Search: “local-ai”

Local-Splitter: A Measurement Study of Seven Tactics for Reducing Cloud LLM Token Usage on Coding-Agent Workloads

arXiv:2604.12301v1 Announce Type: cross Abstract: We present a systematic measurement study of seven tactics for reducing cloud LLM token usage when a small local model can act as a triage layer in fr

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Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

DGX agent

arXiv:2601.14004v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However,

local-aiarxiv-cs-cl

15 Apr 2026

Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

arXiv:2604.12373v1 Announce Type: new Abstract: Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether larg

MoDora: Tree-Based Semi-Structured Document Analysis System

arXiv:2602.23061v3 Announce Type: replace-cross Abstract: Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and

PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning

arXiv:2604.12100v1 Announce Type: new Abstract: Whole-slide image (WSI) classification in computational pathology is commonly formulated as slide-level Multiple Instance Learning (MIL) with a single g

RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair

arXiv:2604.12820v1 Announce Type: new Abstract: Large language models (LLMs) inherently absorb harmful knowledge, misinformation, and personal data during pretraining on large-scale web corpora, with

Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalization

arXiv:2604.12768v1 Announce Type: new Abstract: Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local tr

Ride the Wave: Precision-Allocated Sparse Attention for Smooth Video Generation

arXiv:2604.12219v1 Announce Type: cross Abstract: Video Diffusion Transformers have revolutionized high-fidelity video generation but suffer from the massive computational burden of self-attention. Wh

Robust Federated Inference

arXiv:2510.00310v3 Announce Type: replace Abstract: Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to co

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

arXiv:2601.07177v2 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on imp

Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising

arXiv:2604.12600v1 Announce Type: new Abstract: The core challenge of hyperspectral image denoising is striking the right balance between data fidelity and noise prior modeling. Most existing methods

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

arXiv:2602.18109v2 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets. We present TempoNet, a reinforcement learning scheduler that pa

TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning

arXiv:2512.03963v3 Announce Type: replace Abstract: Enhancing the temporal understanding of Multimodal Large Language Models (MLLMs) is essential for advancing long-form video analysis, enabling tasks

The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems

arXiv:2502.07415v2 Announce Type: replace-cross Abstract: Inferring the mechanical properties of soft tissues from measured deformations is a fundamental challenge in elastography. A rarely examined a

Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection

arXiv:2508.05461v3 Announce Type: replace Abstract: Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their s

Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation

arXiv:2512.05812v5 Announce Type: replace-cross Abstract: Scalable multi-agent driving simulation requires behavior models that are both realistic and computationally efficient. We address this by opt

Towards Interpretable Foundation Models for Retinal Fundus Images

arXiv:2603.18846v2 Announce Type: replace Abstract: Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL

Uncertainty Guided Exploratory Trajectory Optimization for Sampling-Based Model Predictive Control

arXiv:2604.12149v1 Announce Type: new Abstract: Trajectory optimization depends heavily on initialization. In particular, sampling-based approaches are highly sensitive to initial solutions, and limit

local-aiarxiv-cs-ro

Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving

arXiv:2604.12208v1 Announce Type: cross Abstract: Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental result

VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization

arXiv:2604.12887v1 Announce Type: new Abstract: Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what

ViLL-E: Video LLM Embeddings for Retrieval

arXiv:2604.12148v1 Announce Type: new Abstract: Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Caption

VPTracker: Global Vision-Language Tracking via Visual Prompt

arXiv:2512.22799v2 Announce Type: replace Abstract: Vision-Language Tracking aims to continuously localize objects described by a visual template and a language description. Existing methods, however,

A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments

arXiv:2604.11146v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, F

A Modular Zero-Shot Pipeline for Accident Detection, Localization, and Classification in Traffic Surveillance Video

arXiv:2604.09685v1 Announce Type: new Abstract: We describe a zero-shot pipeline developed for the ACCIDENT @ CVPR 2026 challenge. The challenge requires predicting when, where, and what type of traff

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

arXiv:2603.12221v2 Announce Type: replace Abstract: This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition

ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents

arXiv:2604.10096v1 Announce Type: new Abstract: Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environmen

