Search: “models”

arXiv:2608.10947v1 Announce Type: new Abstract: Learned image compression has seen significant progress in recent years with the development of end-to-end learned models that achieve better compressio

arXiv:2608.10291v1 Announce Type: cross Abstract: Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructu

arXiv:2608.10405v1 Announce Type: cross Abstract: Many studies have shown that specially crafted inputs can induce large language models (LLMs) to generate excessively long outputs, resulting in signi

arXiv:2608.11149v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in storing and recalling rich person-related knowledge, raising incre

Alibaba released the open‑weights Qwen3.8‑2.4T‑A95B (Qwen3.8‑Max), a fine‑grained mixture‑of‑experts model with 2.4 trillion parameters, hybrid full‑ and linear‑attention, a one‑million‑token context

arXiv:2608.10692v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across

What's Changed Models that don't set a repeat_penalty now default to 1.0 (off) instead of 1.1, matching other engines and speeding up speculative decoding; set a per-model parameter if an older model

When should you start post-training your own models? @FireworksAI_HQ CEO @lqiao’s answer: after product-market fit. Not because it's hard... but because only after PMF is the data coming off your prod

arXiv:2608.11024v1 Announce Type: new Abstract: Attribute hallucination---where vision-language models (VLMs) correctly identify an object but mischaracterize its properties---is prevalent yet mechani

arXiv:2608.08319v1 Announce Type: new Abstract: Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, po

I just heard of this model. Seems to be a competitor to DeepSeek V4 Flash. About the same size and active parameters. Anyone tested it compared to V4 Flash? Link: https://huggingface.co/upstage/Solar-

arXiv:2608.07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primari

Back after ~6 months — rebuilding my spiking language model around CPU-first inference Hey everyone. It’s been around six months since I last posted anything about this project here. Some of you might

arXiv:2608.07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret. We developed

arXiv:2608.07523v1 Announce Type: cross Abstract: Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language

I’ve been thinking about how agents can learn inside world models for years. We decided to scale up our RSI Lab to bridge recursive self-improvement with physical AI and robotics. We are looking for f

arXiv:2608.08652v1 Announce Type: cross Abstract: We present LegoLM{}, a structured weight-sharing compression framework for large language models grounded in a systematic study of why global weight s

arXiv:2601.15394v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is increasingly adopted to transfer capabilities from large language models to smaller ones, offering significant improv

Nemotron 3.5 Lightning is available on Together AI through Dedicated Model Inference, giving teams reserved capacity and predictable performance for high-volume agent workloads. Start building: https:

NVIDIA’s Nemotron 3.5 Lightning—a fast open AI model designed for always‑on agents that perform high‑volume, specialized work—has been launched on the Together AI platform as of 11 August 2026. NVIDIA

arXiv:2607.16235v3 Announce Type: replace-cross Abstract: Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However,

arXiv:2512.22966v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated promise in medical knowledge assessments, yet their practical utility in real-world clinical decision

arXiv:2608.09424v1 Announce Type: new Abstract: Autoregressive language models align training and use: generation conditions on a clean prompt, and training predicts future tokens from clean left cont

arXiv:2608.07594v1 Announce Type: cross Abstract: Interpretability is often treated as a tax on capability: language models are trained as opaque systems, then explained after the fact, with methods w

arXiv:2608.07712v1 Announce Type: cross Abstract: A predictive model receives a self-supervised signal whenever the consequence of an action is observed. Using that signal after deployment is difficul

Stealing Reasoning Traces from Proprietary LLM APIs A vanity domain name (stolen-thoughts.com) for a neat paper: Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that

arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one questio

arXiv:2608.08168v1 Announce Type: new Abstract: While Large Language Models (LLMs) employing Chain-of-Thought (CoT) exhibit superior reasoning capabilities, the neural mechanisms distinguishing this e

arXiv:2608.08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI. Long-horizon robotic manipulation requires scalable vision feedback beyond han

arXiv:2608.07525v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applicat

arXiv:2608.08791v1 Announce Type: new Abstract: Diffusion language models use broad context to create text, suggesting they might handle input noise better than standard models. Testing reveals this i

arXiv:2608.08477v1 Announce Type: new Abstract: We present VectraYX-Vision-1B, a sub-2B vision-language model (VLM) for Spanish/LATAM cybersecurity imagery, coupling a frozen SigLIP-so400m encoder to

arXiv:2608.08558v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by jointly modeling future visual dynamics and actions. However, their scalability and generali

arXiv:2608.06691v1 Announce Type: new Abstract: Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency

arXiv:2608.06688v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide strong semantic priors for robot navigation, but they often ignore embodiment-specific mobility constraints.

arXiv:2511.18112v3 Announce Type: replace Abstract: Recent progress in Vision-Language-Action (VLA) models has enabled embodied agents to interpret multimodal instructions and perform complex tasks. H

arXiv:2608.06445v1 Announce Type: cross Abstract: Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation. This s

arXiv:2608.07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization. Despite their impressive performance on a wide range of tasks, Large Language Models (LLMs) s

arXiv:2608.06769v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse vision-language tasks, creating an urgent need for more

arXiv:2608.06795v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) enables efficient specialization and distribution of large language models through compact adapters. However, untrusted ada

Meta returns to open weights: Muse Glimmer, its first open-weights release since Llama 4, scores 35 on the Artificial Analysis Intelligence Index. It is a 30B-parameter model, and the first from Meta

arXiv:2511.23204v2 Announce Type: replace Abstract: Pathology foundation models (FMs) have driven significant progress in computational pathology. However, these high-performing models can easily exce

arXiv:2509.05208v2 Announce Type: replace Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise vis

arXiv:2608.06394v1 Announce Type: new Abstract: Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing

Ollama allons you to import model but have you ever tried doing so ? Like running model imported from hugging face or you own model ? Any use case you wanna share ? Very curious about that submitted b

TL;DR: I am building a Windows-first local AI setup for people who want to try local LLMs without spending days choosing models, setting up Ollama, Docker, WSL, Open WebUI, agents, and tool permission