Search: “safety”

TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments

arXiv:2606.24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision. In contrast, robotic systems rely heavily on vision and struggle

Safety

“The Al bubble is by far the biggest stock-market bubble that mankind has ever seen - both in scale and scope, in terms of the leverage that…

DGX agent

“The Al bubble is by far the biggest stock-market bubble that mankind has ever seen - both in scale and scope, in terms of the leverage that's in there and in terms of the sheer excessive exuberance t

safetygary-marcus--x

24 Jun 2026

The problem we face with AI today is not a technical problem, it is a political problem. Of who gets to decide. What level of risk the publi…

The problem we face with AI today is not a technical problem, it is a political problem. Of who gets to decide. What level of risk the public is exposed to, what future we build towards or avert. It's

safetyconnor-leahy--x

The world of computing is and will remain far larger, more impactful, and far more vibrant than the narrow subdomain of genAI.

Gary Marcus argues that generative AI, while significant, represents only a narrow subset of the broader computing field and should not overshadow the diverse and impactful work occurring across other

Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback

arXiv:2606.24622v1 Announce Type: new Abstract: Training safe Reinforcement Learning (RL) systems is inherently challenging, with no guarantee of avoiding unwanted behaviors. The most effective defens

Today I present you a curated collection of @tszzl saying that Fukuyama is always right

Today I present you a curated collection of @tszzl saying that Fukuyama is always right We Need an International Treaty to Ban Superintelligence https://open.substack.com/pub/persuasion1/p/time-for-a-

safetyconnor-leahy--x

Token-to-Token Alignment of Text Embeddings for Semantic Blending

arXiv:2606.24021v1 Announce Type: new Abstract: In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derive

TruncProof: A Guardrail for LLM-based JSON Generation under Token-Length Constraints

arXiv:2605.13076v2 Announce Type: replace Abstract: The LLM-based generation of machine-readable outputs such as JSON has attracted significant attention for integration with external systems. However

safetyarxiv-cs-cl

UniTranslator: A Unified Multi-modal Framework for End-to-end In-Image Machine Translation

arXiv:2606.24333v1 Announce Type: new Abstract: In-Image Machine Translation (IIMT) aims to translate scene text in an image and render the translated text back into the original regions while preserv

UOL@IDEM at BEA 2026 Shared Task 1: Neural Fusion and Feature-Rich Modeling for L1-Aware Vocabulary Difficulty Prediction

arXiv:2606.24501v1 Announce Type: new Abstract: This paper describes UOL@IDEM's closed-track submission to the BEA 2026 shared task on L1-aware vocabulary difficulty prediction. We model the task as r

safetyarxiv-cs-cl

Welcome to the GenerativeAI Fizzle™

This post likely references Gary Marcus's critical commentary on limitations or failures in generative AI systems, using 'Fizzle' as a metaphorical or literal descriptor for underperformance or unmet

What Do Flow-Based Inverse Solvers Approximate? A Posterior-Transport View

arXiv:2606.24516v1 Announce Type: new Abstract: A growing family of training-free solvers -- FlowDPS, FLOWER, PnP-Flow and their diffusion ancestors (DPS, DAPS) -- repurpose a pretrained flow-matching

When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

arXiv:2407.18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroe

When CQs Go Wrong: Challenges in CQ Verification with OE-Assist

arXiv:2606.24619v1 Announce Type: new Abstract: Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natur

Wow even @MicahLasher thinks the OpenAI-Andreessen-Palantir money spent to oppose @AlexBores backfired --

Wow even @MicahLasher thinks the OpenAI-Andreessen-Palantir money spent to oppose @AlexBores backfired -- NY-12 retrospective in the NYT from Teddy Schleifer. Hard to imagine this was the headline tha

360Anything: Geometry-Free Lifting of Images and Videos to 360{eg}

arXiv:2601.16192v2 Announce Type: replace Abstract: Lifting perspective images and videos to 360{eg} panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometr

A Controlled Study of CLIP-Based Body-Scene Fusion for Emotion Recognition in Context

arXiv:2606.22072v1 Announce Type: new Abstract: Apparent emotion in natural images is often not visible from the face alone. The face may be small, hidden, or neutral, while posture and scene context

A Markov Chain Approach to Preference Alignment

arXiv:2606.22652v1 Announce Type: new Abstract: We propose Markov Chain from Human Feedback (MCHF), an elementary approach for aligning generative models from pairwise human preferences. Unlike Reinfo

A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems

arXiv:2606.20031v2 Announce Type: replace Abstract: Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS

A Stitch in Time Saves Nine: Preserving Policy Compatibility Under Perception Updates in End-to-End Autonomous Driving

arXiv:2606.21509v1 Announce Type: new Abstract: End-to-end autonomous driving systems tightly couple perception and decision-making through latent representations. Consequently, updates to perception

A Taxonomy of Conceptual Alignment in Human-Robot Dialogue

arXiv:2606.22360v1 Announce Type: new Abstract: Successful conversations require speakers to align on the meaning of concepts, a challenging but crucial task for human-robot interaction. Understanding

A Watermark for Vision-Language-Action and World Action Models

arXiv:2606.23574v1 Announce Type: cross Abstract: Vision-language-action (VLA) models and world-action models (WAM) are the generative models now driving general-purpose robot control, turning raw cam

Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives

arXiv:2606.23662v1 Announce Type: cross Abstract: Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfa

Active Causal Experimentalist (ACE): Learning Intervention Strategies via Direct Preference Optimization

arXiv:2602.02451v2 Announce Type: replace Abstract: Discovering causal relationships requires controlled experiments, but experimentalists face a sequential decision problem: each intervention reveals

Again: data never stays secure and mass surveillance and data harvesting will always be a liability

Gary Marcus argues that data security is fundamentally fragile and that mass surveillance and data harvesting practices create persistent security vulnerabilities that cannot be eliminated. The post e

Agentic Time Machine as an Infrastructure for Future-Event Forecasting

arXiv:2606.21013v1 Announce Type: cross Abstract: Forecasting future events is a critical challenge for large language model (LLM) agents, spanning domains from elections and monetary policy to financ

AI Capex pushback from GS: 'If frontier intelligence can increasingly be developed in the East at a fraction of the cost incurred in the Wes…

AI Capex pushback from GS: 'If frontier intelligence can increasingly be developed in the East at a fraction of the cost incurred in the West … then the largest capital allocators are also the ones mo

AI data centres are hungry for land, water & power. I’m calling on every major AI company to publicly disclose the full environmental impact…

AI data centres are hungry for land, water & power. I’m calling on every major AI company to publicly disclose the full environmental impact of its systems – as a matter of transparency No more hidden

ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training

arXiv:2602.12691v3 Announce Type: replace Abstract: We study how to improve large foundation vision-language-action (VLA) systems through human-in-the-loop reinforcement learning (RL) in real-world en

Although I supported Musk in his suit against OpenAI, and admire what he did for electric cars, I am afraid my considered overall view is no…

Although I supported Musk in his suit against OpenAI, and admire what he did for electric cars, I am afraid my considered overall view is not very different from this: Candidly I have no idea why anyo

APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots

arXiv:2505.10022v4 Announce Type: replace Abstract: Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. While motion tracking can reproduce refe

ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking

arXiv:2606.20687v1 Announce Type: new Abstract: Multi-Camera Multi-Target (MCMT) tracking has emerged as a critical capability for applications ranging from autonomous driving to animal behavior monit

ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation

arXiv:2606.22480v1 Announce Type: new Abstract: Learning visuomotor policies for long-horizon manipulation remains a fundamental challenge. Recent skill-based imitation learning methods based on discr

Backpropagating Through Simulation: Analytic Policy Gradients for Sample and Learning Efficient Differentiable Continuous Control

arXiv:2606.21525v1 Announce Type: new Abstract: Model-free reinforcement learning algorithms such as Proximal Policy Optimization (PPO) treat the environment as a black box, estimating policy gradient

Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training

arXiv:2606.21498v1 Announce Type: cross Abstract: Autoregressive text-to-image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging.

BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning

arXiv:2606.20701v1 Announce Type: cross Abstract: Learned communication improves coordination in cooperative multi-agent reinforcement learning, but it also creates a trust problem: a trained policy m

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models

arXiv:2606.21645v1 Announce Type: cross Abstract: Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains under

B[FM]^2: Brain Foundation Model via Flow Matching with SplitUNet

arXiv:2606.20812v1 Announce Type: new Abstract: EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical an

BLENDS: Bayesian Learning-Enhanced Deep Smoothing for GNSS-Denied Environments

arXiv:2606.22456v1 Announce Type: new Abstract: Maintaining accurate navigation during GNSS outages remains a significant challenge for autonomous systems relying on low-cost inertial sensors. While c

Boosting CVaR Policy Optimization with Quantile Gradients

arXiv:2601.22100v3 Announce Type: replace Abstract: Optimizing Conditional Value-at-risk (CVaR) using policy gradient (a.k.a CVaR-PG) faces significant challenges of sample inefficiency. This ineffici

Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning

arXiv:2606.23199v1 Announce Type: new Abstract: Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogen

Bypassing Minimization Bias: A Shift-Invariant Variance Estimator for Off-Equilibrium Local Learning Coefficients

arXiv:2606.22389v1 Announce Type: new Abstract: Singular Learning Theory leverages the Local Learning Coefficient (LLC) to quantify the geometry of neural network loss landscapes. However, mean-energy

Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation

arXiv:2606.21981v1 Announce Type: cross Abstract: While Large Language Models (LLMs) can generate fluent Arabic text, their ability to reliably control readability levels remains unclear. We propose a

Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding

arXiv:2606.21809v1 Announce Type: new Abstract: The presence of confounding bias poses a key challenge in policy evaluation, as the target causal effects of actions are not identifiable (i.e., underde

Causally Fair Node Classification on Non-IID Graph Data

arXiv:2505.01652v2 Announce Type: replace Abstract: Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes,

CFPO: Counterfactual Policy Optimization for Multimodal Reasoning

arXiv:2606.23206v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal reasoning. However, prevailing reinforcement learning (RL)

Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL

arXiv:2602.03389v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning remains challenging for long-horizon tasks. While hierarchical approaches mitigate this issue by dec

CLAR: Learning 3D Representations for Robotic Manipulation by Fusing Masked Reconstruction with Multi-Level Contrastive Alignment

arXiv:2507.08262v2 Announce Type: replace-cross Abstract: The spatial information inherent in 3D point clouds is crucial for robotic manipulation. However, existing 3D pre-training methods face a fund