstract: Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from

Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization

arXiv:2604.22885v1 Announce Type: cross Abstract: Federated cross-modal retrieval faces severe challenges from heterogeneous client data, particularly non-IID semantic distributions and missing modali

Applications

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning

arXiv:2506.23210v5 Announce Type: replace-cross Abstract: Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, data and syste

applicationsarxiv-cs-ai

FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection

arXiv:2604.24012v1 Announce Type: new Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algo

feels like forever ago, but had to include openclaw in q1 trends coding models improved greatly in Q4 of 2025, early jan was ppl running cla…

model-releasesyohei-nakajima--x

feels like forever ago, but had to include openclaw in q1 trends coding models improved greatly in Q4 of 2025, early jan was ppl running claude codes in parallel, and clawdbot blew up late jan models

Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware

arXiv:2604.24397v1 Announce Type: cross Abstract: In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error m