All entries

Explanation Fairness in Large Language Models: An Empirical Analysis of Disparities in How LLMs Justify Decisions Across Demographic Groups

arXiv:2605.08671v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed not only to make decisions but to explain them. While AI decision fairness has been studied ext

Research

Explicit and Effectively Symmetric Schemes for Neural SDEs on Lie Groups

DGX agent

arXiv:2509.20599v2 Announce Type: replace Abstract: Backpropagation through (neural) SDE solvers is traditionally approached in two ways: discretise-then-optimise, which offers accurate gradients but

researcharxiv-cs-lg

12 May 2026

Explicit Bounds on the Hausdorff Distance for Truncated mRPI Sets via Norm-Dependent Contraction Rates

arXiv:2511.18374v2 Announce Type: replace Abstract: We derive a computable closed-form upper bound on the Hausdorff distance between a truncated minimal robust positively invariant (mRPI) set and its

researcharxiv-cs-ro

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness

arXiv:2509.13332v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly adopted as automated judges in benchmarking and reward modeling, ensuring their reliability, effici

Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion

arXiv:2605.09944v1 Announce Type: new Abstract: Robust humanoid stair climbing remains challenging due to geometric discontinuities, sensitivity to step height variations, and perception uncertainty i

safetyarxiv-cs-ro

Exploitation Without Deception: Dark Triad Feature Steering Reveals Separable Antisocial Circuits in Language Models

arXiv:2605.09773v1 Announce Type: cross Abstract: We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3

Exploration-Driven Optimization for Test-Time Large Language Model Reasoning

arXiv:2605.09853v1 Announce Type: new Abstract: Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (L

safetyarxiv-cs-lg

Exploring 6D Object Pose Estimation with Deformation

arXiv:2604.06720v2 Announce Type: replace Abstract: We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption t

Exploring and Exploiting Stability in Latent Flow Matching

arXiv:2605.08398v1 Announce Type: cross Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capaci

Exploring the AI Obedience: Why is Generating a Pure Color Image Harder than CyberPunk?

arXiv:2603.00166v2 Announce Type: replace-cross Abstract: Recent advances in generative AI have shown human-level performance in complex content creation. However, we identify a 'Paradox of Simplicity

expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling

arXiv:2605.09923v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, where Group Relative Policy Optim

Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality

arXiv:2605.10206v1 Announce Type: cross Abstract: Distributional causal inference requires estimating not only average treatment effects but also interventional outcome distributions, including quanti

safetyarxiv-cs-lg

Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering

arXiv:2605.10318v1 Announce Type: new Abstract: Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong prog

researcharxiv-cs-cl

ExtraVAR: Stage-Aware RoPE Remapping for Resolution Extrapolation in Visual Autoregressive Models

arXiv:2605.10045v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models have emerged as a strong alternative to diffusion for image synthesis, yet their fixed training resolution prevents d

Applications

Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud

arXiv:2605.08971v1 Announce Type: cross Abstract: Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time

applicationsarxiv-cs-ai

f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignment

arXiv:2602.05946v3 Announce Type: replace Abstract: Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-pre

safetyarxiv-cs-lg

FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models

arXiv:2605.09081v1 Announce Type: cross Abstract: We introduce the first universal pretraining corpus for industrial time-series data: FactoryNet. 51M datapoints across 23k end-to-end task executions

Factual recall in linear associative memories: sharp asymptotics and mechanistic insights

arXiv:2605.10795v1 Announce Type: cross Abstract: Large language models demonstrate remarkable ability in factual recall, yet the fundamental limits of storing and retrieving input--output association

Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2605.08434v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral clonin

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

arXiv:2605.08198v1 Announce Type: cross Abstract: We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applica

Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI

arXiv:2605.09852v1 Announce Type: new Abstract: Machine learning algorithms are being used in high-stakes decisions, including those in criminal justice, healthcare, credit, and employment. The resear

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

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

model-releaseselon-musk--x

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

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

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

Local Ai

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

local-aiarxiv-cs-lg

Local Ai

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

local-aiarxiv-cs-lg

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

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