safety

Light-WAM: Efficient World Action Models with State-Fusion Action Decoding

arXiv:2606.08242v1 Announce Type: new Abstract: World Action Models (WAMs) extend robot policy learning by incorporating future prediction as an additional training objective, encouraging the policy t

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LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution

DGX agent

arXiv:2606.09250v1 Announce Type: new Abstract: Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that re

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9 Jun 2026

LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection

arXiv:2606.08153v1 Announce Type: cross Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure. We present LogNEO, a l

LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving

arXiv:2606.08470v1 Announce Type: new Abstract: While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited

Major improvement in safety statistics with FSD turned on in the Netherlands!

Elon Musk posted on X claiming that Tesla's Full Self-Driving (FSD) system resulted in significant improvements to vehicle safety statistics in the Netherlands. The post suggests FSD activation correl

safetyelon-musk--x

MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training

arXiv:2606.08788v1 Announce Type: new Abstract: Representation alignment with pretrained vision models has recently shown strong potential for accelerating diffusion transformer training. By aligning

MC-CPO: Mastery-Conditioned Constrained Policy Optimization for Pedagogically Safe Intelligent Tutoring Systems

arXiv:2604.04251v2 Announce Type: replace Abstract: Intelligent tutoring systems increasingly rely on reinforcement learning to personalise instruction, yet optimising for observable engagement signal

MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models

arXiv:2606.07996v1 Announce Type: cross Abstract: Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and rais

me January 2025 @politico, timing slightly off: we may soon see “the largest cyberattack in history, taking down, at least for a little whil…

me January 2025 @politico, timing slightly off: we may soon see “the largest cyberattack in history, taking down, at least for a little while, some sizeable piece of the world’s infrastructure… genera

MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation

arXiv:2606.08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-dise

Memetic Capture: A Pluralistic Policy Framework for Governing AI-Driven Cultural Disempowerment

arXiv:2606.07802v1 Announce Type: cross Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks t

Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking

arXiv:2606.08253v1 Announce Type: cross Abstract: Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate ro

MIRAGE: Metadata-Integrated Repository Analysis and Guided Enhancement for MSR Datasets

arXiv:2606.07611v1 Announce Type: cross Abstract: This paper proposes an improved approach to the analysis of Mining Software Repositories (MSR) datasets via metadata enrichment, FAIRness assessment,

mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models

arXiv:2606.07531v1 Announce Type: cross Abstract: We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to M

Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization

arXiv:2606.08815v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. Ho

Monday’s gains are Tuesday’s losses?

This post likely discusses the volatility and unpredictability of financial markets, examining whether gains achieved on one trading day are frequently reversed or lost the following day. Gary Marcus

MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation

arXiv:2606.09215v1 Announce Type: new Abstract: World Action Models (WAMs) couple a video dynamics prior to the policy and have shown encouraging results on tabletop manipulation, but iterative denois

Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers

arXiv:2601.12263v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) integrate visual and textual knowledge into unified representations that increasingly underpin modern retrieval

Muses: Designing, Composing, Generating Nonexistent Fantasy 3D Creatures without Training

arXiv:2601.03256v2 Announce Type: replace Abstract: We present Muses, the first training-free method for fantastic 3D creature generation in a feed-forward paradigm. Previous methods, which rely on pa

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies

arXiv:2606.08312v1 Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite trac

NeuroAlign: Hierarchical Multimodal Fusion of Dynamic and Structural Neuroimaging for MCI Analysis

arXiv:2606.07635v1 Announce Type: cross Abstract: Multimodal neuroimaging fusion of functional MRI (fMRI) and diffusion tensor imaging (DTI) provides complementary information for cognitive impairment

Neutrality Bites: Gender Representation in AI-Generated Animal Stories

arXiv:2606.07969v1 Announce Type: cross Abstract: Gender bias in AI-generated stories is a well-documented problem. While much attention has been paid to reducing or mitigating this bias, it is not al

no

no Hmm, @GaryMarcus , do you think they’d be able to achieve *any* of these goals? 😁 “Currently at OpenAI we have three main goals… 1. Build an automated AI researcher. 2. Accelerate the economy. 3. G

