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Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems

arXiv:2605.10604v1 Announce Type: cross Abstract: Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might requir

safetyarxiv-cs-ai

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Model Releases

Falcon 9 launches NROL-172 to orbit from pad 4E in California

DGX agent

SpaceX's Falcon 9 rocket successfully launched the NROL-172 classified national reconnaissance payload from Space Launch Complex 4E at Vandenberg Space Force Base in California. This mission was condu

model-releaseselon-musk--x

12 May 2026

Tutorials

Falcon 9’s first stage lands on the Of Course I Still Love You droneship

SpaceX successfully landed the Falcon 9 first stage on the 'Of Course I Still Love You' autonomous droneship, demonstrating the company's reusable rocket technology and land-based recovery capability.

tutorialselon-musk--x

Fashion Florence: Fine-Tuning Florence-2 for Structured Fashion Attribute Extraction

arXiv:2605.09827v1 Announce Type: cross Abstract: We present Fashion Florence, a Florence-2 vision-language model fine-tuned with LoRA to extract structured fashion attributes from clothing images. Gi

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition

model-releasesarxiv-cs-cv

arXiv:2605.10127v1 Announce Type: new Abstract: Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

arXiv:2510.23074v2 Announce Type: replace-cross Abstract: We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) again

researcharxiv-cs-cl

Fast mode for Claude Opus 4.7 is now available in Cursor! It's 2.5x the speed at 6x the cost. For most tasks, we recommend using the standar…

Cursor has released fast mode for Claude Opus 4.7, offering 2.5x faster processing speeds but at 6x the cost compared to standard mode. The announcement suggests that for most tasks, standard mode rem

model-releasescursor--x

Fast mode for Claude Opus 4.7 is now available in research preview on the API and in Claude Code.

model-releasesboris-cherny--x

Anthropic has released a fast mode for Claude Opus 4.7, now available in research preview for both the API and Claude Code, offering improved performance for compatible workloads. This feature allows

Tools

Fast mode for Opus 4.7 available on AI Gateway

Vercel has introduced a fast mode option for Claude Opus 4.7 on its AI Gateway service, enabling faster response times for users integrating the model through their platform. This feature likely provi

toolsvercel-blog

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

arXiv:2605.09214v1 Announce Type: cross Abstract: Kullback-Leibler (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of reverse or forward KL. Recent studies have demo

safetyarxiv-cs-ai

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration

arXiv:2605.10330v1 Announce Type: cross Abstract: We propose a novel adaptive Mixture-of-Experts (MoE) framework for time series forecasting that enhances expert specialization by incorporating expert

Industry

Faster Queries and New Capabilities with the Open-Source Databricks JDBC Driver

The Databricks JDBC driver is an open-source tool that enables Java applications to connect to Databricks SQL warehouses and clusters with improved query performance and enhanced capabilities. The dri

industrydatabricks

Industry

FDA chief resigns after Trump admin forced approval of fruity e-cigs

FDA Commissioner Dr. Marty Makary resigned after a rocky tenure marked by complaints from health industry executives, anti-abortion activists, and vaping lobbyists, stepping down after just over a yea

industryars-technica

Applications

Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices

arXiv:2509.21000v2 Announce Type: replace Abstract: Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as

applicationsarxiv-cs-lg

Feature Repulsion and Spectral Lock-in: An Empirical Study of Two-Layer Network Grokking

arXiv:2605.08119v1 Announce Type: cross Abstract: Tian (2025) proves a repulsion theorem (Theorem 6) for the matrix B = (widetilde{F}^op widetilde{F} + eta I)^{-1} during the interactive feature-learn

Feature Rivalry in Sparse Autoencoder Representations: A Mechanistic Study of Uncertainty-Driven Feature Competition in LLMs

model-releasesarxiv-cs-cl

arXiv:2605.08149v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) decompose large language model representations into interpretable features, but how these features interact under uncertain

Featurized Occupation Measures for Structured Global Search in Numerical Optimal Control

model-releasesarxiv-cs-ro

arXiv:2603.16231v2 Announce Type: replace-cross Abstract: Numerical optimal control has long been split between globally structured but dimensionally intractable Hamilton--Jacobi--Bellman (HJB) method

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation

arXiv:2601.22204v2 Announce Type: replace Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client particip

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

arXiv:2605.09428v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate fr

Federated Concept-Based Models: Interpretable models with distributed supervision

arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann

Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage

model-releasesarxiv-cs-cl

arXiv:2605.09986v1 Announce Type: cross Abstract: Training a language model on data scattered across bandwidth-limited nodes that cannot be centralized is a setting that arises in clinical networks, e

FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference

arXiv:2605.08760v1 Announce Type: new Abstract: Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchang

FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning

arXiv:2605.09144v1 Announce Type: new Abstract: Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward

FERA: Uncertainty-Aware Federated Reasoning for Large Language Models

arXiv:2605.10082v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across

researcharxiv-cs-cl

Fetal Brain Imaging: A Composite Neural Network Approach for Keyframe Detection in Ultrasound Videos

arXiv:2605.09750v1 Announce Type: new Abstract: This article presents a novel approach to keyframe detection in ultrasound videos, with a particular focus on fetal brain imaging. The proposed model is

researcharxiv-cs-cv

Few-Click-Driven Interactive 3D Segmentation with Semantic Embedding

arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe

safetyarxiv-cs-cv

Field-Localized Forgery Detection for Digital Identity Documents

arXiv:2605.09089v1 Announce Type: cross Abstract: Digital identity verification systems used in remote onboarding rely on document images to authenticate users, making them vulnerable to localized man

researcharxiv-cs-ai

Filtering Memorization from Parameter-Space in Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t

Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain

model-releasesarxiv-cs-cl

arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility

Applications

Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting

arXiv:2602.07126v2 Announce Type: replace Abstract: Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table s

applicationsarxiv-cs-lg

Finer is Better (with the Right Scaling)

arXiv:2605.08565v1 Announce Type: new Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats. Intuitively, f

FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media

arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent

researcharxiv-cs-cl

FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2502.18834v2 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can

Tutorials

First, Do No Harm: AI Supervisor Scaffolds Novice Growth in Counselor Education

arXiv:2508.09042v3 Announce Type: replace Abstract: The most dangerous mistakes a novice counselor makes are not the obvious ones: they are utterances that sound caring while quietly violating profess

tutorialsarxiv-cs-cl

Applications

First Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits

arXiv:2605.09277v1 Announce Type: new Abstract: We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy c

applicationsarxiv-cs-lg

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs

arXiv:2605.08894v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing

researcharxiv-cs-ai

Fitting Multilinear Polynomials for Logic Gate Networks

arXiv:2605.08657v1 Announce Type: cross Abstract: We study learnable logic gate networks that stack layers of 2-input Boolean gates to build combinational circuits. Every 2-input gate has a unique mul

Fix the Loss, Not the Radius: Rethinking the Adversarial Perturbation of Sharpness-Aware Minimization

model-releasesarxiv-cs-lg

arXiv:2605.10183v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss within a fixed parameter-space radius neighborhood. SAM and

Fixed-Point Neural Optimal Transport without Implicit Differentiation

arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo

Flag Varieties: A Geometric Framework for Deep Network Alignment

arXiv:2605.09861v1 Announce Type: cross Abstract: Alignment, the tendency of adjacent weight matrices in deep networks to develop compatible subspace orientations, underlies gradient flow, Neural Coll

safetyarxiv-cs-ai

FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning

model-releasesarxiv-cs-lg

arXiv:2605.09355v1 Announce Type: new Abstract: Real-world model deployment across multiple domains requires multimodal models to operate under two complementary regimes: (1) multi-task pretraining, t

Flame3D: Zero-shot Compositional Reasoning of 3D Scenes with Agentic Language Models

arXiv:2605.09218v1 Announce Type: cross Abstract: 3D scene understanding spans reasoning about free space, object grounding, hypothetical object insertions, complex geometric relationships, and integr

FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors

arXiv:2605.08594v1 Announce Type: cross Abstract: Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with u

FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation

arXiv:2605.09430v1 Announce Type: new Abstract: Large-scale autoregressive models have demonstrated remarkable capabilities in image generation. However, their sequential raster-scan decoding relies o

safetyarxiv-cs-cv

FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching

model-releasesarxiv-cs-cv

arXiv:2605.09003v1 Announce Type: new Abstract: Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, th

FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration

arXiv:2605.08520v1 Announce Type: new Abstract: LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottl

Hardware

FlashSVD v1.5: Making Low-Rank Transformers Inference Actually Fast

arXiv:2605.08314v1 Announce Type: cross Abstract: SVD-based Low-rank compression reduces transformer parameters and nominal FLOPs, but these savings often translate poorly into real LLM serving speedu

hardwarearxiv-cs-ai

FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors

arXiv:2605.08640v1 Announce Type: new Abstract: Plug-and-play (PnP) methods for solving inverse problems have recently achieved strong performance by leveraging denoising priors based on powerful gene

researcharxiv-cs-cv