safety

Is Per-Agent Policy Composition Safe? Rethinking Successor-Feature Transfer in Cooperative Multi-Agent Reinforcement Learning

arXiv:2608.11658v1 Announce Type: cross Abstract: Many reinforcement learning systems, from fleet management to traffic signal control, must serve an objective that changes dynamically after deploymen

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LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification

DGX agent

arXiv:2608.11753v1 Announce Type: new Abstract: Financial text is produced and interpreted within a market environment, yet financial text classifiers almost always receive text alone. We study whethe

safetyarxiv-cs-cl

13 Aug 2026

Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving

arXiv:2608.11498v1 Announce Type: new Abstract: Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whet

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation

arXiv:2603.13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation

LazyTrain: Limited-resource Allocation toward Zero-waste Yield Optimization in Large Language Model Training

arXiv:2608.11919v1 Announce Type: new Abstract: Training large language models on limited hardware is increasingly a scheduling problem across GPU compute, host memory, PCIe transfer, and storage band

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

arXiv:2608.12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of au

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation

arXiv:2608.11681v1 Announce Type: cross Abstract: This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize bo

Learning from Online User Feedback for Shopping Agents

arXiv:2608.11604v1 Announce Type: new Abstract: Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction

Learning Loco-Manipulation From SMPC Demonstrations With Sparse Offline-to-Online RL

arXiv:2608.12063v1 Announce Type: cross Abstract: Integrating locomotion and manipulation is essential for robot autonomy, but scaling standard Reinforcement Learning (RL) to complex tasks is severely

Let it Cook: Learning to Wait in Sequential Decision Making

arXiv:2608.11511v1 Announce Type: cross Abstract: In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not alwa

LiDAR-based 3D Change Detection at City Scale

arXiv:2510.21112v3 Announce Type: replace-cross Abstract: High-definition 3D city maps enable city planning and change detection, which is essential for municipal compliance, map maintenance, and asse

LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation

arXiv:2608.11967v1 Announce Type: cross Abstract: Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical ca

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

arXiv:2608.12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection a

Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning

arXiv:2608.11705v1 Announce Type: new Abstract: Aligned large language models (LLMs) are expected to exhibit safety behavior based on the content of the user request: they should refuse unsafe request

Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models

arXiv:2606.13840v2 Announce Type: replace-cross Abstract: Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and

NAE: Normalizing AutoEncoder

arXiv:2608.12084v1 Announce Type: new Abstract: We consider the setting of Normalizing flows with approximate inverses, an established paradigm spanning both full-dimensional (d=D) and bottleneck (d<D

On the Definition of Intelligence

arXiv:2507.22423v3 Announce Type: replace Abstract: To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently gene

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL

arXiv:2608.12253v1 Announce Type: cross Abstract: Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that

PAIR: Pairwise-Aware Inclusion Reweighting for Adaptive Rollout Allocation in RLVR

arXiv:2608.11368v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) spends most of its compute generating groups of long reasoning trajectories. Recent allocators red

Policy-as-logic for robust reasoning over rules

arXiv:2608.11905v1 Announce Type: new Abstract: In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect

Policy-Induced Hand Priors in Humanoid Dual-Arm Manipulation: Diagnosing and Mitigating Initial-Pose Dependence

arXiv:2608.11769v1 Announce Type: new Abstract: Vision-language-action (VLA) policies are expected to operate robustly across variations in the robot's initial configuration, yet aggregate task succes

safetyarxiv-cs-ro

Post-Training with Policy Gradients: Optimality and the Base Model Barrier

arXiv:2603.06957v2 Announce Type: replace-cross Abstract: We study post-training linear autoregressive models with outcome and process rewards. Given a context oldsymbol{x}, the model must predict the

Prompt-Driven Exploration

arXiv:2607.08837v2 Announce Type: replace-cross Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject

Proportional Committee Elections with Positive and Negative Votes

arXiv:2503.01985v2 Announce Type: replace-cross Abstract: In the classic committee election setting each voter approves a subset of candidates and the goal is to select k winners based on these prefer

Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs

arXiv:2608.11830v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environme

RA-ClipScore: Making Generative Model Evaluation More Interpretable

arXiv:2608.12088v1 Announce Type: new Abstract: Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conventiona

Redistribution-based Cost Inference Improves Sparse Safe Offline RL

arXiv:2608.12306v1 Announce Type: cross Abstract: Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level stop-feedback:

