Standardization Trends on Safety and Trustworthiness Technology for Advanced AI
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
Artificial Intelligence (AI) has rapidly evolved over the past decade and has advanced in areas such as language comprehension, image and video recognition, programming, and scientific reasoning. Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence. These systems demonstrate superior performance in complex problem solving, natural language processing, and multi-domain tasks, and can potentially transform fields such as science, industry, healthcare, and education. However, these advancements have raised concerns regarding the safety and trustworthiness of advanced AI, including risks related to uncontrollability, ethical conflicts, long-term socioeconomic impacts, and safety assurance. Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI. This study analyzes international trends in safety and trustworthiness standardization for advanced AI, identifies key areas for standardization, proposes future directions and strategies, and draws policy implications. The goal is to support the safe and trustworthy development of advanced AI and enhance international competitiveness through effective standardization.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 33 of its 50 references that are in the map. First posted on arXiv 29 Oct 2024.
Builds on
33 of 50 references.
References · 33
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,999
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,218
- 2022Survey of Hallucination in Natural Language Generationcited by 2,068
- 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,057
- 2023A Survey on Evaluation of Large Language Modelscited by 1,236
- 2024Large Language Models: A Surveycited by 520
- 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 400
- 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 395
- 2024TrustLLM: Trustworthiness in Large Language Modelscited by 284
- 2023AI Alignment: A Comprehensive Surveycited by 261
- 2023Large Language Model Alignment: A Surveycited by 243
- 2023PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Promptscited by 224
21 more
- 2023An Overview of Catastrophic AI Riskscited by 219
- 2023Evaluating Large Language Models: A Comprehensive Surveycited by 201
- 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 186
- 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 164
- 2024Evaluating Frontier Models for Dangerous Capabilitiescited by 126
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 98
- 2023Evaluating Language-Model Agents on Realistic Autonomous Taskscited by 96
- 2023Levels of AGI for Operationalizing Progress on the Path to AGIcited by 95
- 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 92
- 2024Reinforcement Learning for LLM Post-Training: A Surveycited by 92
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 91
- 2024Safeguarding Large Language Models: A Surveycited by 76
- 2023The Foundation Model Transparency Indexcited by 64
- 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
- 2024AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policiescited by 36
- 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 22
- 2024Holistic Safety and Responsibility Evaluations of Advanced AI Modelscited by 19
- 2024A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harmscited by 19
- 2024Responsible Reporting for Frontier AI Developmentcited by 10
- 2024An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mappingcited by 8
- 2024A Survey on Human-AI Collaboration with Large Foundation Modelscited by 7
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2020An overview of 11 proposals for building safe advanced AI
- 2025A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
- 2024Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
- 2026A Unified Evaluation Framework for Trustworthy Large Language Models, Agentic AI, and Multimodal Systems
- 2024Large Language Model Safety: A Holistic Survey
- 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI
- 2026SafePro: Evaluating the Safety of Professional-Level AI Agents
- 2025Policy-Grounded Safety Evaluation of 20 Large Language Models