Search: “safety”

GeoISF: Instance Semantic Forest Inspired Large-Scale Cross-View Geo-Localization via Ground LiDAR-to-Satellite Image

arXiv:2606.28371v1 Announce Type: new Abstract: The problem of localization on a large-scale satellite image given a frame of query ground view point clouds remains challenging. Existing LiDAR-to-imag

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Golden Hour Divide: Trauma Care Accessibility and Resource Vulnerability in Sri Lanka

DGX agent

arXiv:2606.29889v1 Announce Type: new Abstract: Timely intensive care dictates survival, yet emergency infrastructure remains unevenly distributed across Sri Lanka. While pre-hospital services have ex

safetyarxiv-cs-lg

30 Jun 2026

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

arXiv:2606.29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applicat

Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field

arXiv:2606.30474v1 Announce Type: new Abstract: Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target obje

HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data

arXiv:2606.29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these 'gold' labels are expensive to collect

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

arXiv:2606.29126v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat

Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization

arXiv:2606.28764v1 Announce Type: new Abstract: Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageabl

Hierarchical Policy Learning via Spectral Decomposition

arXiv:2606.29570v1 Announce Type: new Abstract: In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By

High-Dimensional Concentration and Retrieval Instability in Embedding Spaces: Implications for Retrieval-Augmented Generation

arXiv:2606.28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance

How Anthropomorphic Language Impacts Public Perceptions of AI

arXiv:2606.29121v1 Announce Type: cross Abstract: Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristi

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position

arXiv:2606.15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models

Hyper-Network Neural Functional Maps for Unsupervised Robust 3D Shape Matching

arXiv:2606.30131v1 Announce Type: cross Abstract: Functional maps are the cornerstone of recent non-rigid 3D shape matching methods due to their efficiency and performance. However, existing methods s

In a new interview with the Washington Post's @ben_guggenheim, ControlAI's US Director Connor Leahy explains why nobody really understands h…

In a new interview with the Washington Post's @ben_guggenheim, ControlAI's US Director Connor Leahy explains why nobody really understands how AI works, the race to uncontrollable superintelligence, a

safetyconnor-leahy--x

Inference-time optimization for experiment-grounded protein ensemble generation

arXiv:2602.24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles tha

InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion

arXiv:2512.17504v2 Announce Type: replace-cross Abstract: Recent advances in diffusion models have enabled impressive video editing capabilities, yet production-grade Video Object Insertion (VOI) rema

Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization

arXiv:2606.29386v1 Announce Type: new Abstract: Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series ext

Invariant Reasoning Directions in Latent Trajectories of Language Models

arXiv:2606.29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains

Is Muon as good as they say? We looked beyond training speed and found a hidden cost: Muon loses the simplicity bias of older optimizers lik…

Muon optimizer shows faster training speeds compared to traditional optimizers, but analysis reveals it sacrifices the simplicity bias that older optimizers maintain, potentially impacting model gener

safetyjeremy-howard--x

ITSPACE: Monotone Gaussian Optimal Transport Updates

arXiv:2606.30523v1 Announce Type: new Abstract: Covariance matrices serve as compact descriptors of feature distributions in many machine-learning pipelines, including domain adaptation and Gaussian e

Keypose Exploration: Efficient Automatic Trajectory Labelling and Cross-Embodiment Policy Transfer

arXiv:2606.29028v1 Announce Type: new Abstract: Keypose-based manipulation decomposes tasks into critical waypoints to simplify policy learning for long-horizon tasks, but existing approaches rely on

Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement

arXiv:2601.21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing metho

KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability

arXiv:2606.30243v1 Announce Type: new Abstract: This paper presents KYON, a hybrid wheel-legged quadruped robot equipped with a bimanual upper body for loco-manipulation tasks. The platform features a

L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction

arXiv:2606.29374v1 Announce Type: new Abstract: High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Ga

Latent Actions from Factorized Transition Effects under Agent Ambiguity

arXiv:2606.30544v1 Announce Type: new Abstract: Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual eff

LatentRevise: Learning from Zero-Hit Reasoning

arXiv:2606.29938v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by hard prompts on which correct trajectories have low probability, so sampling mi

Leader Reward for POMO-Based Neural Combinatorial Optimization

arXiv:2405.13947v2 Announce Type: replace Abstract: Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown

Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing

arXiv:2606.29541v1 Announce Type: new Abstract: Role-semantic assignments provide priors over how heterogeneous agents may coordinate, but cooperative MARL systems instead settle on conventions throug

Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

arXiv:2606.30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.

Learning Transferable Dynamics Priors from Action to World Modeling

arXiv:2606.29501v1 Announce Type: new Abstract: We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict

LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training

arXiv:2606.30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.

LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics

arXiv:2606.29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of commu

LNN-Fly: Continuous-Time UAV Navigation for Robust Obstacle Avoidance under Timing Mismatch

arXiv:2606.28827v1 Announce Type: new Abstract: End-to-end unmanned aerial vehicle (UAV) navigation can achieve impressive agility in simulation, yet its obstacle-avoidance behavior often degrades aft

MARS: A neurosymbolic approach for interpretable drug discovery

arXiv:2410.05289v4 Announce Type: replace Abstract: Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared

Masked Diffusion Decoding as x-Prediction Flow

arXiv:2606.29066v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens, but their standard decoder reduces each step to a binary action:

Meta-learning as a principle for human-like visual representations

arXiv:2606.28399v1 Announce Type: new Abstract: The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual represe

Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy

arXiv:2606.29038v1 Announce Type: cross Abstract: Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi

MIRI Newsletter #126

Announcing: AI StopWatch In our last update, we mentioned we had something new in the works: a dedicated channel for news and analysis about AI. Subscribe to AI StopWatch An experiment from the writer

safetymiri

MIRROR: Aligning Semantic Relations from Language to Image via Gromov--Wasserstein

arXiv:2606.29462v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) inherit rich relational priors from their language backbones, yet often fail when asked to apply these relation

MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling

arXiv:2606.29265v1 Announce Type: new Abstract: Reasoning large language models (LLMs) have recently made much progress in complex problem-solving, leveraging internal reasoning (or thought) to guide

MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning

arXiv:2510.03142v2 Announce Type: replace-cross Abstract: Visual navigation policy is widely regarded as a promising direction, as it mimics humans by using egocentric visual observations for navigati

Modelling Human Values for Value-Aware Multi-Agent Systems

arXiv:2402.06359v2 Announce Type: replace Abstract: One of today's most pressing societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacti

MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training

arXiv:2606.30406v1 Announce Type: new Abstract: Modern large language models (LLMs) rely on reinforcement learning during post-training to push specific capabilities, yet integrating multiple capabili

MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment

arXiv:2606.29049v1 Announce Type: new Abstract: Knowledge Tracing (KT) is important for personalized education but traditionally suffers from two key limitations: a reliance on shallow ID-based repres

MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment

arXiv:2606.29760v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) is commonly built on two basic learning paradigms: regression and ranking. Regression calibrates absolute scores,

muFlow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

arXiv:2606.30528v1 Announce Type: new Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy a

Multimodal Large Language Model driven Radiology Report Generation with Clinical Knowledge Enhancement

arXiv:2403.06728v2 Announce Type: replace Abstract: Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. The performance o

Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506.08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways. This variability is particularly valuable for i

Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation

arXiv:2606.28600v1 Announce Type: cross Abstract: Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems