My (Chiffon) Nguyen

My (Chiffon) Nguyen she/her

Nguyễn Trà My / 阮沐茶 / 윈자미

AI Research for Broader World & Life-long Learning

I research current and future AI that are safe and empowering for more people. Towards this end, I’m currently interested in the following problems:

My technical work focuses on data, simulation, and evaluation, to make grounded, predictive, specific claims about AI capability and safety, then improve them.

I’m a SPAR Fellow (Fall ’26), mentored by Dr. Linh Le and David Williams-King (Lida Safety). I also work as a contractor for Equistamp on AI control projects.

Previously I worked with Dr. Samuel Cahyawijaya (Cohere Labs) through SEACrowd Apprenticeship, and Eyon Jang (then MATS now Scale AI) on CoT monitoring inverse scaling behavior.

I am open to research collaboration and best reached by email.

Latest updates

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My first co-first-authored paper, SEATauBench, is accepted as EMNLP Findings 2026.

Selected publications

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GPS-Bench: A Governance Policy Simulator for Automating Policy Analysis

Linh Le, Melanie Bui, My Chiffon Nguyen, Zachary Schlosser, David Williams-King
Under review • 2026
Abstract
Policy analysis requires more than predicting whether a proposal will pass: it requires identifying who will be affected, how those actors respond, and what follows. LLM-based policy simulations model these processes at scale, but their validity is hard to establish when plausible behaviour is never compared with observed outcomes. We introduce GPS-Bench, an evidence-grounded benchmark for governance policy simulation that links policies to relevant actors, actor actions and downstream impacts using legislative records, lobbying disclosures, regulatory documents, corporate filings, economic data and other public evidence. Actors are reconstructed from the dated record rather than prompted as archetypes, so a persona is an evidence object with provenance; a human-annotated pool forms the Gold evaluation set, while cases labelled by a separate LLM from retrieved evidence are treated as Silver supervision and never as test labels. Because every inference mode reads the same grounded state and emits the same schema, GPS-Bench turns 'does multi-agent simulation help?' into a controlled comparison: we contrast joint reasoning, independent and communicating actor agents, graph-based methods and weight-level fine-tuning over one policy state. Fine-tuning on the grounded record gives the strongest actor-level impact prediction, and decomposition does not beat it; what decomposition adds is mechanism. Agents hold private, non-identical evidence, each seeing its own exposure clause, and address named partners with concrete joint proposals—what they offer, what they need in return, and why acting together beats acting alone—so the coalitions that form can be checked against the commitments the record holds. GPS-Bench therefore gives a common empirical setting for studying when evidence, actor modelling and multi-agent interaction improve the prediction and interpretation of policy outcomes.
PDF
Evaluation/Benchmarking AI Governance Agent

SEATauBench: Progressively Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages

My Chiffon Nguyen*, Aulia Adila*, Saksorn Ruangtanusak*, Kittiphat Leesombatwathana*, Vissuta Gunawan Lim*, Patomporn Payoungkhamdee, Samuel Cahyawijaya
EMNLP Findings • 2026
Abstract
While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood despite its importance to sovereign AI. To fill this gap, we introduce SEATauBench, the first agent-focused evaluation framework for SEA sovereign AI. It adapts τ²-Bench (Barres et al., 2025) to five languages—Mandarin, Vietnamese, Thai, Indonesian, and Filipino—and evaluates agents cross three progressively localized settings that vary the language of user-agent interaction, tool specifications, and task domains. Across three models, we find that English agent capabilities transfer reasonably well when only the conversation language changes, but quality and robustness degrade sharply as more task contexts are localized, with the largest losses in full domain adaptation. We also highlight the limits of English-only agent assessment for predicting agent capabilities in SEA languages. More broadly, SEATauBench provides a diagnostic benchmark and reusable adaptation pipeline for building reliable multilingual agents for linguistically diverse regions.
Code PDF Website
Evaluation/Benchmarking Agent Multilingual AI

Selected projects

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Featured project: Behavior of Chain-of-thought Monitorability

Studying how monitor effectiveness changes as the capability gap between monitor and target models widens, with a case study on distinguishing sandbagging from genuine incapability.

Python · LLM evaluation · Chain-of-thought monitoring

Career highlights

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Miscellaneous

  • My Vietnamese name is ‘Trà My’, which is Camellia japonica (tea flower).

  • I attended Minerva University (🇺🇸 AY 21-25) studying Machine Learning & Statistics, with peers from 50+ countries and living in 6 cities as part of its (old) global immersion model: Seoul (South Korea), Taipei, Hyderabad (India), Buenos Aires (Argentina), Berlin (Germany). This experience heavily shapes my worldviews and motivates my research focus in diversity and collaboration.

  • Before that, I went to Foreign Language Specialized School (🇻🇳 Chuyên Ngoại Ngữ, acceptance rate ~11%) during AY 17-20, where I studied English and French.

  • I speak a bit of 🇨🇳 Mandarin Chinese (HSK4/B1) and 🇫🇷 French (A2).

  • I like reading, cooking, tea, cycling, teaching, travel, event organizing, philosophy, and politics.

  • My favorite book series are Dune, Project Hail Mary, and 契子.

  • My friends asked me to collect tech recommendation in a page here.

  • Donate to charities if you can afford it! Some options are World Food Programme, Doctors Without Borders, and Giving What We Can.