safety

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

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

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

DGX agent

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

safetyarxiv-cs-ai

9 Jun 2026

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

safetygary-marcus--x

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

safetygary-marcus--x

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

Position: Anthropomorphic Misalignment Research Needs Stronger Evidence

arXiv:2606.07612v1 Announce Type: cross Abstract: We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for

PriFT: Prior-Support Guided Supervised Fine-Tuning

arXiv:2606.09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement le

Prisma-World: Camera-Controllable Multi-Agent Video World Model

arXiv:2606.09507v1 Announce Type: new Abstract: Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single obser

PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models

arXiv:2606.08926v1 Announce Type: new Abstract: Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring

Property-Informed Diffusion-Based Text-to-Microstructure Generation

arXiv:2606.08150v1 Announce Type: new Abstract: Designing 3D metamaterial microstructures that meet the intended functions remains a major challenge, as it typically requires domain expertise, iterati

Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization

arXiv:2606.09711v1 Announce Type: new Abstract: Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what

PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping

arXiv:2606.08708v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language M

PTDL:Multi-Terrain Fall Recovery via Phase-Terrain Decoupled Learning

arXiv:2606.08922v1 Announce Type: new Abstract: Humanoid robots can fall on slopes, gravel, and uneven ground in unstructured environments. We target integrated fall recovery and locomotion: rebuildin

Q-VGM: Q-Guided Value-Gradient Matching for Flow-Matching VLA Policies

arXiv:2606.08015v1 Announce Type: new Abstract: We propose Q-Guided Value-Gradient Matching (Q-VGM), an off-policy reinforcement learning (RL) method that tackles a long-standing challenge in fine-tun

QDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition

arXiv:2606.07657v1 Announce Type: cross Abstract: Traffic sign recognition is crucial for intelligent transportation and autonomous driving, as it can improve driving efficiency and ensure road safety

QnRL: Quantum-Native Reinforcement Learning

arXiv:2606.08276v1 Announce Type: cross Abstract: Quantum reinforcement learning (QRL) is a promising approach to learn effective decision strategies across several applications with stochastic enviro

Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations

arXiv:2606.07580v1 Announce Type: cross Abstract: As Low Earth Orbit has grown more crowded with space debris, the need for reliable and efficient debris removal solutions becomes more urgent. An acti

Quantitative Promise Theory: Intentionality and Inference in Autonomous Agents

arXiv:2606.08552v1 Announce Type: new Abstract: I discuss some quantitative representations of Promise Theory for processes involving autonomous agents. Agent models are common in software systems, ma

ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies

arXiv:2606.09630v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring

ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration

arXiv:2606.09381v1 Announce Type: new Abstract: Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the

Region-Wise Correspondence Prediction between Manga Line Art Images

arXiv:2509.09501v4 Announce Type: replace Abstract: Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606.08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems. Our key insight is t

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations

arXiv:2606.08104v1 Announce Type: new Abstract: Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stif