Vision-language models (VLMs) have the potential to enhance machine translation (MT) by leveraging visual context alongside text, yet their real utility for production workflows remains unclear. We conduct a unified, multi-condition evaluation of six leading VLMs—both open and proprietary—on two challenging benchmarks (CoMMuTE and CaMMT), targeting lexical and cultural disambiguation respectively, with a domain-style case study simulating technical documentation localization. Results show that model performance varies widely, and the benefit of relevant images does not necessarily transfer across use cases. Proprietary models are notably sensitive to irrelevant images while open-source models are generally more stable; incorrect or contradicting visuals, by contrast, degrade translation across all models. Taken together, these findings make rigorous evaluation a necessary precondition for production deployment: metric gains can mask real accuracy losses in technical domains, model sensitivity to irrelevant images should inform model selection, and reliable image–text matching is a hard requirement for any pipeline.

Post-Editing (PE) is typically performed on isolated segments or small batches, without access to broader document context. In this paper, we investigate whether pre-generated, document-level summaries can improve PE quality. Using a purpose-built summarization prompt evaluated across nine LLMs from OpenAI and Google, we select two models with contrasting summary styles for downstream experiments on 448 documents covering 37 target locales and 13 content domains. Summaries generated by gemini-2.5-flash-lite, which are directive and domain-specific, yield gains in edit distance and modest gains in COMET, whereas those generated by GPT-4o, which tend to be more generic and descriptive, degrade performance across most metrics. The positive effect appears most pronounced in terminologically dense domains and lower-resource locales. A qualitative analysis shows that improvements arise when summaries provide specific, actionable guidance on terminology, domain conventions, and style, and that performance decreases when summaries are underspecified or conflicting. These findings suggest that summary specificity and actionability, rather than the mere addition of context, determine whether document-level information benefits post-editing.

Localization strategies can differ widely between languages, but the necessity and efficiency of maintaining distinct strategies for closely related variants of the same language is debatable. This paper explores the potential for unifying localization strategies across different Spanish locales, leveraging Large Language Models, prompting techniques, and specialized linguistic resources to perform cross-locale adaptations from a chosen baseline. In this study, we examine and develop vocabulary, terminology, grammar, and style transformation methods from Latin American into Mexican and Argentine Spanish. Our findings suggest that parting from a core translation and then following an automated adaptation process to unify localization strategies is feasible for Spanish diverse variants, regardless of the type of divergence each of them has from the baseline locale. However, even if the need for human post-editing is then minimal compared to a fully ‘manual’ cross-locale adaptation, the linguistic review remains crucial, particularly for editing style nuances.