Ambiguity Detection and Elimination in Automated Executable Process Modeling

arXiv:2604.10884v1 Announce Type: cross Abstract: Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by l

At FullTilt: Real-Time Open-Set 3D Macromolecule Detection Directly from Tilted 2D Projections

arXiv:2604.10766v1 Announce Type: new Abstract: Open-set 3D macromolecule detection in cryogenic electron tomography eliminates the need for target-specific model retraining. However, strict VRAM cons

AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM

arXiv:2510.17934v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has shown some success in augmenting large language models (LLMs) with external knowledge. However, as a

Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising

arXiv:2604.11468v1 Announce Type: new Abstract: This paper presents our solution to the NTIRE 2026 Image Denoising Challenge (Gaussian color image denoising at fixed noise level sigma = 50). Rather th

ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection

arXiv:2604.11790v1 Announce Type: cross Abstract: Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet rem

CodeComp: Structural KV Cache Compression for Agentic Coding

arXiv:2604.10235v1 Announce Type: new Abstract: Agentic code tasks such as fault localization and patch generation require processing long codebases under tight memory constraints, where the Key-Value

Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models

arXiv:2604.10219v1 Announce Type: new Abstract: Multimodal Large Reasoning Models (MLRMs) have achieved remarkable strides in visual reasoning through test time compute scaling, yet long chain reasoni

Data-Driven Automated Identification of Optimal Feature-Representative Images in Infrared Thermography Using Statistical and Morphological Metrics

arXiv:2604.09728v1 Announce Type: new Abstract: Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features such as subsurface defects. However, mo

Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation

arXiv:2604.10741v1 Announce Type: cross Abstract: Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. H

Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition

arXiv:2604.10715v1 Announce Type: new Abstract: Adversarial examples present significant challenges to the security of Deep Neural Network (DNN) applications. Specifically, there are patch-based and t

Degradation-Aware and Structure-Preserving Diffusion for Real-World Image Super-Resolution

arXiv:2604.11470v1 Announce Type: new Abstract: Real-world image super-resolution is particularly challenging for diffusion models because real degradations are complex, heterogeneous, and rarely mode

Different types of syntactic agreement recruit the same units within large language models

arXiv:2512.03676v2 Announce Type: replace Abstract: Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within

Disco-RAG: Discourse-Aware Retrieval-Augmented Generation

arXiv:2601.04377v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowled

Do vision models perceive illusory motion in static images like humans?

arXiv:2604.09853v1 Announce Type: new Abstract: Understanding human motion processing is essential for building reliable, human-centered computer vision systems. Although deep neural networks (DNNs) a

Dual-Branch Remote Sensing Infrared Image Super-Resolution

arXiv:2604.10112v1 Announce Type: new Abstract: Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours,

Echoes of Automation: The Increasing Use of LLMs in Newsmaking

arXiv:2508.06445v3 Announce Type: replace-cross Abstract: The rapid rise of Generative AI (GenAI), particularly LLMs, poses concerns for journalistic integrity and authorship. This study examines AI-g

Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation

arXiv:2503.16771v3 Announce Type: replace-cross Abstract: As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. Howe

Enhancing Cross-Problem Vehicle Routing via Federated Learning

arXiv:2604.10652v1 Announce Type: new Abstract: Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatoria

Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention

arXiv:2512.11811v3 Announce Type: replace-cross Abstract: Crowdsourced social media imagery provides real-time visual evidence of urban flooding but often lacks reliable geographic metadata for emerge

Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion

arXiv:2601.02956v3 Announce Type: replace Abstract: Multilingual Retrieval-Augmented Generation (mRAG) systems often exhibit a perceived preference for high-resource languages, particularly English, r

Fairboard: a quantitative framework for equity assessment of healthcare models

arXiv:2604.09656v1 Announce Type: cross Abstract: Despite there now being more than 1,000 FDA-authorised AI medical devices, formal equity assessments -- whether model performance is uniform across pa

FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher

arXiv:2408.07587v4 Announce Type: replace Abstract: Federated Learning (FL) enables the collaborative training of machine learning models without requiring centralized collection of user data. To comp