Search: “safety”

Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis

arXiv:2208.01899v2 Announce Type: replace Abstract: Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found tha

Unified Map Prior Encoder for Mapping and Planning

DGX agent

arXiv:2605.02762v1 Announce Type: new Abstract: Online mapping and end-to-end (E2E) planning in autonomous driving remain largely sensor-centric, leaving rich map priors, including HD/SD vector maps,

safetyarxiv-cs-cv

5 May 2026

Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics

arXiv:2605.02884v1 Announce Type: new Abstract: Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such a

US labor experts and economists warn US unemployment benefits are unlikely to protect against AI job losses, and the GOP-led Congress is doing little to prepare (New York Times)

New York Times: US labor experts and economists warn US unemployment benefits are unlikely to protect against AI job losses, and the GOP-led Congress is doing little to prepare — Economists aren't sur

safetytechmeme

VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation

arXiv:2601.23286v2 Announce Type: replace Abstract: While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, o

VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids

arXiv:2605.01518v1 Announce Type: new Abstract: The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform com

safetyarxiv-cs-ro

We wanted the singularity; we got the circularity.

Gary Marcus critiques the gap between AI aspirations and current reality, suggesting that instead of achieving transformative 'singularity' breakthroughs, AI development has become cyclical—repeating

When Attention Collapses: Residual Evidence Modeling for Compositional Inference

arXiv:2605.02323v1 Announce Type: new Abstract: Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data a

While AI leaders warn superintelligent AI could cause human extinction, governments are asleep at the wheel. The companies building superint…

While AI leaders warn superintelligent AI could cause human extinction, governments are asleep at the wheel. The companies building superintelligence admit the danger, yet expect to create the tech wi

safetyconnor-leahy--x

Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework

arXiv:2605.01416v1 Announce Type: cross Abstract: The increasing scale and complexity of online platforms raises critical policy questions around harmful content, digital well-being, and user autonomy

Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics

arXiv:2505.13150v2 Announce Type: replace Abstract: Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time traini

A Policy-Driven DRL Framework for System-Level Tradeoff Control in NR-U/Wi-Fi Coexistence

arXiv:2605.00457v1 Announce Type: cross Abstract: The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a system-level resource coordination problem, where heterogeneous channel access m

a question that suddenly feels relevant: what happens if OpenAI loses?

Gary Marcus explores hypothetical scenarios and consequences if OpenAI were to fail or lose its competitive position in the AI industry. The post likely examines implications for AI development, the b

A unified perspective on fine-tuning and sampling with diffusion and flow models

arXiv:2605.00229v1 Announce Type: cross Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base

Adaptive Equilibrium: Dynamic Weighting Framework for Generalized Interruption of DeepFake Models

arXiv:2605.00443v1 Announce Type: cross Abstract: The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation o

Agent Capsules: Quality-Gated Granularity Control for Multi-Agent LLM Pipelines

arXiv:2605.00410v1 Announce Type: new Abstract: A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings,

Almost every warning that I have issued over the last several years has come true. You better hope to God or Darwin or whoever you believe i…

Almost every warning that I have issued over the last several years has come true. You better hope to God or Darwin or whoever you believe in that this one is wrong. Accidental nuclear war is a potent

🌐At the @UN, @Yoshua_Bengio briefed @antonioguterres on AI governance, followed by a reception with @CanadaUN & @DavidLametti opened by @Eg…

🌐At the @UN, @Yoshua_Bengio briefed @antonioguterres on AI governance, followed by a reception with @CanadaUN & @DavidLametti opened by @EgriseldaL As AI risks grow, global coordination matters. 🔗AI D

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Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

arXiv:2508.06361v4 Announce Type: replace Abstract: Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A signifi

Bias in Large Language Models: Origin, Evaluation, and Mitigation

arXiv:2411.10915v2 Announce Type: replace Abstract: Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This

BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis

arXiv:2605.00632v1 Announce Type: new Abstract: Automatic generation of executable Blender code from natural language remains challenging, with state-of-the-art LLMs producing frequent syntactic error

BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception

arXiv:2605.00405v1 Announce Type: new Abstract: Most existing heterogeneous cooperative perception methods depend on prior preparation like offline joint training or tailored collaborator-model adapta

Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation

arXiv:2602.00665v3 Announce Type: replace Abstract: Customer-service question answering (QA) systems increasingly rely on conversational language understanding. While Large Language Models (LLMs) achi

Confirmed 2.5 years later, by Greg Brockman.

