Oaica Research

Research on how AI systems behave, and how far we can measure it

Oaica is a safety-focused AI research company. We publish the methods, the evidence and the limits behind evaluating AI systems — across languages, models and deployments.

6Publications
4Research areas
OpenMethod & evidence

Programmes

What we work on

Safety evaluation

How safety is measured, what a benchmark score supports, and where inference has to stop.

Low-resource languages

Evaluation methods that hold up outside English, starting from Malay and extending to other regional languages.

Model behaviour

Reasoning modes, refusal behaviour and robustness under matched, reproducible conditions.

Deployment & serving

Running capable models on your own hardware, with the limits of each configuration stated plainly.

Recent publications

6 documents
Supplement
2026-10-04
Evidence supplement

Measuring the Measure: Evidence and Limits

Text-free outcome counts, recomputation code and provenance limits for our Malay LLM safety case study, stating what the published evidence can and cannot establish.

Paper
2026-10-02
Regional-language study

AI Safety Evaluation in a Regional Language: A Case Study with a Fine-Tuned Malay Model and an Indonesian Cross-Language Probe

One changed prediction can move a Malay LLM safety subtask by up to 9 points. A study of the SEA-HELM Malay safety track, an Indonesian cross-language probe, and a checklist for any language.

Note
2026-10-02
Model family notes

Oaica 35B-A3B Malay v1.0 260923 model family notes

The Oaica 35B-A3B Malay model, its safety-tuned variant and two experimental specialists: what we measured, the trade we report, and what the copy may say.

Article
2026-10-02
Deployment article

A Malay AI You Can Run Yourself

Oaica's 35B-A3B Malay model runs on your own hardware, from small GPUs to datacenter servers, with our .oqm format and a statement of what we do not claim.

Paper
2026-10-01
Malay safety case study

Malay AI Safety Evaluation: A Case Study with Oaica’s Fine-Tuned 35B-A3B Model, and an Indonesian Cross-Language Probe

How far a Malay AI safety score can be trusted. One changed prediction can move a Malay LLM safety subtask by up to 9 points. What the SEA-HELM Malay safety benchmark can and cannot resolve, with an Indonesian cross-language probe, by Oaica.

Brief
2026-10-01
Executive brief

Malay AI Safety Scores Measure Less Than You Think

Oaica, a safety-focused AI company: one changed answer can move a Malay AI safety sub-score by up to 9 points. On-premises Malay AI and safety audits.

About

Oaica researches AI safety and evaluation. We develop and study language models, and publish the foundations behind them: how a benchmark score is produced, what its intervals support, and what remains unverified. Our first programme is Malay-language safety; the methods are built to extend to other languages and models.

De-identified evaluation tables and item-leverage worksheets are available on request. Nothing on this site certifies any model as safe.