Lost in Translation? Measuring Language-Conditioned Detection-Rate Gaps in AI Code Auditors on Spanish- and Portuguese-Surface Codebases

The paper is technically detailed and carefully documented, but the presentation assumes substantial prior knowledge of smart-contract auditing and blockchain security. The extensive use of domain-specific terminology may make the work difficult for a broader AI safety audience to follow. Much of the discussion focuses on implementation details and evaluation infrastructure, which can make the central AI safety question difficult to identify. A clearer motivation, simpler framing, and greater emphasis on the practical implications of the findings would improve accessibility and strengthen the overall presentation.

- Very well written and very clear.

- Methodology well explained and well defined.

- Limitations are clearly presented. The number of twins is very low to draw conclusions, but it can serve as a basis for future research. With such a small number, no real conclusions can be drawn.

- Some sections of the results could be better explained or summarized.

The problem it addresses is that of errors which an auditor might make when working with code in Spanish and Portuguese, given that the existing auditors are native English speakers. Methodologically, it is sound and interesting. Its proposed analysis of the model’s effort and laziness is particularly interesting. The governance issue is significant and focuses on the financial sector in Latin America; however, it is difficult to grasp as it gets lost in a technical argument. My recommendation to the authors is that they present a non-technical approach and formulation of the problem and subsequently develop it along those lines. Otherwise, they run the risk of the reader losing sight of the scope of their proposal in terms of governance. Another observation is that, whilst the authors appear to be familiar with the jargon and issues within the scope of their proposal—which is not necessarily the case—the reader may feel lost as to what DeFi, Cashio, Loopscale, Mango Markets and zkLend actually mean. These terms are not explained until the end of the text. This makes it difficult to grasp the ideas.

This is a very clean, well‑scoped measurement that does exactly what it claims: it turns a vague multilingual safety divide concern into a concrete, reproducible eval harness for language-conditioned code auditing, with a carefully controlled translation pipeline and a clear separation between capability issues and language effects via the effort-quality classifier. The cross-model story is especially useful for practitioners since it shows frontier models maintaining 100% detection while an open-weight model exhibits the first signs of CS-sensitivity, and it does so without overclaiming beyond an N=4 twin sample. The main limitations are the small N per cell and partial open-weight coverage mean that the single CS MISS and GPT‑4o’s CS false positives should be treated as flags rather than stable effects, and the same‑family Claude‑judges‑Claude setup leaves some residual concern about scorer bias. A natural next step would be to (i) expand the twin set across more bug classes and VMs, (ii) re‑run the harness with an independent judge (or a small human‑judged subset) to bound any same‑family scoring advantage, and (iii) add at least one more open‑weight family and a quantized deployment to see whether the emerging CS gap at the open‑weight tier persists under more realistic LatAm self‑hosting conditions.

This is a work that addresses a question with genuine practical relevance and does so with a level of methodological care that is notable. The experimental design is thoughtfully assembled, the scope is honestly communicated throughout, and the transparency around what can and cannot be concluded from the available evidence is one of the paper's strongest qualities. The research direction is original and the framing around deployment context adds a layer of real-world relevance that gives the contribution meaning beyond the immediate results.

Where the work naturally invites further development is in the relationship between the strength of the methodology and the weight of the evidence it currently produces. The findings are directional rather than conclusive, which the paper itself acknowledges clearly and consistently, and that honesty is appreciated. A future iteration with a broader empirical base would be well positioned to turn what is currently a promising signal into a more substantive claim, and the groundwork laid here would support that effort effectively.

The writing is precise and the structure is easy to follow, which reflects well on the overall presentation. There are moments where some condensation would improve the reading flow, as a few sections carry ideas that overlap with others nearby, but these are minor observations in an otherwise clean document. The paper reads as the work of someone who understands both the subject and the limits of what a study at this scale can responsibly assert, and that combination is genuinely valuable.