Multilingual large language models exhibit substantial performance differences across languages, while existing adaptation methods often require parameter updates and considerable multilingual training data. We propose an inference-time multilingual steering method that uses pretrained sparse autoencoders to identify and strengthen target-language-related features. Using multilingual parallel sentences, we compare SAE activations across languages and select a small number of layer-specific features associated with each target language. These features are decoded into steering signals and injected into the model's hidden states without additional training. Experiments with Gemma-3-12B-it show macro-average accuracy improvements of 11.28 percentage points on XCOPA, 4.11 points on XNLI, and 7.84 points on PAWS-X. Additional evaluation with Qwen2.5-7B-Instruct yields a macro-average improvement of 3.33 percentage points on XCOPA.

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