Search: “local-ai”

LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters

arXiv:2605.13163v1 Announce Type: cross Abstract: Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model recov

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MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning

DGX agent

arXiv:2605.12528v1 Announce Type: cross Abstract: As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly challe

local-aiarxiv-cs-ai

14 May 2026

PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution

arXiv:2605.13027v1 Announce Type: new Abstract: Text image super-resolution (Text-SR) requires more than visually plausible detail synthesis: slight errors in stroke topology may alter character ident

PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models

arXiv:2605.12835v1 Announce Type: new Abstract: Large language models can extract local causal claims from text, but those claims become more useful when organized as persistent, navigable world model

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

arXiv:2605.13434v1 Announce Type: new Abstract: Asynchronous stochastic gradient descent (ASGD) is a standard way to exploit heterogeneous compute resources in distributed learning: instead of forcing

Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle

arXiv:2605.13021v1 Announce Type: cross Abstract: Graph coarsening is a graph dimensionality reduction technique that aims to construct a smaller and more tractable graph while preserving the essentia

Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection

arXiv:2605.12506v1 Announce Type: cross Abstract: Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when consid

Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry

arXiv:2605.13772v1 Announce Type: cross Abstract: Large language models hallucinate during multi-step reasoning, but most existing detectors operate at the trace level: they assign one confidence scor

A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory

arXiv:2605.11847v1 Announce Type: cross Abstract: Analog content-addressable memories (aCAMs) based on memristors provide a promising pathway toward energy-efficient large-scale associative computing

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

arXiv:2605.11222v1 Announce Type: new Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ)

CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating

arXiv:2605.11723v1 Announce Type: new Abstract: In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it

COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication

arXiv:2605.11165v1 Announce Type: new Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.

DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

arXiv:2312.02549v2 Announce Type: replace-cross Abstract: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ t

Distributed Quantum Gaussian Processes for Multi-Agent Systems

arXiv:2602.15006v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale re

EchoTracker2: Enhancing Myocardial Point Tracking by Modeling Local Motion

arXiv:2605.12140v1 Announce Type: new Abstract: Myocardial point tracking (MPT) has recently emerged as a promising direction for motion estimation in echocardiography, driven by advances in general-p

EPIC: Efficient Predicate-Guided Inference-Time Control for Compositional Text-to-Image Generation

arXiv:2605.11722v1 Announce Type: new Abstract: Recent text-to-image (T2I) generators can synthesize realistic images, but still struggle with compositional prompts involving multiple objects, counts,

FAME: Feature Activation Map Explanation on Image Classification and Face Recognition

arXiv:2605.12017v1 Announce Type: new Abstract: Deep Learning has revolutionized machine learning, reaching unprecedented levels of accuracy, but at the cost of reduced interpretability. Especially in

FeatMap: Understanding image manipulation in the feature space and its implications for feature space geometry

arXiv:2605.11203v1 Announce Type: cross Abstract: Intermediate feature representations represent the backbone for the expressivity and adaptability of deep neural networks. However, their geometric st

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602.23638v2 Announce Type: replace Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a dis

FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers

arXiv:2512.07150v2 Announce Type: replace-cross Abstract: Deep generative models are powerful priors for imaging inverse problems, but training-free solvers for latent flow models face a practical fin

From Image Hashing to Scene Change Detection

arXiv:2605.12259v1 Announce Type: new Abstract: Image hashing provides compact representations for efficient storage and retrieval but is inherently limited to global comparison and cannot reason abou

Grounding by Remembering: Cross-Scene and In-Scene Memory for 3D Functional Affordances

arXiv:2605.11616v1 Announce Type: new Abstract: Functional affordance grounding requires more than recognizing an object: an agent must localize the specific region that supports an interaction, such

H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows

arXiv:2605.12252v1 Announce Type: new Abstract: Metal artifacts in computed tomography (CT) severely degrade image quality, compromising diagnostic accuracy and radiotherapy planning, especially in ca

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

arXiv:2605.11509v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control an

Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models

arXiv:2605.12258v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deplo

Keep What Audio Cannot Say: Context-Preserving Token Pruning for Omni-LLMs

arXiv:2605.11605v1 Announce Type: new Abstract: Omnimodal Large Language Models (Omni-LLMs) incur substantial computational overhead due to the large number of multimodal input tokens they process, ma

Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics

arXiv:2412.18594v3 Announce Type: replace Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamic

Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank

arXiv:2605.11272v1 Announce Type: new Abstract: Adobe Express is expanding internationally, but the US has a disproportionately large content supply and interaction volume. Learning-to-rank (LTR) mode

LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows

arXiv:2605.11368v1 Announce Type: new Abstract: We study the application of recent Edit Flows for inference-time reward control for DNA sequence generation. Unlike most reward-guided DNA generation fr

Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement

arXiv:2605.11808v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance on diverse vision-language tasks. However, LVLMs still suffer from hallucinati

MotionGRPO: Overcoming Low Intra-Group Diversity in GRPO-Based Egocentric Motion Recovery

arXiv:2605.05680v2 Announce Type: replace Abstract: This paper studies full-body 3D human motion recovery from head-mounted device signals. Existing diffusion-based methods often rely on global distri

Muon is Not That Special: Random or Inverted Spectra Work Just as Well

arXiv:2605.11181v1 Announce Type: new Abstract: The recent empirical success of the Muon optimizer has renewed interest in non-Euclidean optimization, typically justified by similarities with second-o

Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale

arXiv:2605.12343v1 Announce Type: new Abstract: Most learned PDE solvers follow a global-surrogate paradigm: a neural operator is trained to map full problem descriptions to full solution fields for a

Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario

arXiv:2605.11346v1 Announce Type: new Abstract: Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing t

PoseCompass: Intelligent Synthetic Pose Selection for Visual Localization

arXiv:2605.12144v1 Announce Type: new Abstract: In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-

PRISM: A Geometric Risk Bound that Decomposes Drift into Scale, Shape, and Head

arXiv:2605.11608v1 Announce Type: new Abstract: Comparing post-training LLM variants, such as quantized, LoRA-adapted, and distilled models, requires a diagnostic that identifies how a variant has dri

Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models

arXiv:2605.11142v1 Announce Type: new Abstract: Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, commu

Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction

arXiv:2605.05749v2 Announce Type: replace Abstract: Dense 3D reconstruction from continuous image streams requires both accurate geometric aggregation and stable long-term memory management. Recent fe

Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs

arXiv:2605.11128v1 Announce Type: new Abstract: Diversity is essential for language-model applications ranging from creative generation to scientific discovery, yet modern LLMs often collapse into a n

Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation

arXiv:2605.10988v1 Announce Type: new Abstract: Log anomaly detection is a critical task for system operations and security assurance. However, in networked systems at scale, log data are generated at

SOMA: Efficient Multi-turn LLM Serving via Small Language Model

arXiv:2605.11317v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in multi-turn dialogue settings where preserving conversational context across turns is essential

Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning

arXiv:2605.10985v1 Announce Type: new Abstract: Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their featu

TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection

arXiv:2605.12456v1 Announce Type: cross Abstract: We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generatio

A Physical Theory of Backpropagation: Exact Gradients from the Least-Action Principle

arXiv:2602.02281v2 Announce Type: replace-cross Abstract: Backpropagation is typically presented as a symbolic procedure: a backward pass topologically distinct from inference, with non-local error si

A probabilistic framework for crystal structure denoising, phase classification, and order parameters

arXiv:2512.11077v3 Announce Type: replace-cross Abstract: Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a

A Reconfigurable Multiplier Architecture for Error-Resilient Applications in RISC-V Core

arXiv:2605.08785v1 Announce Type: cross Abstract: Neural Networks (NNs) have been widely adopted due to their outstanding efficacy and adaptability across computer vision and deep learning application

A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment

arXiv:2605.06375v1 Announce Type: cross Abstract: Large language model (LLM) alignment via reinforcement learning from human preferences (RLHF) suffers from unstable policy updates, ambiguous gradient

Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration

arXiv:2605.09034v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization has become increasingly popular and important in fine-tuning large language models (LLMs), especially on edge devices due