Search: “local-ai”

Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision

arXiv:2604.09537v1 Announce Type: cross Abstract: Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the prov

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Local Ai

FaceLiVTv2: An Improved Hybrid Architecture for Efficient Mobile Face Recognition

DGX agent

arXiv:2604.09127v1 Announce Type: new Abstract: Lightweight face recognition is increasingly important for deployment on edge and mobile devices, where strict constraints on latency, memory, and energ

local-aiarxiv-cs-cv

13 Apr 2026

From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection

arXiv:2604.09327v1 Announce Type: new Abstract: Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations.

Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data

arXiv:2604.08581v1 Announce Type: new Abstract: This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression

arXiv:2506.03074v5 Announce Type: replace-cross Abstract: We present `GL-LowPopArt`, a novel Catoni-style estimator for generalized low-rank trace regression. Building on `LowPopArt` (Jang et al., 202

LADR: Locality-Aware Dynamic Rescue for Efficient Text-to-Image Generation with Diffusion Large Language Models

arXiv:2603.13450v2 Announce Type: replace-cross Abstract: Discrete Diffusion Language Models have emerged as a compelling paradigm for unified multimodal generation, yet their deployment is hindered b

Mapping generative AI use in the human brain: divergent neural, academic, and mental health profiles of functional versus socio emotional AI use

arXiv:2604.08594v1 Announce Type: cross Abstract: The widespread adoption of generative artificial intelligence conversational agents (AICAs) among university students constitutes a novel cognitive so

MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification

arXiv:2604.08868v1 Announce Type: cross Abstract: To ensure safe clinical integration, deep learning models must provide more than just high accuracy; they require dependable uncertainty quantificatio

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution

arXiv:2502.06809v3 Announce Type: replace-cross Abstract: Pervasive polysemanticity in large language models (LLMs) undermines discrete neuron-concept attribution, posing a significant challenge for m

Off-the-shelf Vision Models Benefit Image Manipulation Localization

arXiv:2604.09096v1 Announce Type: new Abstract: Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differen

Offline-First LLM Architecture for Adaptive Learning in Low-Connectivity Environments

arXiv:2603.03339v5 Announce Type: replace-cross Abstract: Artificial intelligence (AI) and large language models (LLMs) are transforming educational technology by enabling conversational tutoring, per

Online3R: Online Learning for Consistent Sequential Reconstruction Based on Geometry Foundation Model

arXiv:2604.09480v1 Announce Type: new Abstract: We present Online3R, a new sequential reconstruction framework that is capable of adapting to new scenes through online learning, effectively resolving

Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing

arXiv:2604.09499v1 Announce Type: new Abstract: Autonomous racing without prebuilt maps is a grand challenge for embedded robotics that requires kinodynamic planning from instantaneous sensor data at

local-aiarxiv-cs-ro

Process Reward Agents for Steering Knowledge-Intensive Reasoning

arXiv:2604.09482v1 Announce Type: new Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating ste

QCFuse: Query-Centric Cache Fusion for Efficient RAG Inference

arXiv:2604.08585v1 Announce Type: cross Abstract: Cache fusion accelerates generation process of LLMs equipped with RAG through KV caching and selective token recomputation, thereby reducing computati

Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models

arXiv:2505.12509v3 Announce Type: replace-cross Abstract: Post-hoc explanations provide transparency and are essential for guiding model optimization, such as prompt engineering and data sanitation. H

Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors

arXiv:2604.09366v1 Announce Type: new Abstract: Reconstructing dynamic 4D scenes is an important yet challenging task. While 3D foundation models like VGGT excel in static settings, they often struggl

Statistical Properties of the King Wen Sequence: An Anti-Habituation Structure That Does Not Improve Neural Network Training

arXiv:2604.09234v1 Announce Type: cross Abstract: The King Wen sequence of the I-Ching (c. 1000 BC) orders 64 hexagrams -- states of a six-dimensional binary space -- in a pattern that has puzzled sch

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio

Tango: Taming Visual Signals for Efficient Video Large Language Models

arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th

Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

arXiv:2604.08726v1 Announce Type: new Abstract: Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localizatio

local-aiarxiv-cs-ro

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting

arXiv:2604.09067v1 Announce Type: new Abstract: Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is

TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches

arXiv:2604.08945v1 Announce Type: new Abstract: Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is

Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment

arXiv:2604.06289v1 Announce Type: cross Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at

Auditing Black-Box LLM APIs with a Rank-Based Uniformity Test

arXiv:2506.06975v5 Announce Type: replace-cross Abstract: As API access becomes a primary interface to large language models (LLMs), users often interact with black-box systems that offer little trans

Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding

arXiv:2604.08014v1 Announce Type: new Abstract: Spatio-Temporal Video Grounding requires jointly localizing target objects across both temporal and spatial dimensions based on natural language queries

CBM-Dual: A 65-nm Fully Connected Chaotic Boltzmann Machine Processor for Dual Function Simulated Annealing and Reservoir Computing

arXiv:2604.06808v1 Announce Type: cross Abstract: This paper presents CBM-Dual, the first silicon-proven digital chaotic dynamics processor (CDP) supporting both simulated annealing (SA) and reservoir

Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systems

arXiv:2604.07304v1 Announce Type: cross Abstract: Large Language Models (LLMs) challenge conventional automated programming assessment because students can now produce functionally correct code withou

CHiQPM: Calibrated Hierarchical Interpretable Image Classification

arXiv:2511.20779v2 Announce Type: replace Abstract: Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local

Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs

arXiv:2603.20698v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis. However, their application in gastr

Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand

arXiv:2604.06198v1 Announce Type: cross Abstract: The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure

ConfusionPrompt: Practical Private Inference for Online Large Language Models

arXiv:2401.00870v5 Announce Type: replace-cross Abstract: State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud

Data Selection for Multi-turn Dialogue Instruction Tuning

arXiv:2604.07892v1 Announce Type: new Abstract: Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsiste

Differentially Private Best-Arm Identification

arXiv:2406.06408v2 Announce Type: replace-cross Abstract: Best Arm Identification (BAI) problems are progressively used for data-sensitive applications, such as designing adaptive clinical trials, tun

EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration

arXiv:2604.07003v1 Announce Type: new Abstract: Large language models (LLMs) has been widely used for automated negotiation, but their high computational cost and privacy risks limit deployment in pri

Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach

arXiv:2604.06838v1 Announce Type: new Abstract: In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learnin

Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC

arXiv:2512.24290v2 Announce Type: replace-cross Abstract: Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rar

FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shift

arXiv:2604.06795v1 Announce Type: cross Abstract: Federated Learning (FL) enables decentralized model training across multiple clients without exposing private data, making it ideal for privacy-sensit

FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization

arXiv:2604.06833v1 Announce Type: cross Abstract: As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving p

From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion

arXiv:2603.26588v2 Announce Type: replace-cross Abstract: We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth

Governance and Regulation of Artificial Intelligence in Developing Countries: A Case Study of Nigeria

arXiv:2604.06018v2 Announce Type: replace-cross Abstract: This study examines the perception of legal professionals on the governance of AI in developing countries, using Nigeria as a case study. The

Hallucination Detection and Evaluation of Large Language Model

arXiv:2512.22416v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs) pose a significant challenge, generating misleading or unverifiable content that undermines trust and

HQF-Net: A Hybrid Quantum-Classical Multi-Scale Fusion Network for Remote Sensing Image Segmentation

arXiv:2604.06715v1 Announce Type: cross Abstract: Remote sensing semantic segmentation requires models that can jointly capture fine spatial details and high-level semantic context across complex scen

Learning to Query History: Nonstationary Classification via Learned Retrieval

arXiv:2604.07027v1 Announce Type: new Abstract: Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout

Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models

arXiv:2601.04068v3 Announce Type: replace Abstract: Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization

arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz

MSGL-Transformer: A Multi-Scale Global-Local Transformer for Rodent Social Behavior Recognition

arXiv:2604.07578v1 Announce Type: new Abstract: Recognition of rodent behavior is important for understanding neural and behavioral mechanisms. Traditional manual scoring is time-consuming and prone t

NativeTernary: A Self-Delimiting Binary Encoding with Unary Run-Length Hierarchy Markers for Ternary Neural Network Weights, Structured Data, and General Computing Infrastructure

arXiv:2604.03336v2 Announce Type: replace Abstract: BitNet b1.58 (Ma et al., 2024) demonstrates that large language models can operate entirely on ternary weights {-1, 0, +1}, yet no native binary wir