mlsanityData to 7 Oct 2026

Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance

arXiv 2503.04736 ↗First posted 3 Feb 2025cs.CY cs.AI cs.CLcited by 5

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Abstract (arXiv)

Technical standards, or simply standards, are established documented guidelines and rules that facilitate the interoperability, quality, and accuracy of systems and processes. In recent years, we have witnessed an emerging paradigm shift where the adoption of generative AI (GenAI) models has increased tremendously, spreading implementation interests across standard-driven industries, including engineering, legal, healthcare, and education. In this paper, we assess the criticality levels of different standards across domains and sectors and complement them by grading the current compliance capabilities of state-of-the-art GenAI models. To support the discussion, we outline possible challenges and opportunities with integrating GenAI for standard compliance tasks while also providing actionable recommendations for entities involved with developing and using standards. Overall, we argue that aligning GenAI with standards through computational methods can help strengthen regulatory and operational compliance. We anticipate this area of research will play a central role in the management, oversight, and trustworthiness of larger, more powerful GenAI-based systems in the near future.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 55 of its 103 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

55 of 103 references.

References · 55

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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Tracescited by 4
  2. 2026Safer Policy Compliance with Dynamic Epistemic Fallbackcited by 1
  3. 2026Constrained Process Maps for Multi-Agent Generative AI Workflowscited by 1
  4. 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoningcited by 0
  5. 2026Evaluating and Preventing Security Smells in AI-Generated Ansible Codecited by 0