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: What is the impact of integrating domain-specific multilingual datasets into the hybrid batch training strategy on cross-lingual retrieval accuracy across different domains (e.g., biomedical, legal) in the XTREME-R benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.