Search: “models”

arXiv:2604.16517v1 Announce Type: cross Abstract: Large vision-language models (VLMs) achieve strong performance on multimodal tasks but often suffer from hallucination and poor grounding in knowledge

arXiv:2604.17104v1 Announce Type: cross Abstract: Modern AI models are growing rapidly in size and redundancy, leading to significant storage and distribution challenges in model hubs. We present Tens

arXiv:2503.18970v3 Announce Type: replace Abstract: Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Ne

Moonshot AI today released Kimi-K2.6, the latest addition to its popular Kimi series of open-source large language models. The Chinese artificial intelligence startup says that the algorithm outperfor

arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T

arXiv:2601.11374v2 Announce Type: replace Abstract: Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage

arXiv:2604.14661v1 Announce Type: cross Abstract: Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, r

arXiv:2502.12222v2 Announce Type: replace Abstract: The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep

arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data

To go deeper on our new Life Sciences model series, research lead @joyjiao12 and product lead Yunyun Wang joined @AndrewMayne on the OpenAI Podcast to discuss how we’re building models for biology, dr

arXiv:2604.15009v1 Announce Type: cross Abstract: Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for ge

🇺🇸 xAI just dropped a real-time speech-to-text model built for voice apps: high limits, multi-language support, the works. Priced at just 0.10–0.20/hour, crushing most competitors. Quiet release, loud

arXiv:2510.06180v2 Announce Type: replace-cross Abstract: In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of t

arXiv:2507.01041v4 Announce Type: replace-cross Abstract: Split learning (SL) is a distributed learning paradigm that can enable computation-intensive artificial intelligence (AI) applications by part

arXiv:2604.12935v1 Announce Type: new Abstract: Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Des

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

arXiv:2512.24503v2 Announce Type: replace-cross Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scal

This r/ollama post discusses a tool for easily linking models between LM Studio and Ollama without duplicating disk storage. Both Ollama and LM Studio are popular local LLM tools, but they store their

This r/ollama community guide provides a practical breakdown of which AI language models (and combinations of models) can realistically fit within the unified memory constraints of common Apple Silico

This Reddit thread from r/ollama discusses recommendations for running local AI models on the AMD Radeon RX 9070 XT using Ollama, touching on GPU compatibility considerations given that the card uses

arXiv:2405.16772v2 Announce Type: replace-cross Abstract: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. Ho

arXiv:2604.09101v1 Announce Type: cross Abstract: Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers,

arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th

arXiv:2602.10603v3 Announce Type: replace Abstract: Genomic foundation models have the potential to decode DNA syntax, yet face a fundamental tradeoff in their input representation. Standard fixed-voc

arXiv:2604.08586v1 Announce Type: cross Abstract: Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications

At Google Cloud, we often see customers asking themselves: 'How can we manage our generative AI costs effectively without sacrificing the performance and availability our applications demand?' This is

arXiv:2604.03906v2 Announce Type: replace Abstract: Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeo

The model is not the agent. The harness is. You need to read this recent study, and a blog post from @hwchase17 ... (links below). It will resonate deeply. This diagram from a recent paper captures so

Try it immediately on Ollama's cloud: ollama run gemma4:31b-cloud Try it with OpenClaw: ollama launch openclaw --model gemma4:31b-cloud Try it with Claude Code: ollama launch claude --model gemma4:31b

managed agents are the right form factor but the lock-in is real if your agent harness lives inside a model provider, you dont own the memory, the tools, or the execution open harness + model choice +

I was unable to retrieve the content of that specific X (Twitter) post, as web search results did not surface the tweet or its content. X.com posts are generally not indexed in a way that makes the...

arXiv:2604.06287v1 Announce Type: new Abstract: Mathematical models and numerical simulations offer a non-invasive way to explore cardiovascular phenomena, providing access to quantities that cannot b

arXiv:2510.21366v3 Announce Type: replace Abstract: In this work, we propose a novel framework to enable diffusion models to adapt their generation quality based on real-time network bandwidth constra

To determine if an AI model can run locally on your computer, the key factors are RAM, storage, and GPU availability: a modern PC with at least 8GB of RAM and a dedicated GPU is generally sufficien...

arXiv:2602.09987v5 Announce Type: replace-cross Abstract: Influence functions are commonly used to attribute model behavior to training documents. We explore the reverse: crafting training data that i

arXiv:2603.25898v2 Announce Type: replace-cross Abstract: LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and senso

arXiv:2604.06391v1 Announce Type: cross Abstract: Graphs are a central representation in biomedical research, capturing molecular interaction networks, gene regulatory circuits, cell--cell communicati

arXiv:2604.08522v1 Announce Type: new Abstract: Video temporal grounding (VTG) is typically tackled with dataset-specific models that transfer poorly across domains and query styles. Recent efforts to

arXiv:2604.06896v1 Announce Type: new Abstract: Epithelial tissues dynamically reshape through local mechanical interactions among cells, a process well captured by vertex models. Yet their many tunab

what he said 🗣️ the very best agents today obsessively tailor the harness layer around the model I’m looking at you “5 things I learned from the Claude code leak” bros 👀 orchestration patterns, tool d

arXiv:2602.22251v3 Announce Type: replace-cross Abstract: General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities. However, most

Anyhow, its not bad. Just not the vibe level that the benchmarks might indicate. And, for a first re-entry into the frontier model space, given the engineering efficiencies they achieved, it feels lik

Having a model like Gemma 4, which is perfectly adequate for everyday use in many cases, runs locally, is free, and secure, still feels unreal. We have a very good AI that costs nothing, uses hardly a

I think the most obvious is that Meta has its own frontier model and can use that to extract additional value out of its customer base/explore new markets for its products. Very few companies can say

arXiv:2610.15372v1 Announce Type: new Abstract: Large language models are generally more capable than smaller ones, but their memory requirements can make local deployment difficult. We ask whether pa

arXiv:2610.15677v1 Announce Type: new Abstract: Continual learning aims to adapt pretrained large language models (LLMs) to evolving data while preserving their pretrained capabilities. A central chal