Search: “safety”

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation

arXiv:2606.26423v1 Announce Type: cross Abstract: Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-bo

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Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

DGX agent

arXiv:2606.27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance

safetyarxiv-cs-lg

26 Jun 2026

CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

arXiv:2510.20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerica

Data-Free Reservoir Features for Efficient Long-Horizon Cold-Start Continual Learning

arXiv:2606.27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initia

Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis

arXiv:2409.01447v3 Announce Type: replace Abstract: We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-respons

Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation

arXiv:2606.26743v1 Announce Type: new Abstract: Point clouds are an important carrier of three-dimensional spatial information, and their quality directly affects the performance of downstream percept

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search

arXiv:2606.27291v1 Announce Type: new Abstract: Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present

Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning

arXiv:2606.26397v1 Announce Type: cross Abstract: Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently

Diagnosing Task Insensitivity in Language Agents

arXiv:2606.26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key sourc

DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents

arXiv:2606.26122v1 Announce Type: new Abstract: Recent methods train search agents via reinforcement learning from (question, answer, evidence) tuples without requiring expert trajectories. The tuples

Don't Settle at the Mode! Mitigating Diversity Collapse in Pretrained Flow Models via Feature Self-Guidance

arXiv:2606.27371v1 Announce Type: new Abstract: State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple

Dream machine -- the next creative economy

arXiv:2606.26114v1 Announce Type: cross Abstract: We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning poli

E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

arXiv:2606.27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reaso

Effective Covariance Dynamics in Solvable High-Dimensional GANs

arXiv:2606.27246v1 Announce Type: new Abstract: We study a solvable high-dimensional model of generative adversarial network (GAN) training in which a linear generator learns a low-dimensional subspac

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606.26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed. Automating this design is challenging because each cand

Fantastic interview: Ed Elson is talking with Gary Marcus about AI tech, overrelience on LLMs, the business of AI, etc. I totally enjoyed th…

Fantastic interview: Ed Elson is talking with Gary Marcus about AI tech, overrelience on LLMs, the business of AI, etc. I totally enjoyed this: https://youtu.be/_pSivPlRx5o?si=dzrJKpUGTHE0Y76I @edels0

Finding the Time to Think: Learning Planning Budgets in Real-Time RL

arXiv:2606.26463v1 Announce Type: new Abstract: Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits inde

Forecasting With LLMs: Improved Generalization Through Feature Steering

arXiv:2606.27199v1 Announce Type: new Abstract: Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

arXiv:2606.26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnos

From query to action: Introducing SQL alerting in Cloud Monitoring Observability Analytics

Traditional alerting systems often force a compromise: you can either alert immediately on simple, noisy log events, or monitor rigid, pre-configured metrics that fail when faced with data with many u

safetygoogle-cloud-ai

from tokenmaxxing to this in less than three months. anyone who doesn’t see this as affecting Anthropic and OpenAI is not paying attention.

from tokenmaxxing to this in less than three months. anyone who doesn’t see this as affecting Anthropic and OpenAI is not paying attention. UBS says 60% of companies now watching AI budgets are moving

“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”.…

“Gary Marcus … says that investments into scaling AI is the “greatest capital misallocation in history” which leaves everyone “on the hook”. However, he also says that it is still early days for the A

@GaryMarcus, great interview and explanations of the challenges in AI. https://youtu.be/_pSivPlRx5o?is=dlFN6YBXsMA-tW4h

Gary Marcus discusses fundamental challenges and limitations in current AI systems during an interview, likely covering topics such as the gap between narrow and general intelligence, reasoning limita

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606.26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items. In m

GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning

arXiv:2606.26917v1 Announce Type: cross Abstract: Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or mi

Geometric Fairness-Aware Routing for Federated Edge Networks

arXiv:2606.26125v1 Announce Type: cross Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing f

Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems

arXiv:2606.26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This pape

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606.27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing

arXiv:2606.12816v3 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors. Routes that appear efficient by s

Hardware Design for Table Tennis Robot Capable of Beating Professional Players

arXiv:2606.26643v1 Announce Type: new Abstract: This paper focuses on the hardware specifications required for a table tennis robot to beat professional players. After analyzing the motions of elite p

HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification

arXiv:2606.26100v1 Announce Type: new Abstract: Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each s

High-Probability PL-SGD with Markovian Noise: Optimal Mixing and Tail Dependence

arXiv:2606.26316v1 Announce Type: new Abstract: We study first-order methods for smooth objectives satisfying the Polyak-L{}ojasiewicz (PL) condition when gradient samples are generated by an exogenou

How US federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque, and how to fix it, including using independent auditors (Dean W. Ball/Hyperdimensional)

Dean W. Ball / Hyperdimensional: How US federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque, and how to fix it, including using independent auditors — 35 thoug

safetytechmeme

Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking

arXiv:2606.26855v1 Announce Type: new Abstract: Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches re

HumanoidUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation

arXiv:2606.27239v1 Announce Type: new Abstract: High-quality demonstration data are essential for humanoid robot skill learning, especially for whole-body behaviors that require coordinated perception

Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation

arXiv:2606.26502v1 Announce Type: new Abstract: Large reasoning models (LRMs) take longer on harder problems, just as humans do. This surface similarity hides an opposite pattern within items. When an

I wrote about how accumulating capital won't save you from being disempowered by superintelligent AI.

Connor Leahy argues that accumulating personal capital provides no protection against disempowerment by superintelligent AI systems, suggesting that wealth alone cannot guarantee security or agency in

safetyconnor-leahy--x

IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control

arXiv:2606.26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based

Improved Bounds for Private and Robust Alignment

arXiv:2512.23816v2 Announce Type: replace-cross Abstract: In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the

Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization

arXiv:2606.27025v1 Announce Type: new Abstract: Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant par

Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision

arXiv:2606.26801v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervisio

In a matter of weeks, U.S. federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque. Today, over 35 dist…

In a matter of weeks, U.S. federal AI policy has gone from implausibly libertarian to increasingly draconian and opaque. Today, over 35 distinct observations, I analyze how we got here and offer the m

In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics

arXiv:2606.26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off

indeed literally a trillion dollar argument

Gary Marcus discusses arguments surrounding trillion-dollar implications, likely related to AI development, regulation, or economic impacts given his expertise in artificial intelligence and cognitive

Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure

arXiv:2606.26588v1 Announce Type: new Abstract: A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferenc

June has been a dark month for the US generative AI industry, between hamfisted US policy and the transition to cheaper Chinese AI models. A…

June has been a dark month for the US generative AI industry, between hamfisted US policy and the transition to cheaper Chinese AI models. And then there is the delay of the OpenAI IPO. Is that the co

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

arXiv:2606.27023v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual

KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction

arXiv:2606.26179v1 Announce Type: cross Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pat