Search: “local-ai”

arXiv:2604.07027v1 Announce Type: new Abstract: Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout

arXiv:2601.04068v3 Announce Type: replace Abstract: Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization

arXiv:2604.06798v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binariz

arXiv:2604.07578v1 Announce Type: new Abstract: Recognition of rodent behavior is important for understanding neural and behavioral mechanisms. Traditional manual scoring is time-consuming and prone t

arXiv:2604.03336v2 Announce Type: replace Abstract: BitNet b1.58 (Ma et al., 2024) demonstrates that large language models can operate entirely on ternary weights {-1, 0, +1}, yet no native binary wir

arXiv:2503.24135v3 Announce Type: replace Abstract: Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-c

arXiv:2604.06912v1 Announce Type: cross Abstract: MLLMs require high-resolution visual inputs for fine-grained tasks like document understanding and dense scene perception. However, current global res

arXiv:2604.08502v1 Announce Type: new Abstract: Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existi

arXiv:2604.07298v1 Announce Type: cross Abstract: Multiple Instance Learning (MIL) is the dominant framework for gigapixel whole-slide image (WSI) classification in computational pathology. However, c

arXiv:2604.07994v1 Announce Type: new Abstract: Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies. However, the quadratic computational comple

arXiv:2604.08542v1 Announce Type: new Abstract: This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown pro

arXiv:2604.07737v1 Announce Type: new Abstract: While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when

arXiv:2508.16703v4 Announce Type: replace-cross Abstract: On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention o

arXiv:2601.16294v2 Announce Type: replace-cross Abstract: General Matrix Multiplication (GEMM) is the cornerstone of HPC workloads and Deep Learning. State-of-the-art vendor libraries tune tensor layo

arXiv:2604.06631v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and sta

arXiv:2604.07101v1 Announce Type: cross Abstract: We present the Surveillance Forgery Image Test Range (SurFITR), a dataset for surveillance-style image forgery detection and localisation, in response

arXiv:2604.01130v3 Announce Type: replace Abstract: As sports training becomes more data-driven, traditional dart coaching based mainly on experience and visual observation is increasingly inadequate

We just got back from our team offsite in Hawaii - and wow, did we need it. 🏝️☀️ Our team has been heads down driving some serious growth, and this trip was the perfect reset. We spent the time doing

1️⃣✅❌ 2️⃣✅❌ 3️⃣✅❌ One of the benefits of Deep Agents deploy is model optionality Choose from models from @OpenAI @GeminiApp @AnthropicAI @FireworksAI_HQ @baseten @OpenRouter @ollama @nvidia and many o

I’m excited to announce that I’ve joined @lmstudio 👾 The team behind the app is amazing and I couldn’t be more proud. I’ll still be working on Locally AI, now full-time, to bring the best experience p

So excited to share that @adrgrondin and @LocallyAIApp are joining LM Studio family! Together we are doubling down on Apple platforms to bring you delightful AI experiences across devices. Adrien was

Today, we’re excited to announce that we’re joining @lmstudio! We share a common vision of building amazing products for everyone and making local models more accessible. We’re combining our efforts t

Cool thing is that you might be safer in jail with local AI than anywhere else! Delete your search history, delete your bookmarks, delete your reddit, medical records, 12 yr old tumblr, delete everyth

sam3.cpp - Meta's SAM 3 in pure C++ with @ggerganov's ggml - Supports SAM 3.1, 3, 2.1, 2 and EdgeTAM - FP16, 4-bit quant (EdgeTAM in 15 MB) - Apple Metal GPU, CUDA, CPU - Text-prompted: 'peach' → ever

Server overload should be resolved now ✅ Our servers are overloaded at the moment, and we are working to fix this! Very sorry for the inconvenience. Model search, download, LM Link, and website are im

.@steipete has been an amazing supporter of open models and local models. This is even before the rise of OpenClaw. 🦞 Ollama is here to love. ❤️ Let's make the world a better place together. Some folk

Today, we are launching our collaboration with @nomic_ai to make AI agents more effectively and efficiently understand complex PDF documents. Nomic's new nomic-layout-v1 model allows your AI agents to

We absolutely LOVE local models. Have been talking about this for years, it is my favorite thing. Most people cannot afford luxury AI tokens. Yet, everyone on earth should have a personal AI. The only

See you there! We are hosting Ollama's MLX meetup this Thursday night (April 9th) at Ollama's office in Palo Alto at 6pm. Come meet amazing people! RSVP is required as space is very limited. Food & dr

We are hosting Ollama's MLX meetup this Thursday night (April 9th) at Ollama's office in Palo Alto at 6pm. Come meet amazing people! RSVP is required as space is very limited. Food & drinks will be av

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Hey guys, Last time I tested Qwen3.8-Flash-Next on its own. This time I put three Qwen3.8 checkpoints through the same 10 tests on the same RTX PRO 6000: RadixArk/Qwen3.8-27B-NVFP4 (dense) orcarouter/