Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual langResearch goal: To what extent does the proposed hybrid batch training strategy improve cross-lingual retrieval robustness on the XLID benchmark compared to standard multilingual fine-tuning across diverse language families?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10.