Search: “local-ai”

arXiv:2607.12592v1 Announce Type: new Abstract: We present WanToFight, a generative game engine that simulates real-time, two-player The King of Fighters '97 (KOF~'97) gameplay from keyboard input. Pr

arXiv:2607.12501v1 Announce Type: new Abstract: The Forward-Forward (FF) algorithm trains each layer locally, so that a scalar goodness - the sum of squared activations - is high on real inputs and lo

You can use OpenCode Desktop with Ollama! Try it with the top open models! Introducing Tabs OpenCode Desktop is now built around tabs. Start a new session in a tab, or open an existing session from an

Very impressive release by the PrismML team. 1-bit quantization shrinks it from 54GB to just 3.8GB (-93%), while retaining 90% of its intelligence. - Collection on Hugging Face: https://huggingface.co

Bonsai 27B is a big step for local AI: 27B-class multimodal capability in a phone-class footprint. Congrats to @PrismML on the launch. Try Ternary Bonsai 27B on Together AI: https://www.together.ai/mo

Huge if true! We are talking about a 27B multimodal model that runs locally on a phone. That's wild! Bonsai 27B reaches up to 163 tok/s in 1-bit and 134 tok/s in Ternary on an NVIDIA GeForce RTX 5090.

Local AI is the future. Learning how to run Opensource models (Inference), how to evaluate them systematically (Evals), and how to customize them (Fine-tuning / RL / Post-training) are invaluable skil

**Official Ollama Extension for VS Code** The extension on the Visual Studio Marketplace provides a dedicated Ollama language‑model integration for Visual Studio Code, enabling developers to run and a

Today, we’re announcing Bonsai 27B: the first 27B-class model to run on a phone. Bonsai 27B is the new multimodal flagship of the Bonsai family. Based on Qwen3.6 27B, it brings a new capability tier t

For the last few months, the conversation around open models has centered on cost and performance optimization. But Satya highlights something more existential: open models aren't just an optimization

Ollama’s partner @jmorgan hosted a live session on Yahoo Finance on July 13, 2026 at 8:30 PM, where they discussed open‑model initiatives. The event was streamed in real time and attracted 16.3 K view

Parse any PDF into agent-ready context. Fully local, free desktop app. No model / internet connection required! Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent,

.@satyanadella's Reverse Information Paradox is real. What @satyanadella's calling for already exists: open models. Over 9M+ developers have used @ollama to access open models and keep their competiti

We just shipped a full revamp of the RunAnywhere apps. iOS and Android, live now. One assistant, fully on-device, works offline: - new UI, opens straight into chat - device-aware model recommendations

A local coding agent, an in-app customer assistant, and an AI SRE triaging production logs may all use the same model class—but not the same harness, eval plan, or rollout risk. Mastra CEO Sam Bhagwat

arXiv:2512.21414v2 Announce Type: replace Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tool

arXiv:2607.08041v1 Announce Type: new Abstract: How diffusion models circumvent the curse of dimensionality to learn complex distributions over high dimensional spaces from a finite training set, inst

arXiv:2503.15414v3 Announce Type: replace-cross Abstract: Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional feder

arXiv:2607.08252v1 Announce Type: new Abstract: Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking

The search did not return the specific details of the b9947 release. Based on the repository and context available: B9947 is a release of llama.cpp, which enables LLM inference in C/C++ . The release

b9948 is a build release of llama.cpp, an open-source C/C++ project for efficient large language model inference. The project ships continuous build-tagged releases using a build numbering system rath

b9949 is a release build of llama.cpp, the open-source C/C++ inference engine for running large language models locally. llama.cpp releases are published frequently with build versions in the b-series

b9950 is a release of llama.cpp, a C/C++ tool for LLM inference . This build number represents one of the frequent incremental releases from the llama.cpp project, which follows a rapid development cy

Release b9956 is a build version of llama.cpp, an open-source project for LLM inference in C/C++ . The project releases frequently, with multiple releases published in a single day , and b9956 represe

Release b9967 of llama.cpp, an LLM inference project in C/C++, includes an update to the server component allowing it to accept null sampling parameters (PR #25538). This change enables more flexible

arXiv:2607.08643v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly constrained by memory capacity, weight bandwidth, and checkpoint storage during deployment. Existing low-b

Call the homies, new @UnslothAI NVFP4 just dropped 🔥 We’re releasing new Qwen3.6 quants that run 2.5× faster on your GPU. Qwen3.6-27B NVFP4 runs on 24GB VRAM. 35B-A3B can hit 17,561 tok/s (B200). We a

arXiv:2601.00549v2 Announce Type: replace-cross Abstract: The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge in

arXiv:2607.08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while p

arXiv:2607.07845v1 Announce Type: new Abstract: The Hessian of the training loss governs the local geometry of the loss landscape, yet despite existing explanations for its largest eigenvalues, the or

arXiv:2607.08014v1 Announce Type: new Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models witho

arXiv:2607.08427v1 Announce Type: cross Abstract: Achieving nanosecond-scale inference latency for deep neural networks (DNNs) has become a primary architectural concern for latency-critical applicati

arXiv:2602.08417v2 Announce Type: replace Abstract: Map-based LiDAR pose tracking is essential for long-term autonomous operation, where onboard map priors need be compact for scalable storage and fas

arXiv:2607.08448v1 Announce Type: new Abstract: Language-conditioned manipulation requires both precise contact-rich control and robust reasoning over language, scenes, and long horizons. End-to-end V

arXiv:2607.08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing meth

arXiv:2607.08303v1 Announce Type: new Abstract: The problem of learning constant-depth circuits holds profound implications for computational learning theory. In a seminal result, by introducing the l

arXiv:2606.15920v2 Announce Type: replace Abstract: Reinforcement learning for multimodal large language models (MLLMs) is often hindered by severe reward sparsity in complex reasoning tasks. This cha

arXiv:2607.08079v1 Announce Type: new Abstract: Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging becaus

arXiv:2607.08025v1 Announce Type: new Abstract: While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by

arXiv:2607.08270v1 Announce Type: new Abstract: Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial d

arXiv:2607.08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-g

arXiv:2601.09071v2 Announce Type: replace Abstract: The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a Rashomon set of models achi

arXiv:2607.08029v1 Announce Type: new Abstract: The emergence of vision language models with fewer than 3 billion parameters has accelerated the implementation of on-device multimodal intelligence. Ho

arXiv:2607.08234v1 Announce Type: cross Abstract: Real-world time series exhibit complex dynamics characterized by multiple simultaneous temporal patterns: short-term fluctuations, periodic seasonal c

Cohere has enabled local Arabic speech-to-text transcription on MacBooks through contributions from Prince_Canuma and lllucas, allowing users to transcribe meetings, voice notes, and other audio witho

arXiv:2607.07727v1 Announce Type: cross Abstract: We present SPL (Structured Prompt Language), a declarative language that composes deterministic and probabilistic computation modes in a single specif