Search: “models”

LongE2V: Long-Horizon Event-based Video Reconstruction, Prediction, and Frame Interpolation with Video Diffusion Models

arXiv:2607.08770v1 Announce Type: new Abstract: Recovering high-quality video from sparse event streams is a challenging task. Regression methods often blur textures, while existing generative models

safetyarxiv-cs-cv

10 Jul 2026

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Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception

DGX agent

arXiv:2607.08321v1 Announce Type: new Abstract: In computational vision science, Convolutional Neural Networks (CNNs) have emerged as a popular model of biological vision because of the alignment they

safetyarxiv-cs-cv

10 Jul 2026

Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models

arXiv:2607.07772v1 Announce Type: new Abstract: In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and cu

researcharxiv-cs-cl

10 Jul 2026

Write-Protected Discrete Bottlenecks for Language-Grounded World Models: A Structural Limitation and Sufficient Fix

arXiv:2607.08312v1 Announce Type: new Abstract: How should language interface with a world model's discrete symbol system? The dominant paradigm -- end-to-end injection of LLM/VLM features into robot

tutorialsarxiv-cs-lg

10 Jul 2026

Aurora 1.5: Extending open foundation models for weather and Earth-system applications

applicationsmicrosoft-research

Aurora 1.5 adds 22 more variables, hourly temporal resolution, and probabilistic ensemble forecasting to the Aurora foundation model, making it more useful for real-world weather, climate, and energy

Agents

Future Confidence Distillation in Large Language Models

arXiv:2607.07626v1 Announce Type: cross Abstract: Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as

agentsarxiv-cs-ai

Loving this piece: bringing delight to your daily coffee, your daily workflows, and your AI bill. @gumloop is now running open-weight models…

applicationsfireworks-ai--x

Loving this piece: bringing delight to your daily coffee, your daily workflows, and your AI bill. @gumloop is now running open-weight models on Fireworks in production. 80% lower cost vs. closed-lab A

Local Ai

Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency

arXiv:2607.07207v1 Announce Type: cross Abstract: We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.2), rapid

local-aiarxiv-cs-ai

Tensorized algorithms and scalable filtering methods for hidden Markov and factorial hidden Markov models

arXiv:2607.07008v1 Announce Type: cross Abstract: A common method for the representation and analysis of time-series data is the hidden Markov model (HMM), where each observation is associated with a

applicationsarxiv-cs-lg

Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection

arXiv:2607.05434v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behavi

applicationsarxiv-cs-cv

Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity

arXiv:2607.05665v1 Announce Type: new Abstract: This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. We focus on mo

researcharxiv-cs-ro

My big takeaway is that both Sol & Fable represent jumps over previous models and have opened a large gap with the next-best AIs. People wil…

applicationsethan-mollick--x

My big takeaway is that both Sol & Fable represent jumps over previous models and have opened a large gap with the next-best AIs. People will have preferences for one or the other, but if you doing an

On the Redundancy of Timestep Embeddings in Diffusion Models

arXiv:2606.20416v2 Announce Type: replace-cross Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales. In this work, we

PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models

arXiv:2607.05441v1 Announce Type: cross Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still str

Replication in Visual Diffusion Models: A Survey and Outlook

arXiv:2408.00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content. However, they inevitably mem

applicationsarxiv-cs-ai

Same prompt. 3 models. 6 camera moves. We put Seedance 2.0, Kling 3.0, and Grok Imagine 1.5 head-to-head on the camera techniques → Bullet t…

Same prompt. 3 models. 6 camera moves. We put Seedance 2.0, Kling 3.0, and Grok Imagine 1.5 head-to-head on the camera techniques → Bullet time → Crash zoom → Crane reveal → Dolly zoom → Object POV →

applicationscomfyui--x

Local Ai

SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR

arXiv:2607.05484v1 Announce Type: cross Abstract: The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability

local-aiarxiv-cs-lg

SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models

arXiv:2607.06442v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not nece

researcharxiv-cs-ro

Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition

arXiv:2607.06185v1 Announce Type: new Abstract: Fine-grained image recognition poses a significant challenge due to the substantial expertise and effort required for manual annotation. Vision-language

safetyarxiv-cs-cv

Hardware

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing

arXiv:2607.06136v1 Announce Type: new Abstract: Recent diffusion-based generative models have shown impressive performance in image generation and editing. However, due to memory limitations and the h

hardwarearxiv-cs-cv

A Decomposable Probe for Few-Step Diffusion Models: Prompt, Latent, and Score Selectivity across Backbone Families and Distillation Paradigms

arXiv:2607.03256v1 Announce Type: new Abstract: Few-step distilled diffusion students cut text-to-image inference from ~50 to 1-8 network evaluations, but the quality gap is usually summarised by a si

