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Exclusive: AirOps targets emerging AI search market with autonomous content optimization agent

AirOps, the business name of Rivington Labs Inc., today introduced Quill, an artificial intelligence agent designed to help brands maintain visibility in generative AI search engines by continuously m

agentssiliconangle

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Expected Batch Optimal Transport Plans and Consequences for Flow Matching

DGX agent

arXiv:2605.12174v1 Announce Type: new Abstract: Solving optimal transport (OT) on random minibatches is a common surrogate for exact OT in large-scale learning. In flow matching (FM), this surrogate i

safetyarxiv-cs-lg

13 May 2026

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?

arXiv:2605.11086v1 Announce Type: cross Abstract: AI agents are rapidly gaining capabilities that could significantly reshape cybersecurity, making rigorous evaluation urgent. A critical capability is

Exploring Token-Space Manipulation in Latent Audio Tokenizers

arXiv:2605.11192v1 Announce Type: cross Abstract: Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as frame-lev

researcharxiv-cs-lg

Express Your Doubts -- Probabilistic World Modeling Should not be Based on Token logprobs

arXiv:2505.02072v2 Announce Type: replace Abstract: Language modeling has shifted in recent years from a distribution over strings to prediction models with textual inputs and outputs for general-purp

researcharxiv-cs-cl

Extending Kernel Trick to Influence Functions

arXiv:2605.11239v1 Announce Type: new Abstract: In this paper, we present a dual representation of the influence functions, whose computational complexity scales with dataset size rather than model si

Tools

External content is scanned in parallel by ML classifiers and the BrowseSafe model before agents act on it. File connector data is encrypted…

External content is scanned in parallel by ML classifiers and the BrowseSafe model before agents act on it. File connector data is encrypted in transit and at rest, uploaded files automatically delete

toolsperplexity--x

Fair Conformal Classification via Learning Representation-Based Groups

arXiv:2605.12195v1 Announce Type: new Abstract: Conformal prediction methods provide statistically rigorous marginal coverage guarantees for machine learning models, but such guarantees fail to accoun

FAME: Feature Activation Map Explanation on Image Classification and Face Recognition

arXiv:2605.12017v1 Announce Type: new Abstract: Deep Learning has revolutionized machine learning, reaching unprecedented levels of accuracy, but at the cost of reduced interpretability. Especially in

Fast Image Super-Resolution via Consistency Rectified Flow

arXiv:2605.12377v1 Announce Type: new Abstract: Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step s

applicationsarxiv-cs-cv

Hardware

Fast MoE Inference via Predictive Prefetching and Expert Replication

arXiv:2605.11537v1 Announce Type: new Abstract: The Mixture of Experts (MoE) architecture has become a fundamental building block in state-of-the-art large language models (LLMs), improving domain-spe

hardwarearxiv-cs-lg

FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling

arXiv:2605.11428v1 Announce Type: new Abstract: Exploratory analysis of high-dimensional data rarely stops at a single embedding. In practice, analysts rerun dimensionality reduction after changing pr

Industry

FCC angers small carriers by helping AT&T and Starlink buy EchoStar spectrum

The FCC approved EchoStar's 40 billion spectrum sales to AT&T and SpaceX , with AT&T acquiring approximately 50 megahertz of nationwide spectrum for 23 billion and SpaceX purchasing 65 megahertz for 1

industryars-technica

Feasible Force Set Shaping for a Payload-Carrying Platform Consisting of Tiltable Multiple UAVs Connected Via Passive Hinge Joints

arXiv:2503.00341v2 Announce Type: replace Abstract: This paper presents a method for shaping the feasible force set of a payload-carrying platform composed of multiple Unmanned Aerial Vehicles (UAVs)

researcharxiv-cs-ro

FeatMap: Understanding image manipulation in the feature space and its implications for feature space geometry

arXiv:2605.11203v1 Announce Type: cross Abstract: Intermediate feature representations represent the backbone for the expressivity and adaptability of deep neural networks. However, their geometric st

Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning

arXiv:2605.11815v1 Announce Type: new Abstract: Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for join

Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention

arXiv:2605.11752v1 Announce Type: new Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods ass

researcharxiv-cs-lg

FedOUI: OUI-Guided Client Weighting for Federated Aggregation

arXiv:2605.11571v1 Announce Type: new Abstract: Federated learning usually aggregates client updates using dataset size or gradient-level criteria, while overlooking internal signals about how each cl

FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA

arXiv:2602.23638v2 Announce Type: replace Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a dis

local-aiarxiv-cs-lg

FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement

arXiv:2605.11122v1 Announce Type: cross Abstract: Federated Learning remains highly susceptible to backdoor attacks--malicious clients inject targeted behaviours into the global model. Existing defens

Tutorials

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models

arXiv:2605.11527v1 Announce Type: new Abstract: Diffusion models are the leading approach for tabular data synthesis and are increasingly used to share sensitive records. Whether they actually protect

tutorialsarxiv-cs-lg

Few-Shot Synthetic Data Generation with Diffusion Models for Downstream Vision Tasks

arXiv:2605.11898v1 Announce Type: new Abstract: Class imbalance is a persistent challenge in visual recognition, particularly in safety-critical domains where collecting positive examples is expensive

safetyarxiv-cs-cv

fg-expo: Frontier-guided exploration-prioritized policy optimization via adaptive kl and gaussian curriculum

model-releasesarxiv-cs-cl

arXiv:2605.11403v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, with Group Relative Policy Opti

Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models

arXiv:2605.12374v1 Announce Type: new Abstract: Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools

safetyarxiv-cs-cv

Finally we're going to get shots of Starship from a deployed payload:

model-releaseselon-musk--x

Finally we're going to get shots of Starship from a deployed payload: Starship’s twelfth flight test will debut the next generation Starship and Super Heavy vehicles, powered by the next evolution of

Tutorials

Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI

In this post, we demonstrate how to build a secure, complete LLM fine-tuning workflow that integrates Unity Catalog with Amazon SageMaker AI using Amazon EMR Serverless for preprocessing. The solution

tutorialsaws-ml-blog

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

model-releasesarxiv-cs-ro

arXiv:2603.28561v2 Announce Type: replace Abstract: The growing deployment of small Unmanned Aerial Systems (sUASs) in low-altitude airspaces has increased the need for reliable tactical deconfliction

Fine, you can do sorcery sometimes. But go all the way: circles of salt, grimoires, robes.

applicationsethan-mollick--x

This post likely discusses a humorous or philosophical take on the conditional acceptance of magical practices, suggesting that if one is going to engage in sorcery, they should commit fully to all th

Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

arXiv:2602.01682v2 Announce Type: replace Abstract: We study online inverse linear optimization, also known as contextual recommendation, where a learner sequentially infers an agent's hidden objectiv

agentsarxiv-cs-lg

Finite Sentence-Interface Control for Learning Bounded-Fan-Out Linear MCFGs under Fixed Monoid Typing

arXiv:2605.11644v1 Announce Type: cross Abstract: We study positive-data learning of bounded-fan-out linear multiple context-free grammars under a fixed explicit finite monoid homomorphism (h). The ma

researcharxiv-cs-lg

Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance

arXiv:2605.11001v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are a simple surrogate-modelling paradigm for partial differential equations, but their standard strong-form re

FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity

arXiv:2605.11869v1 Announce Type: new Abstract: While the overall inference latency of Video Diffusion Transformers (DiTs) can be substantially reduced through model distillation, per-step inference l

researcharxiv-cs-cv

FLAME: A New Dataset on FLemish Accounts of Momentary Experiences

model-releasesarxiv-cs-cl

arXiv:2504.14707v3 Announce Type: replace Abstract: We introduce FLAME (FLemish Accounts of Momentary Experiences), a new corpus of nearly 25,000 daily personal narratives in Belgian-Dutch (Flemish),

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning

arXiv:2511.14715v3 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy. However, it remains vulnerable to malicious clients who

FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers

arXiv:2512.07150v2 Announce Type: replace-cross Abstract: Deep generative models are powerful priors for imaging inverse problems, but training-free solvers for latent flow models face a practical fin

Focusable Monocular Depth Estimation

model-releasesarxiv-cs-cv

arXiv:2605.11756v1 Announce Type: new Abstract: Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not disting

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning

arXiv:2506.19417v2 Announce Type: replace Abstract: Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrat

agentsarxiv-cs-lg

For more details: https://docs.comfy.org/interface/app-mode

ComfyUI's app mode is a feature that provides an alternative interface for the application, likely offering a simplified or specialized user experience compared to the standard interface. This documen

local-aicomfyui--x

ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching

arXiv:2605.11048v1 Announce Type: new Abstract: Existing imitation learning methods enable robots to interact autonomously with the physical environment. However, contact-rich manipulation tasks remai

safetyarxiv-cs-ro

Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation

arXiv:2605.11144v1 Announce Type: new Abstract: We introduce Forecast-aware Gaussian Splatting (Forecast-GS), a predictive 3D representation framework for language-conditioned robotic manipulation. Wh

agentsarxiv-cs-ro

Fractal Graph Contrastive Learning

arXiv:2505.11356v4 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) relies on semantically consistent graph augmentations, but common local perturbations provide limited control over

applicationsarxiv-cs-lg

Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training

model-releasesarxiv-cs-cl

arXiv:2605.11416v1 Announce Type: new Abstract: Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze o

From a robot expert https://x.com/mattbeane/status/2054667222040330265?s=20

applicationsethan-mollick--x

From a robot expert https://x.com/mattbeane/status/2054667222040330265?s=20 @emollick It is very impressive. Deformable goods? Reflection issues? Imperfect labels? Impossible problems just a few years

From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation

arXiv:2605.11613v1 Announce Type: new Abstract: On-policy self-distillation has emerged as a promising paradigm for post-training language models, in which the model conditions on environment feedback

From Image Hashing to Scene Change Detection

arXiv:2605.12259v1 Announce Type: new Abstract: Image hashing provides compact representations for efficient storage and retrieval but is inherently limited to global comparison and cannot reason abou

From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation

arXiv:2605.12167v1 Announce Type: cross Abstract: Video generation models offer a promising imagination mechanism for robot manipulation by predicting long-horizon future observations, but effectively

safetyarxiv-cs-cv

From Message-Passing to Linearized Graph Sequence Models

arXiv:2605.12358v1 Announce Type: new Abstract: Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of moder

From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review

arXiv:2605.12303v1 Announce Type: cross Abstract: High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted

researcharxiv-cs-cv