Large Language Models (LLMs) often struggle with formal logical reasoning, frequently conflating content plausibility with logical validity. This well-known content effect undermines their capacity to act as reliable deductive reasoners, particularly in multilingual contexts where both linguistic variability and world knowledge may deepen biases. Prior work shows that prompting and tuning interventions can alleviate these issues only partially, leaving models vulnerable to semantic interference.While previous studies have explored activation steering and other test-time interventions, this work has focused predominantly on English.To make reasoning more consistent, robust, and transferable across languages, we investigate the use of activation steering—an inference-time intervention that modulates internal representations towards a cross-lingual reasoning space. Our experiments demonstrate that steering techniques constructed for English-based syllogisms generalise effectively to multilingual datasets, yielding higher formal reasoning accuracy (up to +36%) while minimally affecting language modelling performance. Moreover, steering supports partial transfer to out-of-distribution tasks, highlighting its potential as a scalable mechanism for cross-lingual transferable reasoning. These findings advance the prospect of developing LLMs that can serve as reliable soft reasoners across language landscapes.