arXiv AI
Aug 5
Principles of Robot Autonomy
arXiv:2608. 03496v1 Announce Type: cross Abstract: Autonomous robots are moving rapidly from research labs into everyday life - on roads, in the air, in warehouses, and in space.
Engineering Reliable Autonomous Systems: Challenges and Solutions
arXiv:2606. 23760v1 Announce Type: cross Abstract: Engineering reliable autonomous systems is an important and growing topic in computer science.
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arXiv AI
Jul 22
Engineering Trustworthy Agentic AI for Critical Systems
arXiv:2607. 18548v1 Announce Type: new Abstract: Agentic artificial intelligence systems, capable of autonomous perception, planning, tool use, and multi-step action, are increasingly proposed for critical engineering domains where decisions carry physical, operational, or economic consequences.
arXiv AI
Jun 8
An Abstract Architecture for Explainable Autonomy in Hazardous Environments
arXiv:2606. 07211v1 Announce Type: cross Abstract: Autonomous robotic systems are being proposed for use in hazardous environments, often to reduce the risks to human workers.
arXiv AI
Jun 12
Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems
arXiv:2605. 27628v2 Announce Type: replace Abstract: As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge.
arXiv AI
Jun 15
I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts
arXiv:2606. 14327v1 Announce Type: cross Abstract: This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance.
arXiv AI
Jun 3
Towards a Science of AI Agent Reliability
arXiv:2602. 16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks.
arXiv AI
Aug 11
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
arXiv:2608. 09857v1 Announce Type: cross Abstract: Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose.
arXiv AI
Jun 2
Safety Must Precede the Deployment of Open-Ended AI
arXiv:2502. 04512v4 Announce Type: replace Abstract: AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability.
OpenAI Blog
Apr 5, 2023
Our approach to AI safety
Ensuring that AI systems are built, deployed, and used safely is critical to our mission.
arXiv AI
Jul 2
Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems
arXiv:2607. 00334v1 Announce Type: new Abstract: Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous human oversight: safety violations from unverified actions, behavioral instability from unconstrained loops, and continuity loss from unhandled error states.
arXiv AI
Sep 18
A Simulation Platform for AUV Fault Recovery: Exploring LLM-Based Diagnostic Strategies
The paper introduces SPAR, a closed‑loop simulation platform that couples real‑time AUV control software with a higher‑level orchestration layer for fault injection, prompting, and evaluation of large language models (LLMs) in diagnosing and recovering from anomalies. SPAR enables ensemble testing of LLMs, comparing a frontier model with three locally deployable LLMs on a mass‑shift fault scenario across 480 trials, revealing that model choice significantly affects diagnostic accuracy. The study demonstrates that while the frontier model consistently ranks the correct fault mechanism among its top hypotheses, local models succeed mainly when they follow the full diagnostic procedure, and overall diagnosis and operational decisions appear decoupled in this dataset.
arXiv AI
Sep 7
From Language Models to World-Acting Systems: Progress and Limits of Agentic AI across Digital, Social, Virtual, and Physical Environments
The paper reviews how large language models have evolved into agents that can influence external environments through tool use, interface operation, delegation, state retention, virtual world inhabitation, and robotic control. It critiques the narrative of a single march toward autonomy, distinguishing model competence from system integration, persistence, and safe authority. The authors find that action-interface expansion is well documented, while robust completion, recovery, authorization, and independent verification remain less proven, and they propose a framework of justified delegation to guide future research.