Search: “local-ai”

AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis

arXiv:2607.07553v1 Announce Type: new Abstract: Accurate tumour localization and diagnosis is a critical component of clinical care for brain cancers. Magnetic Resonance Imaging (MRI) is the most comm

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Activation Quantization of Vision Encoders Needs Prefixing Registers

DGX agent

arXiv:2510.04547v5 Announce Type: replace-cross Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-lang

local-aiarxiv-cs-cv

9 Jul 2026

ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation

arXiv:2607.07640v1 Announce Type: cross Abstract: Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within t

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

arXiv:2607.07033v1 Announce Type: cross Abstract: Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are

C-DeltaTheta: Circuit-Restricted Weight Arithmetic for Selective Refusal

arXiv:2602.04521v2 Announce Type: replace Abstract: Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compu

Context-Aware Slum Mapping in Sub-Saharan Africa Using Sentinel-1 Texture and Local Climate Zones

arXiv:2607.07532v1 Announce Type: new Abstract: Accurate mapping of informal settlements remains a major challenge in Sub-Saharan African (SSA) cities because optical imagery often fails to distinguis

DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting

arXiv:2607.07409v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash furthe

Distributed Sparse Interventions in Language Models

arXiv:2607.07128v1 Announce Type: new Abstract: Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple

ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening

arXiv:2607.07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG

EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning

arXiv:2607.07187v1 Announce Type: new Abstract: Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of int

Format-Controlled Multi-Scale JPEG Compression Response Analysis for Image-Level Forgery Screening

arXiv:2607.06615v1 Announce Type: cross Abstract: Image forgery detection is a critical task in digital forensics, yet many deep-learning localization approaches are typically GPU-accelerated and comp

MegaFlow: Zero-Shot Large Displacement Optical Flow

arXiv:2603.25739v2 Announce Type: replace Abstract: Accurate estimation of large displacement optical flow remains a critical challenge. Existing methods typically rely on iterative local search or/an

Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency

arXiv:2607.07207v1 Announce Type: cross Abstract: We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.2), rapid

Multiplication Beyond Groups: Stratified Fourier Mechanisms in Transformer Circuits

arXiv:2607.07066v1 Announce Type: cross Abstract: Transformers have demonstrated a remarkable ability to learn algorithmic reasoning, yet mechanistic analyses have mostly focused on globally invertibl

Naming the Concepts Classifiers Rely On: Language-Anchored Decomposition for Faithful Explanation

arXiv:2607.07264v1 Announce Type: new Abstract: Deep neural networks are widely deployed in high-stakes visual applications where interpretability is critical, yet existing explanations face a trade-o

Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis

arXiv:2607.07077v1 Announce Type: cross Abstract: Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-commun

NEST: Tackling Dataset-Level Distribution Shifts via Regime-Oriented Mixture-of-Experts

arXiv:2607.06607v1 Announce Type: cross Abstract: Accurate long-term forecasting in complex systems is frequently compromised by dataset-level distribution shifts, where diverse underlying behavioral

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing use

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

arXiv:2607.06889v1 Announce Type: cross Abstract: Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly whi

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

arXiv:2607.06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation, w

Robust Federated Learning Under Real-World Client Churn

arXiv:2607.06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refr

Segmenting Low-Contrast XCTs of Concrete: An Unsupervised Approach

arXiv:2603.00127v2 Announce Type: replace Abstract: X-Ray Computed Tomography (XCT) is a compelling tool in experimental mechanics, capable of non-destructively extracting information pertaining to th

Smart Scissor: Coupling Spatial Redundancy Reduction and CNN Compression for Embedded Hardware

arXiv:2607.06915v1 Announce Type: new Abstract: Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising fo

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP

arXiv:2607.07135v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to

TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation

arXiv:2607.07287v1 Announce Type: new Abstract: Dexterous manipulation in everyday environments requires both anticipation and reaction: a robot must predict how contact should evolve while rapidly co

local-aiarxiv-cs-ro

TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation

arXiv:2607.06601v1 Announce Type: cross Abstract: Conditional computation can decouple language model quality from per-token inference cost, yet leading techniques act on a single axis in isolation: M

Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities

arXiv:2607.07277v1 Announce Type: new Abstract: Harmful online communication often contains slang, coded terms, abbreviations, and community-specific expressions, which make messages difficult to inte

URS-Stereo: Uncertainty-Guided Residual Search for Real-Time Stereo Matching

arXiv:2607.06779v1 Announce Type: new Abstract: Real-time stereo matching is crucial for robotics, autonomous systems, and embedded vision applications, where both computational efficiency and dispari

When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems

arXiv:2607.06807v1 Announce Type: cross Abstract: While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities

When Do Geometric Algebra Layers Beat Scalarization? A Controlled Study on SO(3)-Equivariant Vector Laws

arXiv:2607.06634v1 Announce Type: new Abstract: Compact networks built from Clifford algebra Cl(3,0) primitives are exactly SO(3)-equivariant and learn synthetic 3D vector laws from few samples. We as

When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning

arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise

Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors

arXiv:2607.07216v1 Announce Type: new Abstract: Deepfake (DF) technology poses a significant threat to information integrity, driving the need for robust detection methods. Most DF detectors only cons

AEGIS: A Mechanism-Guided Defense against Visual Synonym Jailbreaks in Text-to-Image Models

arXiv:2607.06120v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved high visual fidelity and broad adoption, but remain vulnerable to safety violations when adversaries exploi

Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders

arXiv:2607.06445v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly utilized as the conditioning backbone for diffusion-based image editing due to their remarkable multimo

AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring

arXiv:2607.05859v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are promising for construction-site monitoring, and recent construction-tailored VLMs have primarily adapted pretrained VL

CHARLIE: An On-Premise Multi-Agent Retrieval-Augmented Generation System for Evidential Reasoning in Forensic Science

arXiv:2607.05428v1 Announce Type: cross Abstract: We present Charlie, an on-premise multi-agent Retrieval-Augmented Generation (RAG) system for structured evidential processing in digital forensic env

Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices

arXiv:2607.05933v1 Announce Type: cross Abstract: Dynamic Voltage Frequency Scaling (DVFS) on resource-constrained embedded GPU platforms is essential for energy-efficient small language model (SLM) f

Freqformer: Image-Demoireing Transformer via Effective Frequency Decomposition

arXiv:2505.19120v2 Announce Type: replace Abstract: Image demoireing remains a challenging task due to the complex interplay between texture corruption and color distortions caused by moire patterns.

High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control

arXiv:2607.06162v1 Announce Type: new Abstract: Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on

Higher-Order Certified Robustness for Regression

arXiv:2607.05536v1 Announce Type: cross Abstract: Randomized smoothing has emerged as a scalable technique for certifying the adversarial robustness of classifiers. However, its application to regress

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization

arXiv:2508.01725v5 Announce Type: replace-cross Abstract: Recent advances in continuous conditional generative modeling, including Continuous conditional Generative Adversarial Network (CcGAN) and Con

LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference

arXiv:2607.06111v1 Announce Type: cross Abstract: Industrial prediction and soft sensing depend on credible input measurements. In field deployment, a predictor may receive biased, delayed, stale, or

Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability

arXiv:2607.06127v1 Announce Type: new Abstract: We present LLM4SDM, the first study of open-source smaller language models (OS-sLLMs) for automated assessment of shared decision making (SDM) using the

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

arXiv:2409.06067v3 Announce Type: replace Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of t

NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds

arXiv:2411.17392v3 Announce Type: replace Abstract: Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advanc

Patch Knowledge Transfer for Efficient AI-Generated Image Quality Assessment

arXiv:2607.05605v1 Announce Type: new Abstract: With the rapid advancement of image generation technologies, perceptual quality assessment of AI-generated images has emerged as a crucial research dire

Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors

arXiv:2607.05663v1 Announce Type: cross Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments. While fusing Inertial Measurement Unit (IMU)

Propose and Attend: Training-free MLLM Grounding Confidence via Multi-Token Localized Attention

arXiv:2607.05978v1 Announce Type: cross Abstract: Multimodal large language models can emit localized predictions, bounding boxes for objects and temporal windows for video and audio events, but they