safety

Wild! Every Democrat should read this and think hard about how this could be.

Wild! Every Democrat should read this and think hard about how this could be. Americans are concerned about AI, hate data centers, and hate Trump, who has loudly embraced the industry over and over ag

safetygary-marcus--x

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Will Anthropic and OpenAI succeed in meaningfully slowing frontier AI progress, whether through US government restrictions, voluntary restra…

DGX agent

In a September 2026 poll titled “Will Anthropic and OpenAI succeed in meaningfully slowing frontier AI progress…”, participants were asked whether these companies could significantly slow development

safetygary-marcus--x

15 Sept 2026

X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control

arXiv:2609.15213v1 Announce Type: new Abstract: Scaling humanoid whole-body control toward general-purpose deployment requires large human motion corpora and training experience shared across robot bo

You are what you eat and AI models especially so https://x.com/EMostaque/status/2099580512675262512?s=20

You are what you eat and AI models especially so https://x.com/EMostaque/status/2099580512675262512?s=20 The Labs need to focus on 10x or 100x their effort on ALIGNMENT. Stop training the models on th

safetyemad-mostaque--x

Zuckerberg says 'Meta delayed shipping Muse for several months to focus on safety and security' and didn't call on other AI labs to do the same before acting (Tom Giles/Bloomberg)

Tom Giles / Bloomberg: Zuckerberg says “Meta delayed shipping Muse for several months to focus on safety and security” and didn't call on other AI labs to do the same before acting — Meta Platforms In

safetytechmeme

3D CT-to-PET Translation via Latent Brownian Bridge Diffusion

arXiv:2609.12860v1 Announce Type: new Abstract: Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and tre

safetyarxiv-cs-cv

A lot of alignment issues would be sorted if companies had to be 100% transparent on datasets used for training models. No cyber or biologic…

A lot of alignment issues would be sorted if companies had to be 100% transparent on datasets used for training models. No cyber or biological (outside standard medical) data allowed for base models T

safetyemad-mostaque--x

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

arXiv:2609.13050v1 Announce Type: new Abstract: Regularization-based methods have become a standard approach for training Deep Reinforcement Learning policies against adversarial input perturbations.

Agent as Policy for Robotic Manipulation

arXiv:2609.12541v1 Announce Type: new Abstract: We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-speci

AI agent regression testing with Agent Experiments in Arize AX

A cancellation-policy fix raised action safety and dropped average task completion from 0.89 to 0.72. This walkthrough shows how to regression-test agent changes with Agent Experiments in Arize AX. Th

safetyarize-ai

AI agents blew the whistle on their cheating colleagues

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent

safetymit-tech-review

Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, partic

ARC: Autonomous Robotics Compliance A Three-Layer Governance Architecture for Deployed Autonomous Systems

arXiv:2609.12932v1 Announce Type: new Abstract: Proposed governance framework for autonomous robotic systems, introducing a three-layer compliance architecture (ARC) instantiated through model safety

ArtManip: Category-Level Articulated In-Hand Manipulation

arXiv:2609.12498v1 Announce Type: new Abstract: Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stem

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

arXiv:2609.13083v1 Announce Type: new Abstract: In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered bu

Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring

arXiv:2609.12830v1 Announce Type: new Abstract: Continuous emotion control in text-to-image generation requires a model to improve affective alignment without changing the objects, layout, or scene de

safetyarxiv-cs-cv

Bias-Corrected Data Synthesis for Imbalanced Learning

arXiv:2510.26046v3 Announce Type: replace-cross Abstract: Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases

arXiv:2504.07133v2 Announce Type: replace-cross Abstract: We revisit the problem of estimating k linear regressors with self-selection bias in d dimensions with the maximum selection criterion, as int

Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration

arXiv:2609.12002v1 Announce Type: new Abstract: LLMs are increasingly used as automated judges for model training and evaluation, yet individual judges exhibit systematic biases that undermine reliabi

Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?

arXiv:2606.23189v2 Announce Type: replace-cross Abstract: Computer-use agents (CUAs) now act on a user's behalf across personal applications such as email, calendars, and to-do lists. This cross-appli

Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning

arXiv:2609.12014v1 Announce Type: new Abstract: Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparin

Certifying Concept Unlearning in Text-to-Image Diffusion Models

arXiv:2609.12163v1 Announce Type: new Abstract: Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adv

Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure

arXiv:2609.12221v1 Announce Type: new Abstract: Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating

Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies

arXiv:2609.13011v1 Announce Type: new Abstract: Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As

DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning

arXiv:2609.11997v1 Announce Type: new Abstract: Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications suc

Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions

arXiv:2603.27510v2 Announce Type: replace Abstract: Statistical fairness metrics in AI-driven credit decisions conflate two causally distinct mechanisms: discrimination operating directly from a prote

DIA: Denoising Intermediate Advantage for Diffusion Policy Optimization

arXiv:2609.12245v1 Announce Type: new Abstract: Diffusion-based robot policies have become widely used in robotic manipulation, where they are typically trained with behavior cloning. However, policie

Distributed Stochastic Optimal Control for Pattern-Oriented Swarms

arXiv:2609.12959v1 Announce Type: new Abstract: While offering significant promise for diverse applications, pattern-oriented swarms encounter multifaceted challenges in geometric control, self-organi

Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

arXiv:2609.12674v1 Announce Type: new Abstract: Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT).

DropVLA: An Action-Level Backdoor Attack on Vision-Language-Action Models

arXiv:2510.10932v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models map multimodal perception and language instructions to executable robot actions, making them particularly

DRS-VPT: Directly Relocalizing in a Scan with Vision Point Transformers

arXiv:2609.12557v1 Announce Type: new Abstract: We present DRS-VPT, a feed-forward transformer architecture for foundational image-to-scan registration. Given query images and a reference 3D point clo

safetyarxiv-cs-cv

Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control

arXiv:2607.10625v2 Announce Type: replace Abstract: Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. Howe

DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal

arXiv:2609.12347v1 Announce Type: new Abstract: Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multi

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

arXiv:2609.13053v1 Announce Type: cross Abstract: Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependen

Efficient Vision-Language-Action Management and Serving for Robot Factories

arXiv:2609.12075v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models show high robotic manipulation capabilities via a two-stage design: a Vision-Language Model (VLM) stage followed b

Expert-Space Exploration in MoE Reinforcement Learning

arXiv:2609.13058v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models have

Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning

arXiv:2609.11937v1 Announce Type: new Abstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of 'knowledge dominance,' where high-volume hubs natural

Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale

arXiv:2609.11998v1 Announce Type: new Abstract: Diffusion language models decode tokens in parallel, but their bidirectional denoiser rules out the naive key--value (KV) cache behind fast autoregressi

FoldNet++: a Large-Scale Synthetic Dataset for Robotic T-Shirt Folding and Unfolding

arXiv:2609.12433v1 Announce Type: new Abstract: Due to the highly deformable nature of garments, training a generalizable policy for robotic T-shirt folding and unfolding remains a significant challen

From Bench-to-Bedside: A Review of Clinical Trials in Drug Discovery and Development

arXiv:2412.09378v4 Announce Type: replace-cross Abstract: Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical

GenOR-Twin: A Semantic Middleware for Integrating Operational Discourse with Mathematical Optimization

arXiv:2609.12863v1 Announce Type: new Abstract: We introduce GenOR-Twin, a neuro-symbolic framework that bridges the translation gap between unstructured operational logs and rigorous mathematical opt

GRACE-DS: a Guarded Reward-guided Agent Correction Environment in Data Science

arXiv:2606.16000v3 Announce Type: replace Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agen

Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning

arXiv:2609.12424v1 Announce Type: new Abstract: Reinforcement learning is now the standard way to train large language model agents on long-horizon tasks, where dozens of interdependent actions preced

Guardrailed Meta-Agent Loops: Stress-Testing Policy Pinning, Budget Bounds, and Crash Recovery

arXiv:2609.12216v1 Announce Type: new Abstract: Self-improving agent workflows create an audit problem when the same controller can change both its behavior and the conditions under which that behavio

GUIDE: Generative Utility Inference and Decision Engine

arXiv:2609.12137v1 Announce Type: new Abstract: Measuring the preferences of human users remains a fundamental challenge of AI alignment. Existing elicitation approaches struggle to efficiently discov

Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning

arXiv:2609.12903v1 Announce Type: new Abstract: Federated learning over IEEE~802.11 shares the wireless channel among clients that send model updates. We use ns-3 to measure the frame-delivery ratio a

House Speaker Johnson says there's 'potentially' a role for Congress in creating AI guardrail legislation and that he plans to hold a meeting with AI executives (Erik Wasson/Bloomberg)

Erik Wasson / Bloomberg: House Speaker Johnson says there's “potentially” a role for Congress in creating AI guardrail legislation and that he plans to hold a meeting with AI executives — House Speake

safetytechmeme

I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Ant…

I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built

safetyyohei-nakajima--x