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation

arXiv:2608.11698v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own trajectories under dense token-level supervision from a teacher. Reward-extrapolation methods

Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints

arXiv:2608.11383v1 Announce Type: new Abstract: We study new algorithms for Contextual Bandits with Knapsack. In these problems, there are finitely many types of customers, products, and resources. Ea

Reproducible canvas-aligned low-level patterns in somerandomllm-generated images and their possible relation to iterative editing artifacts [D]

I may have stumbled onto something interesting while trying to figure out a recurring artifact in ChatGPT image generation and editing (maybe applicable to other models as well?). It started with a ve

safetyr-machinelearning

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

arXiv:2608.11587v1 Announce Type: cross Abstract: Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalist

ScaleVid: Geometry-Aware Video Object Scaling with Mesh-Free Inference

arXiv:2608.12232v1 Announce Type: new Abstract: Geometry-aware video object scaling aims to anisotropically resize the object along object-centric axes while preserving geometric plausibility, tempora

Small Data Explainer -- The impact of small data methods in everyday life

arXiv:2507.11773v2 Announce Type: replace-cross Abstract: The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings wi

Sources: Demis Hassabis pitched a new independent industry AI safety entity, modeled on the IAEA, to top Trump officials before stepping down as DeepMind's CEO (Wall Street Journal)

Wall Street Journal: Sources: Demis Hassabis pitched a new independent industry AI safety entity, modeled on the IAEA, to top Trump officials before stepping down as DeepMind's CEO — Demis Hassabis di

safetytechmeme

Stolen LLM Reasoning: How come OpenAI, Anthrophic, Google have the same vulnerabilities?

If you haven't checked the paper: https://arxiv.org/abs/2608.09867 TLDR: the authors show that you can swap out the 'encrypted' reasoning of the biggest model, like Opus, Sol, and put them into weaker

safetyr-localllama

TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement

arXiv:2608.11951v1 Announce Type: cross Abstract: Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operationa

Through Van Gogh's Eyes: Global Style Transfer with Diffusion Mod

arXiv:2608.11546v1 Announce Type: new Abstract: Artistic image synthesis aims to recreate the expressive visual identity of a target artist, yet existing methods often fail to capture an artist's glob

ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents

arXiv:2608.11878v1 Announce Type: cross Abstract: Large language model (LLM) agents integrated with external tools are vulnerable to indirect prompt injections embedded in environmental states. Howeve

Topology-Aware Query Selection for Surgical Instrument Instance Segmentation

arXiv:2608.11607v1 Announce Type: new Abstract: Accurate foreground masks can still form an incorrect surgical-instrument instance set: duplicate, fragmented, merged, missed, or empty-frame prediction

Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion

arXiv:2608.11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content prove

Towards a Formal Definition of Agent Memory: Basis, Span, Optimality, and the Sequential Memory Problem

arXiv:2608.11654v1 Announce Type: new Abstract: Despite the wide deployment of memory in large-model agents, there is no unified formal account of what a memory is or when it is optimal. This paper ta

Towards Human Motion World Models via Executable Behaviour Representations

arXiv:2604.18064v2 Announce Type: replace Abstract: Human motion world models should capture motion's intentionality by being executable: adaptable to different actions and capable of assessing motion

Towards Understanding On-Policy Distillation through the Lens of Test-Time Scaling

arXiv:2608.11829v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as a promising post-training technique for enhancing LLM reasoning. It is commonly believed to enable the stu

UniSwap: Streaming Audio-Visual Identity Swapping for Talking Videos

arXiv:2608.11752v1 Announce Type: new Abstract: Talking-video character replacement requires coordinated transfer of appearance and voice while preserving the source motion, scene, linguistic content,

Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits

arXiv:2608.11410v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisions, yet standard evaluation metrics Mean Squared Err

Variable Selection in the Context of AI Fairness

arXiv:2608.11251v1 Announce Type: cross Abstract: Fairness in AI systems has become more important with recent regulatory demands, such as the EU AI Act. Traditional approaches often do not take into

Video2Track: From Real-World Interaction Videos to Steerable Adversarial Closed-Track Testing for Automated Driving Systems

arXiv:2608.11592v1 Announce Type: new Abstract: Closed-track testing plays a fundamental role in the verification and validation of automated driving systems (ADS), particularly for safety-critical sc

safetyarxiv-cs-ro

Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study

arXiv:2608.11649v1 Announce Type: new Abstract: As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the po