A Random Matrix Theory Perspective on the Consistency of Diffusion Models

arXiv:2602.02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise

CompressedVQA-AEV: Full-Reference and No-Reference Quality Assessment Models for Asymmetric Encoded Videos

arXiv:2607.04606v1 Announce Type: cross Abstract: This report presents our solutions to the QoMEX 2026 Grand Challenge on Video Quality Assessment for Asymmetric Encoded Videos, comprising a full-refe

Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models

arXiv:2607.02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes. Beyond data augmentation, t

tutorialsarxiv-cs-ai

Do ECG Foundation Models Transfer to Rare Cardiac Diseases? Evidence from Brugada Syndrome Detection

arXiv:2607.03009v1 Announce Type: new Abstract: Background: Foundation models (FMs) trained on large-scale unlabeled physiological data have emerged as a promising paradigm for medical artificial inte

safetyarxiv-cs-lg

Don't Commit Alone: Joint Token Commitment in Diffusion Large Language Models

arXiv:2607.04469v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) commit multiple tokens per denoising step by decoding each selected position independently from the shared conte

researcharxiv-cs-cl

Efficient bias mitigation in T2I diffusion models using Concept Graphs

arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene on

Industry

European banking watchdogs ECB and ESRB warn that frontier AI models pose 'systemic risks to the financial system', giving lenders four months to prepare (Financial Times)

Financial Times: European banking watchdogs ECB and ESRB warn that frontier AI models pose “systemic risks to the financial system”, giving lenders four months to prepare — IT weaknesses could be expl

industrytechmeme

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

arXiv:2607.04265v1 Announce Type: cross Abstract: World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration,

Handwriting Trajectory Recovery with Diffusion Models

arXiv:2607.03422v1 Announce Type: new Abstract: Recovering online pen trajectories from offline handwriting images, often referred to as handwriting trajectory recovery (stroke recovery), is an offlin

Hierarchical Anti-Aesthetics: Protecting Facial Privacy against Customized Diffusion Models

arXiv:2607.02038v2 Announce Type: replace Abstract: The rise of customized diffusion models has fueled a boom in personalized visual content creation, but it also introduces serious risks of malicious

tutorialsarxiv-cs-cv

Identifiability Without Gaussianity: Symbolic World Models and Near-Infinite Temporal Consistency

arXiv:2606.12471v2 Announce Type: replace-cross Abstract: Klindt, LeCun, and Balestriero (arXiv:2605.26379) proved that Joint-Embedding Predictive Architectures (JEPAs) achieve linear identifiability,

safetyarxiv-cs-cl

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

arXiv:2506.07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing featu

Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models

arXiv:2607.03217v1 Announce Type: new Abstract: Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have adv

applicationsarxiv-cs-lg

Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

arXiv:2607.02909v1 Announce Type: cross Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite the

Hardware

NVIDIA and Hugging Face Bring New Models and Frameworks to LeRobot for the Open Robotics Community

Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated b

hardwarenvidia-blog

Industry

OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months (Kai Nicol-Schwarz/CNBC)

Kai Nicol-Schwarz / CNBC: OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months — Chinese-built

industrytechmeme

PLoRA: Efficient Concurrent LoRA Training for Large Language Models

arXiv:2508.02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and

researcharxiv-cs-lg

Quantum-Inspired Harmonic Decision Models: A Computational Framework for Music Generation

arXiv:2607.05007v1 Announce Type: new Abstract: This paper introduces a quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization a

Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models

arXiv:2607.05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows. Limited

Recursive Entropic Variational Inference for Nonlinear State-Space Models

arXiv:2511.15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangi

researcharxiv-cs-lg

Agents

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

arXiv:2607.02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that

agentsarxiv-cs-ai

SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revision

arXiv:2508.03177v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) recently achieve significant breakthroughs in understanding complex visual-textual contexts. However, hallucina

applicationsarxiv-cs-cv

Model Releases

The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation

model-releasesarxiv-cs-ai

arXiv:2607.03160v1 Announce Type: cross Abstract: This study examines how prompt language and translation theory-driven prompt design influence the quality of Spanish-Chinese journalistic translations

Tightening the Score Matching Gap for Diffusion Models

arXiv:2607.04442v1 Announce Type: cross Abstract: Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution. Their training and evaluation pr

researcharxiv-cs-lg

Transferability Between Understanding and Generation in Unified Multimodal Models

arXiv:2607.04423v1 Announce Type: cross Abstract: Unified Multimodal Models (UMMs) integrate image understanding and generation within a single architecture, yet how the two tasks interact remains und

VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models

arXiv:2607.04517v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, h

// ReContext // Models now support 128K context windows and still fail to use evidence that is already in the prompt. Where is the gap? New …