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Reuters: Sources: Jeff Bezos pushed Prime Video head Mike Hopkins to highlight AI in an overhaul of the service, leading to an internal effort called Project Lighthouse — Jeff Bezos has identified a n

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Sources: OpenAI models breached Hugging Face's internal systems in a matter of hours, a feat that would typically have taken a talented hacker a couple of weeks (Bloomberg)

DGX agent

Bloomberg: Sources: OpenAI models breached Hugging Face's internal systems in a matter of hours, a feat that would typically have taken a talented hacker a couple of weeks — When OpenAI's advanced art

industrytechmeme

23 Jul 2026

Spatiotemporal Facial Action Unit Detection using Twin Cycle Autoencoders for Driver Monitoring

arXiv:2607.16760v2 Announce Type: replace-cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time. Facial Act

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606.19411v2 Announce Type: replace Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curati

researcharxiv-cs-lg

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing

model-releasesarxiv-cs-ai

arXiv:2607.19368v1 Announce Type: new Abstract: Long-prompt inference remains expensive because prefill attention scales quadratically with sequence length. We propose Spectral-LSH, a training-free pr

Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

arXiv:2607.18722v2 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byp

safetyarxiv-cs-cl

Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes

model-releasesnvidia-developer

Prime Intellect Lab offers a streamlined, hosted reinforcement‑learning workflow that lets users customize the NVIDIA Nemotron 3 Nano in minutes. The process establishes a baseline, trains the model o

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

model-releasesarxiv-cs-ai

arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a

Statevector-Referenced Geometry Survival of a Four-Qubit ZZ Quantum Kernel on IBM Quantum Hardware: A Fixed-Subset Diagnostic Across Three Execution Configurations

arXiv:2607.20377v1 Announce Type: cross Abstract: Quantum-kernel methods encode a dataset's geometry in a Gram matrix, so learning claims on hardware kernels assume the intended geometry survives exec

safetyarxiv-cs-lg

Statistical Early Stopping for Reasoning Models

arXiv:2602.13935v2 Announce Type: replace Abstract: While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning steps, parti

researcharxiv-cs-ai

Statistical Inference for Rank Allocation in Low-Rank Adaptation

arXiv:2607.20205v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and laye

Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2607.19364v1 Announce Type: new Abstract: Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods ofte

Applications

STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning

arXiv:2508.04166v3 Announce Type: replace-cross Abstract: Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data acce

applicationsarxiv-cs-cl

STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching

arXiv:2607.19986v1 Announce Type: new Abstract: Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a determi

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

arXiv:2607.19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and

Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems

arXiv:2607.19357v1 Announce Type: new Abstract: Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often inv

StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation

arXiv:2607.20174v1 Announce Type: cross Abstract: Existing human--object interaction (HOI) video generation methods are largely limited to offline short-video generation with complex driving condition

Strength-Parity Ensembling with Parameter-Isolated Experts for Multi-Task Affect Recognition

model-releasesarxiv-cs-cv

arXiv:2607.16290v2 Announce Type: replace Abstract: Leading entries on the multi-task track of the 11th ABAW challenge rely on heavy ensembling, yet which member is worth adding to an already strong e

Stress Testing Concept Erasure with Large Language Model Agents

arXiv:2607.17890v2 Announce Type: replace Abstract: Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. Howeve

StrokeSeg2: Stroke Lesion Segmentation in Clinical Research Workflows

model-releasesarxiv-cs-cv

arXiv:2607.19901v1 Announce Type: new Abstract: Deep learning frameworks like nnU-Net achieve state-of-theart brain lesion segmentation performance but remain difficult to deploy in clinical research

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

arXiv:2607.19459v1 Announce Type: cross Abstract: Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is

Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting

arXiv:2607.19404v1 Announce Type: cross Abstract: Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learned represen

Applications

STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series

arXiv:2607.18791v1 Announce Type: new Abstract: Early and accurate detection of crop stress is essential to improve agricultural productivity and ensure global food security. However, collecting a lar

applicationsarxiv-cs-cv

Style over Substance: A Shortcut Audit of Emotion-Description Preference Evaluation

arXiv:2607.18508v1 Announce Type: new Abstract: Preference over model-generated emotion descriptions is emerging as a standard evaluation metric for multimodal emotion understanding, exemplified by th

Local Ai

Sudo authentication fails when trying to access local models folder on Fedora

When trying to access the models folder on /usr/share/ollama, I'm asked to authenticate as sudo, which weirdly enough, fails. I type my password, which I'm sure is correct since I use it several time

local-air-ollama

SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning

arXiv:2607.19384v1 Announce Type: new Abstract: Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks. Thi

SUPER Module for Detail-Sensitive and Cost-Efficient U-Net Variant Decoders

arXiv:2511.11015v2 Announce Type: replace Abstract: Skip-connected U-Net variants are widely used for dense inverse problems, yet their decoders commonly recover resolution through spatial upscaling,

surprisal is Not a Theory

arXiv:2607.20208v1 Announce Type: new Abstract: Surprisal Theory is often characterized as a computational-level explanation per (Marr, 1982). We argue in this work that, even though a computational l

researcharxiv-cs-cl

Surprise Forcing: What to Remember, When to Skip in Long Video Generation

arXiv:2607.18436v1 Announce Type: new Abstract: Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources

SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery

arXiv:2603.18634v3 Announce Type: replace-cross Abstract: Rapid, large-scale 3D reconstruction from multi-date satellite imagery is vital for environmental monitoring, urban planning, and disaster res

researcharxiv-cs-lg

Tutorials

SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling

arXiv:2607.18990v1 Announce Type: new Abstract: SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which

tutorialsarxiv-cs-cv

Agents

Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

arXiv:2607.19767v1 Announce Type: new Abstract: A rich and recognizable component library is the cornerstone of printed circuit board (PCB) design and generation. Traditionally, engineers manually cre

agentsarxiv-cs-ai

SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition

model-releasesarxiv-cs-cv

arXiv:2607.18907v1 Announce Type: new Abstract: Instance-level artwork recognition requires matching a handheld visitor photograph to a specific work in a large museum collection. This is challenging

Local Ai

SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

arXiv:2607.19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted d

local-aiarxiv-cs-ai

Synthetic and Derived Training Images for Campus Waste Detection: A Multi-Seed Evaluation with YOLOv8n

arXiv:2607.19535v2 Announce Type: new Abstract: Incorrect disposal can contaminate campus recycling streams, and a bin-mounted camera could provide feedback as an item is discarded. We evaluated wheth

TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management

arXiv:2607.20009v1 Announce Type: new Abstract: This paper presents the second edition of the TalentCLEF Challenge, which will run as an evaluation lab as part of CLEF 2026. The aim of TalentCLEF is t

safetyarxiv-cs-cl

Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

arXiv:2607.20003v1 Announce Type: cross Abstract: An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between s

researcharxiv-cs-ai

TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering

arXiv:2607.18917v1 Announce Type: new Abstract: Long-document multimodal question answering requires more than retrieving relevant chunks from a large document. Different queries require different evi

Task Competence Is Not Instruction Following: Evaluating Instruction-Conflicting Behavior in Small Language Models

model-releasesarxiv-cs-cl

arXiv:2607.19608v1 Announce Type: new Abstract: Instruction tuning is meant to make language models follow user requests, yet it is unclear whether small models comply when an instruction conflicts wi

Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

arXiv:2607.14702v2 Announce Type: replace Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, f

Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations

arXiv:2607.19503v1 Announce Type: cross Abstract: A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embe

researcharxiv-cs-lg

Test Case Prioritization for DNNs via Neural Collapse Instability

arXiv:2607.20046v1 Announce Type: cross Abstract: With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing

Test-Time Registers as Global Priors for Tokenized Image Generation

arXiv:2607.16824v2 Announce Type: replace Abstract: Attention-based models often develop attention sinks, where a small number of tokens repeatedly attract attention and accumulate unusually large act

Test-Time Training for Modality Order Consistency in Vision-Language Models

arXiv:2607.20351v1 Announce Type: cross Abstract: We find that vision-language models are sensitive to a specific semantically irrelevant change: the order in which the image and question are presente

researcharxiv-cs-cl

Applications

Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data

arXiv:2607.18576v1 Announce Type: new Abstract: Vision-based automation is an excellent candidate for reducing manual labor in greenhouse crop production and phenotyping. However, progress is constrai

applicationsarxiv-cs-cv

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers

arXiv:2607.19139v1 Announce Type: new Abstract: Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly unders

The Anatomy of a Truth Direction: Knowledge-Dependent Dimensionality, a Relational Law, and a Convergent Category Geometry in Small Language Models

arXiv:2607.16741v2 Announce Type: replace Abstract: Burger et al. (2024) demonstrated that truth representations in large language models are universal across statement polarity but reside within a mu

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability

model-releasesarxiv-cs-cl

arXiv:2607.20301v1 Announce Type: cross Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such