No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation

arXiv:2509.15017v2 Announce Type: replace Abstract: Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its abi

Nope. I think the positive press for @pontifex is because the Pope cares about humanity, and the consequence of technology for humanity. And…

Nope. I think the positive press for @pontifex is because the Pope cares about humanity, and the consequence of technology for humanity. And that the tech bros mostly don’t. He’s also sharper on philo

NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning

arXiv:2602.21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures. However,

Normality Calibration in Semi-supervised Graph Anomaly Detection

arXiv:2510.02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-super

OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation

arXiv:2606.08548v1 Announce Type: new Abstract: Recent progress in robot manipulation has been largely driven by learning from large-scale demonstrations. For humanoid robot loco-manipulation tasks, h

omega-EVA: Envision, Verify, and Act with Latent Interactive World Models

arXiv:2606.09457v1 Announce Type: new Abstract: Embodied policies typically map current observations directly to actions, leaving candidate-action consequences implicit. World models provide predictiv

On-the-fly hand-eye calibration for the da Vinci surgical robot

arXiv:2601.14871v2 Announce Type: replace Abstract: In Robot-Assisted Minimally Invasive Surgery (RMIS), accurate tool localization is crucial to ensure patient safety and successful task execution. H

Operationalising the Superficial Alignment Hypothesis via Task Complexity

arXiv:2602.15829v2 Announce Type: replace Abstract: The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-traini

Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints

arXiv:2601.23221v2 Announce Type: replace Abstract: As acquiring reliable ground-truth labels is usually costly, or infeasible, crowdsourcing and aggregation of noisy human annotations is the typical

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework

arXiv:2606.08574v1 Announce Type: cross Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined p

Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

arXiv:2606.07583v1 Announce Type: cross Abstract: Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several action

Overcoming the Regulatory Bottleneck via Agent-to-Agent Protocols: A Nuclear Case Study

arXiv:2606.07866v1 Announce Type: new Abstract: Regulatory review of advanced nuclear reactor designs routinely spans more than three years and consumes hundreds of millions of dollars in combined reg

Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human

arXiv:2606.08919v1 Announce Type: new Abstract: As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approva

PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation

arXiv:2606.08414v1 Announce Type: cross Abstract: Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for s

PAEC: Position-Aware Entropy Calibration for LLM Reasoning in RLVR

arXiv:2606.08543v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning but often suffers from rapid policy-entropy collapse, wher

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling

arXiv:2606.07988v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models a

PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization

arXiv:2605.06582v2 Announce Type: replace Abstract: Many operations on sensory data -- comparison, memory, retrieval, and reasoning -- are naturally expressed over discrete symbolic structures. In lan

PairWise Image Finder: An Open-source Tool for Finding Visually Aligned Street-Level Image Pairs for Urban Perception Studies

arXiv:2606.08795v1 Announce Type: new Abstract: Change detection and scene recognition techniques have been widely applied to Street View Imagery (SVI) to understand changes in scenes across the years

Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach

arXiv:2606.07855v1 Announce Type: new Abstract: Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge because the problem is nonlinear and nonconvex even in sim

PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow

arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise for computational pathology, yet reliable patc

Payoff scaling shapes cooperation in LLM agents across languages

arXiv:2601.19082v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users. Whether they coo

PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment

arXiv:2606.09348v1 Announce Type: new Abstract: Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final

Petri Net Modeling and Deadlock-Free Scheduling of Attachable Heterogeneous AGV Systems

arXiv:2508.00724v2 Announce Type: replace-cross Abstract: The increasing demand for flexible automation has accelerated the adoption of heterogeneous automated guided vehicles (AGVs). This work invest

Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks

arXiv:2606.08473v1 Announce Type: new Abstract: False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when

Polaffini: A feature-based approach for robust affine and polyaffine image registration

arXiv:2602.17337v2 Announce Type: replace Abstract: In this work we present Polaffini, a robust and versatile framework for anatomically grounded registration. Medical image registration is dominated