safety

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

arXiv:2606.08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectra

Safety

Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method

DGX agent

arXiv:2606.08090v1 Announce Type: cross Abstract: Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based d

safetyarxiv-cs-ai

9 Jun 2026

Few-step Cofolding with All-Atom Flow Maps

arXiv:2606.08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-liga

FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching

arXiv:2601.05212v2 Announce Type: replace Abstract: Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain

Frankenstein in the Pipeline: Computational Epistemicide in Facial Recognition

arXiv:2606.07628v1 Announce Type: cross Abstract: While the eugenic roots of computer vision are well-documented in critical technology studies, less attention has been paid to the operational mechani

From A to B to A: Palindromic Zero-Shot Voice Conversion with Non-Parallel Data

arXiv:2606.08843v1 Announce Type: cross Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source

@GaryMarcus @Pontifex Specifically that the Pope is a powerful, influential individual serving as head of one of humanity's most enduring in…

@GaryMarcus @Pontifex Specifically that the Pope is a powerful, influential individual serving as head of one of humanity's most enduring institutions who is asserting clearly & unequivocally the prim

Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses

arXiv:2606.08921v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-wor

Generalizing Fair Top-k Selection: An Integrative Approach

arXiv:2603.04689v3 Announce Type: replace-cross Abstract: Fair top-k selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups amon

Generative Reasoning Re-ranker

arXiv:2602.07774v5 Announce Type: replace-cross Abstract: Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and wor

GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model

arXiv:2512.20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and

Guided Discovery of New Behaviors using Diffusion Policies

arXiv:2606.08743v1 Announce Type: new Abstract: Diffusion models have become a powerful tool for generative modeling in robotics, with diffusion policies excelling at modeling multimodal action-trajec

GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence

arXiv:2606.08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications. Recent advancements leverage multiple pre-trained f

HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning

arXiv:2606.08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains

Harness Engineering for Physical AI: Robot Middleware Is the Harness Layer

arXiv:2606.09416v1 Announce Type: cross Abstract: Robot middleware faces a new role in the era of Physical AI. Learned policies, planners, and vision-language-action (VLA) models now enter deployed ro

HARP: Efficient Data Selection for Finetuning Large Language Models

arXiv:2606.07690v1 Announce Type: cross Abstract: Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repea

HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging

arXiv:2606.09110v1 Announce Type: new Abstract: Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynamic

Hierarchical Projection for Adaptive Knowledge Transfer

arXiv:2606.08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related informa

HiMat: DiT-based Ultra-High Resolution SVBRDF Generation

arXiv:2508.07011v5 Announce Type: replace Abstract: Creating ultra-high-resolution spatially varying bidirectional reflectance functions (SVBRDFs) is critical for photorealistic 3D content creation, t

How Reliable are Fairness Audits with Unreliable Data?

arXiv:2506.23033v2 Announce Type: replace Abstract: Fairness audits are a key component of responsible machine-learning deployment. Yet, the reliability of audit recommendations under incomplete prote

How Well Do Latent World Models Understand Partially Observable Safety Constraints?

arXiv:2510.06492v2 Announce Type: replace Abstract: Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling r

Human-Centered Benchmarking of Driver Monitoring Models

arXiv:2606.08123v1 Announce Type: cross Abstract: Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always co

Humanity's ability to know, reason, judge, and act well is the foundation of science, democracy, crisis response, & management of AI itself.…

Humanity's ability to know, reason, judge, and act well is the foundation of science, democracy, crisis response, & management of AI itself. AI poses serious risks to that foundation. New paper on epi

safetyyoshua-bengio--x

Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations

arXiv:2606.08855v1 Announce Type: new Abstract: This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The an

Hyperspectral Smoke Segmentation via Mixture of Prototypes

arXiv:2602.10858v2 Announce Type: replace Abstract: Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations

I have said exactly this before and still believe it to be true

I have said exactly this before and still believe it to be true this is why the labs are pushing visions of mass unemployment and replacing the white-collar workforce it's not because the models can d

i used to be the only person saying the emperor was wearing no clothes. those days are over. the only people buying SpaceX and OpenAI at lis…

i used to be the only person saying the emperor was wearing no clothes. those days are over. the only people buying SpaceX and OpenAI at list price will be the stragglers who still can’t see it. It de

i would love to see OpenAI take steps around this. how about starting by compensating artists and writers for their work and allowing them c…

i would love to see OpenAI take steps around this. how about starting by compensating artists and writers for their work and allowing them control over how their work is used? like Sam promised when w

IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment

arXiv:2606.08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes. Creations by

I'm really not interested in testing a new closed-weight model in the expensive tier. What's the point? I know what will happen: 1. They pre…

I'm really not interested in testing a new closed-weight model in the expensive tier. What's the point? I know what will happen: 1. They present the model. 2. The model beats the competition on benchm

Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses

arXiv:2606.08400v1 Announce Type: cross Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great pote

Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety

arXiv:2606.08281v1 Announce Type: new Abstract: Physical human-robot interaction (pHRI) demands simultaneous trajectory accuracy and compliant safety under unplanned contact. Classical impedance contr

InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs

arXiv:2606.08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely o

Industrial policy for the Intelligence Age

This OpenAI publication likely discusses how governments and policymakers should approach industrial policy in an era dominated by artificial intelligence and computational intelligence capabilities.

safetyopenai

Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning

arXiv:2604.07848v2 Announce Type: replace Abstract: Multi-task learning shows strikingly inconsistent results -- sometimes joint training helps substantially, sometimes it actively harms performance -

Instrumental convergence and power-seeking

arXiv:2606.08832v1 Announce Type: new Abstract: Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is

IntentNav: Learning Spatial-Visual Object Navigation from Human Demonstrations

arXiv:2606.08029v1 Announce Type: new Abstract: Object navigation requires a robot to search for an unobserved target in an unknown environment by deciding where to explore next under partial observab

IR-SIM: A Lightweight Skill-Native Simulator for Navigation, Learning, and Benchmarking

arXiv:2606.08729v1 Announce Type: cross Abstract: Simulation plays a key role in automated robotics research supported by large language models (LLMs). However, existing simulators often require custo

justine may kid about being bullied, but the answer sure sounds more like 2026 LLM-powered-AI slop than AGI to me.

Gary Marcus critiques an AI response about bullying, arguing that it exemplifies typical large language model output rather than demonstrating genuine artificial general intelligence (AGI). The post s

Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models

arXiv:2602.12996v2 Announce Type: replace-cross Abstract: Knowledge augmentation has significantly enhanced the performance of Large Language Models (LLMs) in knowledge-intensive tasks. However, exist

LAEI: Layered Autonomous Edge Intelligence Framework for Robust UAV Swarm Operations

arXiv:2606.09099v1 Announce Type: new Abstract: Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty,

Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences

arXiv:2606.07629v1 Announce Type: cross Abstract: Current approaches to aligning large language models (LLMs) aggregate diverse human preferences into a single reward signal, effectively optimizing fo

Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation

arXiv:2606.08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single de

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

arXiv:2601.22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed b

Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes

arXiv:2606.07908v1 Announce Type: new Abstract: Derivative-controlled networks based on ChainzRule (CR) combine cubic polynomial layers with a lightweight forward-mode per-layer Jacobian penalty (DREG

LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI

arXiv:2606.08131v1 Announce Type: cross Abstract: Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncert

Learning Quantized Continuous Controllers for Integer Hardware

arXiv:2511.07046v4 Announce Type: replace-cross Abstract: Deploying continuous-control reinforcement learning policies on embedded hardware requires meeting tight latency and power budgets. Small FPGA

Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO

arXiv:2606.09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel att