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Sprint projectJun 21, 2026Hồ Chí Minh

Two Failure Modes Require Architectural Change: A Formal Harmonization Gap Analysis of the EU AI Act, Vietnam's AI Law, and the ASEAN AI Governance Guide

Ngô Thái Bảo · Team B.ONE

Submitted to Global South AI Safety Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Two Failure Modes Require Architectural Change: A Formal Harmonization Gap Analysis of the EU AI Act, Vietnam's AI Law, and the ASEAN AI Governance Guide

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There is a common assumption in tech policy that the EU sets the global baseline for AI regulation, with other regions simply converging toward it. However, a formal analysis of Vietnam's new AI Law (Law No. 134/2025/QH15)—Southeast Asia's first binding national AI legislation, taking effect on March 1, 2026—shows this isn't always true. In fact, for minimal-risk AI systems, Vietnam enforces a universal human-control principle where the EU AI Act remains completely silent, making Vietnam the stricter regime in terms of formal legal force.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. very pleased with the comparative approach and imaginative spirit in creating a framework to evaluate it. good job, standout. There is room for growth in the presentation and clarity side, consider putting yourself more in the headpieces of policymakers who will know very little of this and still need to understand the relevance and the steps you took.

  2. 4/3/4

    Criteria 1 — Impact Potential & Innovation: 4

    Criteria 2 — Execution Quality: 3

    Criteria 3 — Presentation & Clarity: 4

    Very innovative , good presentation quality, i understand there is little AI footprint , but that is for abstarct and wording part, thought process and ideas are original and technically solid given the short duration, limitations acknowledged, work builds toward clear conclusions.

  3. This is a genuinely rigorous piece of work! The sensitivity analysis that tested your own findings against alternative encodings is a particular strength. An independent review from a legal expert on how the statutes (the seven obligation-pair encodings) were translated into HSDL would be a good next step to further boost the validity of the work, since every quantitative result depends on those encodings. Overall, very well written and methodologically sound.

  4. Solid, reproducible work. Verification runs cleanly and strengthens the paper through self-falsification tests and documented corrections.

    To improve:

    1. Add an adversarial encoding pass. The main claims rely on your own web-based legal encodings, so the theorems prove results about the model, not directly about the statutes. A second annotator should independently encode a few obligation pairs, and you should report which conclusions survive alternative readings.

    2. Make the companion proofs public.

Cite this project

@misc{bao2026two,
  title = {{Two Failure Modes Require Architectural Change: A Formal Harmonization Gap Analysis of the EU AI Act, Vietnam's AI Law, and the ASEAN AI Governance Guide}},
  author = {Ngô Thái Bảo},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/two-failure-modes-require-architectural-change-a-formal-harmonization-gap-analysis-of-the-eu-ai-act-vietnams-ai-law-and-the-asean-ai-governance-guide-k7ml}},
  url = {https://apartresearch.com/sprints/projects/two-failure-modes-require-architectural-change-a-formal-harmonization-gap-analysis-of-the-eu-ai-act-vietnams-ai-law-and-the-asean-ai-governance-guide-k7ml}
}

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