Search: “models”

As a result, we can show that open-source-derived models are not inherently untrustworthy. They can be suitable for production agents with c…

applicationscognition-ai--x

As a result, we can show that open-source-derived models are not inherently untrustworthy. They can be suitable for production agents with careful development, realistic evals, and targeted mitigation

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Research

Constrained Decoding for Diffusion Language Models via Efficient Inference over Finite Automata

DGX agent

arXiv:2607.07026v1 Announce Type: new Abstract: Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function ca

researcharxiv-cs-lg

9 Jul 2026

Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation

arXiv:2607.07608v1 Announce Type: cross Abstract: Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling wi

How training environments can teach AI models to misbehave

This IBM Research article examines how training environments and conditions can inadvertently cause AI models to develop undesirable behaviors or fail to learn intended objectives. The piece likely ex

researchibm-research

Impressive Intelligence vs. Cost for Grok 4.5 I think given terafab + the Tesla FSD team's expertise in making very efficient AI models, thi…

Impressive Intelligence vs. Cost for Grok 4.5 I think given terafab + the Tesla FSD team's expertise in making very efficient AI models, this advantage will only continue to compound Grok is making pr

industryelon-musk--x

Incentivizing Temporal-Awareness in Egocentric Video Understanding Models

Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning dep

safetyapple-ml-research

🤗 MOSS Transcribe series update.@Open_MOSS Alongside MOSS-Transcribe-Diarize-0.9B, we are also updating two models: MOSS-Transcribe-Diarize…

applicationsclem-delangue--x

🤗 MOSS Transcribe series update.@Open_MOSS Alongside MOSS-Transcribe-Diarize-0.9B, we are also updating two models: MOSS-Transcribe-Diarize Pro >Flagship multi-speaker transcription for complex meetin

On Runway Dev, get access to the world’s best media models on one enterprise-grade platform. Seed Audio 1.0, Seedance Mini, Seedance 4K, Goo…

applicationscristobal-valenzuela--x

On Runway Dev, get access to the world’s best media models on one enterprise-grade platform. Seed Audio 1.0, Seedance Mini, Seedance 4K, Google Omni Flash, Seedream 5.0 Pro and more now available. Get

Physics-guided spatiotemporal neural models for fuel density prediction

arXiv:2607.06999v1 Announce Type: cross Abstract: This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowled

researcharxiv-cs-ai

Practicing with Language Models Cultivates Human Empathic Communication

arXiv:2603.15245v2 Announce Type: replace Abstract: Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) ge

researcharxiv-cs-cl

Very cool to see @netflix releasing video datasets and models on @huggingface. They have a strong video AI team and would be amazing to see …

Netflix has released video datasets and models on Hugging Face, leveraging their internal video AI expertise to contribute to the open-source community. The post, shared by Clem Delangue (CEO of Huggi

VultronRetriever: Open Visual Document Retrieval Models Built for Scale

Introducing VultronRetriever, a family of open visual document retrieval models built on Vultr. Achieve state-of-the-art retrieval performance with smaller indexes, higher throughput, and support for

applicationsvultr

We evaluated 30+ frontier embodied AI models. The result is clear: current generalist robot policies are still far from robust real-world ma…

applicationsyohei-nakajima--x

A comprehensive evaluation of over 30 frontier embodied AI models reveals that current generalist robot policies lack the robustness needed for reliable real-world manipulation tasks. The analysis dem

When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models

arXiv:2607.06630v1 Announce Type: new Abstract: Formal robustness certificates for embedded neural-interface models can pass while task accuracy collapses: at perturbation budget e=0.25, EEGNet classi

safetyarxiv-cs-lg

AEGIS: A Mechanism-Guided Defense against Visual Synonym Jailbreaks in Text-to-Image Models

arXiv:2607.06120v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved high visual fidelity and broad adoption, but remain vulnerable to safety violations when adversaries exploi

local-aiarxiv-cs-cv

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…

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

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

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

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

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

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

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

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

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

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

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

arXiv:2607.04163v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answ

researcharxiv-cs-ai

Selective Disclosure Watermarking for Large Language Models

arXiv:2607.05353v1 Announce Type: cross Abstract: Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs). Existing approaches include zero-

researcharxiv-cs-ai