local ai

Web Chat para Ollama is a tool that enables users to run a private, free AI chat interface locally on their own machines using Ollama, an open-source framework for running large language models. The p

arXiv:2605.30102v1 Announce Type: cross Abstract: The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong

arXiv:2605.28578v1 Announce Type: new Abstract: We propose the Tikhonov layer, a graph neural network layer that is interpretable by design: once trained, its learned parameters directly reveal which

arXiv:2605.27763v1 Announce Type: new Abstract: Safety evaluations of language models often treat serving configuration as fixed background infrastructure, but batch condition is an untested treatment

arXiv:2511.02398v2 Announce Type: replace Abstract: We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off be

This post announces an 8-9 billion parameter language model optimized for efficiency and speed, trained on a very large token dataset, and positioned as accessible for users with limited GPU resources

arXiv:2605.28362v1 Announce Type: new Abstract: Mobile robot path planning methods are often constrained by vast search spaces, resulting in latency in samplingbased algorithms. Learning-based approac

arXiv:2605.27986v1 Announce Type: new Abstract: Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicit

Wall Street Journal: As Google plans to build a $15B AI data center hub in India's Visakhapatnam, locals and rights groups are raising concerns over “extremely high” water stress — Developing-world na

b9374 is a release of llama.cpp from May 28, 2026 , focused on continuous integration improvements. The release includes refactoring of CI/CD workflows, separate CUDA Windows workflow configuration, c

b9375 is a release of llama.cpp, a project for LLM inference in C/C++. The release likely contains bug fixes, performance improvements, or feature additions to the llama.cpp codebase. Specific details

Based on the available information about llama.cpp, b9378 is likely an intermediate build release of the llama.cpp project. llama.cpp enables LLM inference in C/C++ , and the project releases multiple

B9381 is a build/release tag for llama.cpp, a C/C++ framework for LLM inference. This release likely includes bug fixes, performance optimizations, and feature improvements to the project, which enabl

I don't have specific information about release b9382 from llama.cpp. Based on the GitHub release tag format, this entry likely documents a specific build or commit version of llama.cpp, which is a C+

arXiv:2605.27674v1 Announce Type: cross Abstract: Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, ind

arXiv:2605.28729v1 Announce Type: cross Abstract: Robustness of neural networks is commonly quantified via local or global Lipschitz constants. However, Lipschitz continuity can be overly coarse or ov

arXiv:2605.28115v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face severe memory and latency bottlenecks due to high-resolution visual tokens. While current token reduction methods the

arXiv:2605.28387v1 Announce Type: cross Abstract: Recognizing and continuously learning novel human actions without forgetting prior classes is a requirement for emerging AR/VR and robotics applicatio

arXiv:2605.28521v1 Announce Type: new Abstract: ClinicalEncoder26AM is a multilingual Diagnosable ColBERT for clinical and biomedical texts, which aligns at multiple levels its token-level semantic wi

Create transformation videos where a cube, sphere, capsule, or custom object transforms into a person or item with a button press using Seedance 2.0 inside ComfyUI. The workflow automatically generate

arXiv:2602.03491v2 Announce Type: replace-cross Abstract: Reasoning over table images remains challenging for Large Vision-Language Models (LVLMs) due to complex layouts and tightly coupled structure-

arXiv:2507.22554v3 Announce Type: replace Abstract: To understand our global progress for sustainable development and disaster risk reduction in many developing economies, two recent major initiatives

arXiv:2605.28018v1 Announce Type: new Abstract: Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and

arXiv:2605.27432v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) has emerged as a paradigm for grounding large language models in external knowledge, yet most existing RAG system

arXiv:2605.28200v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) profiles large numbers of cells but loses spatial context, whereas spatial transcriptomics (ST) preserves partial

arXiv:2605.28811v1 Announce Type: new Abstract: We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illuminati

arXiv:2605.27489v1 Announce Type: cross Abstract: Multi-agent LLM systems decompose workflows across agents, tools, shared context, memory, and decision gates. This modularity improves interpretabilit

A Chrome extension built for automated web interaction and data extraction that uses Ollama's local LLM capabilities to intelligently navigate websites, scroll pages, fill out forms, type text, and sc

arXiv:2605.28320v1 Announce Type: cross Abstract: This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number

arXiv:2603.16985v2 Announce Type: replace Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibil

arXiv:2605.27786v1 Announce Type: cross Abstract: Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving

arXiv:2605.28170v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a

arXiv:2605.27937v1 Announce Type: cross Abstract: Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leadin

arXiv:2502.17832v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and

arXiv:2602.12843v2 Announce Type: replace Abstract: Chest X-ray (CXR) reporting follows a region-based clinical workflow in which radiologists inspect anatomical regions and integrate localized findin

arXiv:2605.28805v1 Announce Type: cross Abstract: Visual outcomes are increasingly central to multimodal large language models, making reliable and fine-grained verification essential for scaling gene

arXiv:2605.28691v1 Announce Type: new Abstract: Diffusion Transformers achieve strong video generation quality, but the quadratic cost of full attention limits efficiency. We introduce OSP-Next, an ef

arXiv:2605.28585v1 Announce Type: new Abstract: Communication-efficient distributed optimizers such as DiLoCo reduce synchronization costs by letting workers perform many local updates before aggregat

arXiv:2601.17354v5 Announce Type: replace Abstract: While 3D Gaussian Splatting (3DGS) enables real-time rendering, its training demands workstation-level compute and memory, making mobile deployment

arXiv:2605.27750v1 Announce Type: cross Abstract: Recent work has shown that Vision-Language Models (VLMs) used for optical character recognition (OCR) can generate plausible but visually unsupported

arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e

arXiv:2605.28209v1 Announce Type: new Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contr

arXiv:2605.27959v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have increasingly localized and interleaved visual evidence for deliberative reasoning. Grounding-based appro

arXiv:2602.01807v3 Announce Type: replace Abstract: Language models (LMs) are a central component of modern AI systems, and diffusion language models (DLMs) have recently emerged as a competitive alte

arXiv:2605.27924v1 Announce Type: new Abstract: Text-driven image editing has advanced rapidly, but reliably localizing these manipulations requires image manipulation localization (IML) models traine

arXiv:2605.27747v1 Announce Type: cross Abstract: Existing training approaches for large language models learn a single set of parameters, based on large volumes of data, which is typically heterogene