Confirmed 2.5 years later, by Greg Brockman. Remember how Sam Altman told the US Senate he had no “direct” investment in OpenAI? and how they gushed over his apparent selflessness? 👉He didn’t mention

Data Deletion Can Help in Adaptive RL

arXiv:2605.00298v1 Announce Type: new Abstract: Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Mark

Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations

arXiv:2512.20260v5 Announce Type: replace Abstract: Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scene

Decentralized Proximal Stochastic Gradient Langevin Dynamics

arXiv:2605.00723v1 Announce Type: cross Abstract: We propose Decentralized Proximal Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Markov chain Monte Carlo (MCMC) algorithm for samp

Model Releases

Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive

model-releasesarxiv-cs-ro

arXiv:2605.00066v1 Announce Type: new Abstract: Open-loop evaluation offers fast, reproducible assessment of autonomous driving planners, but its ability to predict real closed-loop driving performanc

@dromanocpm @GaryMarcus Two stood up. I know that one told the truth. Gary Marcus and Sam Altman, Senate Hearing on Oversight for Artificial…

Gary Marcus and Sam Altman testified before the Senate regarding AI oversight, with Marcus noting that two individuals stood up during the hearing and asserting that one of them told the truth. This r

Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

arXiv:2605.00238v1 Announce Type: new Abstract: Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa.

Fair Dataset Distillation via Cross-Group Barycenter Alignment

arXiv:2605.00185v1 Announce Type: new Abstract: Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different dem

Fairness of Classifiers in the Presence of Constraints between Features

arXiv:2605.00592v1 Announce Type: new Abstract: In Machine Learning, an accepted definition of fairness of a decision taken by a classifier is that it should not depend on protected features, such as

Foundation AI Models for Aerosol Optical Depth Estimation from PACE Satellite Data

arXiv:2605.00678v1 Announce Type: new Abstract: Aerosol Optical Depth (AOD) retrieval is essential for Earth observation, supporting applications from air quality monitoring to climate studies. Conven

FreeRet: MLLMs as Training-Free Retrievers

arXiv:2509.24621v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc

@GaryMarcus to yann - yank overhyped AI claims after they've been marcussed https://julian-goldstahl.blogspot.com/2025/09/dictionairy.html

Gary Marcus criticizes overhyped AI claims, with the term 'marcussed' referring to his practice of debunking exaggerated AI assertions. The post appears to reference a discussion with Yann LeCun about

🚨 GREG BROCKMAN JUST CONFESSED UNDER OATH Q: You have an ownership interest in this cap profit company. Brockman: That is accurate. Q: And …

🚨 GREG BROCKMAN JUST CONFESSED UNDER OATH Q: You have an ownership interest in this cap profit company. Brockman: That is accurate. Q: And you invested 0 in order to acquire that interest. Correct? Br

Greg Brockman’s diary is well on its way to being the most famous, impactful diary of the 21st century.

Greg Brockman’s diary is well on its way to being the most famous, impactful diary of the 21st century. takeaway from Greg Brockman testimony at Elon vs. OpenAI trial today is that no grown man should

Hey filthy rich AI warlords who have alienated most of the population, don’t say I didn’t try to warn you.🤷‍♂️

Hey filthy rich AI warlords who have alienated most of the population, don’t say I didn’t try to warn you.🤷‍♂️ “Bubble or not, the AI backlash is validating what one researcher and critic has been say

How 'emotion AI', the use of facial and sentiment analysis tools to track workers' moods, is seeping into white-collar jobs amid concerns over privacy and bias (Ellen Cushing/The Atlantic)

Ellen Cushing / The Atlantic: How “emotion AI”, the use of facial and sentiment analysis tools to track workers' moods, is seeping into white-collar jobs amid concerns over privacy and bias — The good

safetytechmeme

How Language Models Process Out-of-Distribution Inputs: A Two-Pathway Framework

arXiv:2605.00269v1 Announce Type: new Abstract: Recent white-box OOD detection methods for LLMs -- including CED, RAUQ, and WildGuard confidence scores -- appear effective, but we show they are struct

HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs

arXiv:2605.00820v1 Announce Type: cross Abstract: We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closu

If you need proof of why AI output can't be trusted by default watch that page for more than 1 minute for any given LLM that produces output…

If you need proof of why AI output can't be trusted by default watch that page for more than 1 minute for any given LLM that produces output in there. An amazing time capsule of the unevenness of curr

Import AI 455: AI systems are about to start building themselves.

This newsletter entry discusses advancements in automating AI research and development processes, exploring how artificial intelligence systems are becoming capable of autonomously designing and impro

safetyimport-ai

InpaintSLat: Inpainting Structured 3D Latents via Initial Noise Optimization

arXiv:2605.00664v1 Announce Type: new Abstract: We present a training-free approach for controllable 3D inpainting based on initial noise optimization. In the structured 3D latent diffusion framework,

Jensen Huang said Nvidia's market share of AI accelerators in China has 'now dropped to zero' and that US export policy 'has already largely backfired' (Anton Shilov/Tom's Hardware)

Anton Shilov / Tom's Hardware: Jensen Huang said Nvidia's market share of AI accelerators in China has “now dropped to zero” and that US export policy “has already largely backfired” — US export restr

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Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs

arXiv:2408.11513v2 Announce Type: replace Abstract: This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regul

Learn where to Click from Yourself: On-Policy Self-Distillation for GUI Grounding

arXiv:2605.00642v1 Announce Type: cross Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves as a core capabili

Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels

arXiv:2605.00718v1 Announce Type: new Abstract: Knee osteoarthritis (OA) assessment involves a natural but often underused label hierarchy: a coarse binary OA decision and a fine-grained Kellgren--Law