All entries

arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen

hardwarearxiv-cs-lg

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion

DGX agent

arXiv:2604.18790v1 Announce Type: new Abstract: Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing m

model-releasesarxiv-cs-cv

22 Apr 2026

EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation

arXiv:2604.19105v1 Announce Type: new Abstract: Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has

EgoSelf: From Memory to Personalized Egocentric Assistant

arXiv:2604.19564v1 Announce Type: cross Abstract: Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhib

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval

arXiv:2604.17458v2 Announce Type: replace Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recen

Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment

model-releasesarxiv-cs-cl

arXiv:2604.19547v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering caus

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks

arXiv:2510.23156v2 Announce Type: replace-cross Abstract: The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition.

applicationsarxiv-cs-ai

Tutorials

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

arXiv:2507.01918v3 Announce Type: replace-cross Abstract: We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform hi

tutorialsarxiv-cs-ai

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling

model-releasesarxiv-cs-lg

arXiv:2509.03726v2 Announce Type: replace-cross Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions mu_{ext{target}}(x) propto exp(-E(x)/T), is fundamental to many

Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation

arXiv:2602.11631v2 Announce Type: replace-cross Abstract: Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but ar

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

arXiv:2604.19559v1 Announce Type: new Abstract: Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence rem

Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract

arXiv:2604.19505v1 Announce Type: cross Abstract: Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previou

researcharxiv-cs-cl

Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers

arXiv:2510.18358v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensemb

safetyarxiv-cs-cv

Environmental Sound Deepfake Detection Using Deep-Learning Framework

arXiv:2604.19652v1 Announce Type: cross Abstract: In this paper, we propose a deep-learning framework for environmental sound deepfake detection (ESDD) -- the task of identifying whether the sound sce

Epistemic orientation in parliamentary discourse is associated with deliberative democracy

arXiv:2604.19699v1 Announce Type: new Abstract: The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from

researcharxiv-cs-cl

Epistemic Skills: Reasoning about Knowledge and Oblivion

arXiv:2504.01733v4 Announce Type: replace Abstract: This paper presents a class of epistemic logics that captures the dynamics of acquiring knowledge and descending into oblivion, while incorporating

EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training

arXiv:2411.16312v2 Announce Type: replace Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth l

Error-free Training for MedMNIST Datasets

arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be

EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading

arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int

researcharxiv-cs-cl

Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks

arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work

Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection

arXiv:2604.19510v1 Announce Type: new Abstract: Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogra

applicationsarxiv-cs-cv

Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation

arXiv:2604.19331v1 Announce Type: new Abstract: Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of

safetyarxiv-cs-cl

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious

Local Ai

Evaluation-driven Scaling for Scientific Discovery

arXiv:2604.19341v1 Announce Type: cross Abstract: Language models are increasingly used in scientific discovery to generate hypotheses, propose candidate solutions, implement systems, and iteratively

local-aiarxiv-cs-ai

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge

arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

arXiv:2604.17406v2 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is

agentsarxiv-cs-ai

EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training

arXiv:2604.19485v1 Announce Type: cross Abstract: Reinforcement learning (RL) for LLM post-training faces a fundamental design choice: whether to use a learned critic as a baseline for policy optimiza

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603.15956v2 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (

Explicit Trait Inference for Multi-Agent Coordination

arXiv:2604.19278v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misa

agentsarxiv-cs-ai

Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents

arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio

agentsarxiv-cs-cv

Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation

arXiv:2604.19678v1 Announce Type: new Abstract: Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that

applicationsarxiv-cs-cl

Face Anything: 4D Face Reconstruction from Any Image Sequence

arXiv:2604.19702v1 Announce Type: new Abstract: Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and

Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck

arXiv:2601.12499v2 Announce Type: replace Abstract: Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which caus

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines

arXiv:2604.19468v1 Announce Type: cross Abstract: Fairness audits of institutional risk models are critical for understanding how deployed machine learning pipelines allocate resources. Drawing on mul

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition

arXiv:2604.19357v1 Announce Type: new Abstract: The evaluation of machine learning models typically relies mainly on performance metrics based on loss functions, which risk to overlook changes in perf

safetyarxiv-cs-lg

FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing

arXiv:2604.18644v1 Announce Type: cross Abstract: Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm

model-releasesarxiv-cs-cv

arXiv:2512.05791v2 Announce Type: replace-cross Abstract: Purpose: The Unadjusted Langevin Algorithm (ULA) in combination with diffusion models can generate high quality MRI reconstructions with uncer

Fast estimation of Gaussian mixture components via centering and singular value thresholding

arXiv:2604.19091v1 Announce Type: cross Abstract: Estimating the number of components is a fundamental challenge in unsupervised learning, particularly when dealing with high-dimensional data with man

FASTER: Value-Guided Sampling for Fast RL

arXiv:2604.19730v1 Announce Type: cross Abstract: Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampl

FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning

arXiv:2604.19729v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributi

safetyarxiv-cs-lg

Feasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model

arXiv:2604.18831v1 Announce Type: new Abstract: Frame-wise semantic segmentation of indoor lidar scans is a fundamental step toward higher-level 3D scene understanding and mapping applications. Howeve

agentsarxiv-cs-cv

Feature Perturbation Pool-based Fusion Network for Unified Multi-Class Industrial Defect Detection

arXiv:2604.19259v1 Announce Type: new Abstract: Multi-class defect detection constitutes a critical yet challenging task in industrial quality inspection, where existing approaches typically suffer fr

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion

arXiv:2604.19015v1 Announce Type: cross Abstract: Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring

FedSEA: Achieving Benefit of Parallelization in Federated Online Learning

arXiv:2604.19336v1 Announce Type: new Abstract: Online federated learning (OFL) has emerged as a popular framework for decentralized decision-making over continuous data streams without compromising c

FG^2-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control

arXiv:2604.19021v1 Announce Type: new Abstract: Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advanc

applicationsarxiv-cs-lg

Fine-Tuning Code Language Models to Detect Cross-Language Bugs

arXiv:2507.21954v2 Announce Type: replace-cross Abstract: Multilingual programming, which involves using multiple programming languages (PLs) in a single project, is increasingly common due to its ben

Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition

arXiv:2604.03476v2 Announce Type: replace-cross Abstract: Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable f