Search: “models”

From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models

arXiv:2607.06553v1 Announce Type: new Abstract: Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and

Tools

Happy to announce that we’ve enlisted @FireworksAI_HQ as our primary provider for open-source models. In AI inference, quality, latency, rel…

DGX agent

Happy to announce that we’ve enlisted @FireworksAI_HQ as our primary provider for open-source models. In AI inference, quality, latency, reliability, and privacy vary a lot. Fireworks has earned our t

toolsfireworks-ai--x

8 Jul 2026

Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context

arXiv:2607.05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment a

researcharxiv-cs-ai

Hermes Agent is now in the Cloud! Setup couldn't be simpler: pick a model and a server size. Two clicks and 60 seconds later, your agent is …

Hermes Agent is now in the Cloud! Setup couldn't be simpler: pick a model and a server size. Two clicks and 60 seconds later, your agent is live. Running a team? Spin up agents for everyone at your or

agentsnous-research--x

Hardware

InfluMatch: Frontier-Quality KOL Search at 4B-Model Cost

arXiv:2607.05968v1 Announce Type: cross Abstract: Matching influencers (KOLs) to free-form, multi-part Thai marketing criteria is today served either by keyword search over structured profiles, which

hardwarearxiv-cs-ai

It has never been a better time to leave the paid intelligence tokens providers and self-host models yourself Applies to individuals Applies…

This post from Clem Delangue (CEO of Hugging Face) argues that the current state of AI development makes self-hosting open-source language models a viable and advantageous alternative to relying on pa

industryclem-delangue--x

LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models

arXiv:2602.14147v2 Announce Type: replace Abstract: Diffusion language models (dLLMs) recently emerged as a promising alternative to auto-regressive LLMs. The latest works further extended it to multi

“Model orchestration is in many ways the natural outgrowth of agentic engineering” Huge thanks to @AndrewYNg and @DeepLearningAI for the dee…

“Model orchestration is in many ways the natural outgrowth of agentic engineering” Huge thanks to @AndrewYNg and @DeepLearningAI for the deep dive into Sakana AI’s Fugu and Fugu-Ultra. The article hig

agentsdavid-ha--x

MP-MPPI: A Motion Primitive Guided Sampling-Based Optimizer for Model Predictive Control

arXiv:2607.06123v1 Announce Type: new Abstract: This paper proposes a novel method that extends the Model Predictive Path Integral (MPPI) method with motion primitives for additional structured sampli

researcharxiv-cs-ro

Hardware

Nvidia and AI chip startup d-Matrix are combining their hardware in a new system to power AI models, the latest example of Nvidia partnering with rivals (Phoebe Liu/The Information)

Phoebe Liu / The Information: Nvidia and AI chip startup d-Matrix are combining their hardware in a new system to power AI models, the latest example of Nvidia partnering with rivals — Nvidia has a pl

hardwaretechmeme

Storage and egress fees have been one of the biggest cloud lock-in traps in AI. When models and datasets are hard and expensive to move, inf…

Storage and egress fees have been one of the biggest cloud lock-in traps in AI. When models and datasets are hard and expensive to move, infrastructure choice stops being a real choice. Today, we’re c

industryclem-delangue--x

Tutorials

Synthetic Consumer Insight Generation with Large Language Models

arXiv:2607.05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. T

tutorialsarxiv-cs-ai

VEIL: How Visual Encoding Hijacking Induces Bias In Vision Models

arXiv:2607.05641v1 Announce Type: new Abstract: Rendering time series as chart images for CNN-based classification has become increasingly common in time-series classification (TSC). However, it remai

Tools

We're introducing Provisioned Throughput: reserved inference capacity for frontier open models, with token-based pricing and a 99% uptime SL…

We're introducing Provisioned Throughput: reserved inference capacity for frontier open models, with token-based pricing and a 99% uptime SLA. Serverless simplicity, guaranteed capacity, up to 90% low

toolstogether-ai--x

We're still just scratching the surface of how much better, faster, cheaper we can make inference for open-source models! So super excited t…

We're still just scratching the surface of how much better, faster, cheaper we can make inference for open-source models! So super excited to see companies like @zml_ai by @steeve come out of stealth

industryclem-delangue--x

Tools

We've partnered with SpaceXAI to train Grok 4.5. It’s our most powerful model yet and the first we've built for more than software engineeri…

Cursor has partnered with xAI to train Grok 4.5, positioning it as their most advanced model to date with capabilities extending beyond software engineering applications. The partnership represents an

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Applications

A Provably-Correct and Robust Convex Model for Smooth Separable NMF

arXiv:2511.07109v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspe

applicationsarxiv-cs-lg

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

arXiv:2607.04426v1 Announce Type: new Abstract: Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, mon

agentsarxiv-cs-ro

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

arXiv:2607.03887v1 Announce Type: cross Abstract: The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches

researcharxiv-cs-ai

Aligning Language Models with Selective Prediction

arXiv:2607.03528v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reli

safetyarxiv-cs-ai

AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection

arXiv:2603.08387v2 Announce Type: replace Abstract: Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affe

local-aiarxiv-cs-cv

Benign Overfitting Does Not Occur in Diffusion Models

arXiv:2607.02671v1 Announce Type: cross Abstract: Benign overfitting and double descent have come to shape our understanding of generalization in deep learning, establishing that overfitting is not on

safetyarxiv-cs-lg

BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models

arXiv:2607.02643v1 Announce Type: new Abstract: Diffusion-based generative models have transformed visual content synthesis, yet they remain vulnerable to unauthorized usage and lack reliable attribut

CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models

arXiv:2607.04816v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a promising paradigm for generalist robot manipulation, where visual-language representations are used t

researcharxiv-cs-ro

Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models

arXiv:2602.24264v2 Announce Type: replace Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although mod

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

arXiv:2607.04809v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, t

CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Experts

arXiv:2607.03693v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced generalist robotic manipulation, yet real-world deployment reveals a fundamental challenge: robots are

safetyarxiv-cs-ro

deepagents is our newest open source project - an open source, model agnostic agent harness this is maybe the most important academy course …

deepagents is our newest open source project - an open source, model agnostic agent harness this is maybe the most important academy course we've launched 🎓 New course launch from LangChain Academy: I

agentsharrison-chase--x

DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models

arXiv:2607.03899v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specifi

Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis

arXiv:2607.04636v1 Announce Type: new Abstract: Key Information Extraction (KIE) converts visually rich documents into structured data, but practical deployment remains challenging: strong performance

local-aiarxiv-cs-cv

Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

arXiv:2607.02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary

Fast 3D Foundation Model Initialized Gaussian Splatting

arXiv:2607.03209v1 Announce Type: new Abstract: This paper introduces a fast method for high-quality 3D Gaussian Splatting (3DGS) reconstruction without traditional Structure-from-Motion (SfM). The pr

agentsarxiv-cs-cv

Finite-Sample Closed-Loop Stability of Model Predictive Path Integral Control for Linear Time-Invariant Systems

arXiv:2607.04006v1 Announce Type: cross Abstract: We establish finite-sample closed-loop stability guarantees for Model Predictive Path Integral (MPPI) control applied to discrete-time Linear Time-Inv

safetyarxiv-cs-ro

Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models

arXiv:2607.04461v1 Announce Type: new Abstract: Inference-time scaling for text-to-image generation has progressed from simple Best-of-N (BoN) sampling to guided search methods that verify and steer c

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning

arXiv:2606.18089v2 Announce Type: replace Abstract: Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming l

local-aiarxiv-cs-lg

G^2TAM: Geometry Grounded Track Anything Model

arXiv:2607.03789v1 Announce Type: new Abstract: Human spatial understanding arises from jointly perceiving geometry and semantics, enabling consistent object identification and localization across vie

local-aiarxiv-cs-cv

Gradient Descent as Implicit EM in Distance-Based Neural Models

arXiv:2512.24780v2 Announce Type: replace Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specializat

KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation

arXiv:2607.04652v1 Announce Type: new Abstract: Learning manipulation from few demonstrations requires visual priors that capture not only where to interact, but also how the interaction should begin;

local-aiarxiv-cs-ro

Local Learning Rules for Out-of-Equilibrium Physical Generative Models

arXiv:2506.19136v4 Announce Type: replace Abstract: We show that the out-of-equilibrium driving protocol of score-based generative models (SGMs) can be learned via local learning rules. The gradient w

Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models

arXiv:2607.03517v1 Announce Type: cross Abstract: Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge fr

Hardware

MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems

arXiv:2607.02558v1 Announce Type: cross Abstract: As machine learning shifts from laboratory curiosity to critical infrastructure, the systems that sustain it span an extraordinary range, from sub-mil

hardwarearxiv-cs-lg

Modeling the Impact of Visual Brand Language on Attention, Object Recognition, and Memory Retrieval

arXiv:2607.02929v1 Announce Type: cross Abstract: Visual brand language is the set of visual properties that convey brand identity for a product. What is the impact of visual brand language on a perso

researcharxiv-cs-ai

Monitoring discriminative ML models using Amazon SageMaker AI with MLflow

Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use ope

industryaws-ml-blog

NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models

arXiv:2607.03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches f

safetyarxiv-cs-lg

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

arXiv:2607.04775v1 Announce Type: cross Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistica

On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models

arXiv:2607.04332v1 Announce Type: new Abstract: In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reason

Out-of-distribution Neural Inference in Dynamical Ising Models

arXiv:2607.03039v1 Announce Type: new Abstract: Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distri

Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models

arXiv:2607.02914v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness,

safetyarxiv-cs-ai