REAL: Repairability-Aware Latent Alignment for Low-Resource Languages

Multilingual language models remain less accurate in low-resource languages. Translation-based approaches change the input language, and fine-tuning updates model weights; activation steering preserves both but can also damage correct answers. We introduce REAL, an inference-time method that proposes cross-lingual latent interventions and selects among their executed outcomes using predicted repair and damage. REAL improves low-resource answers without changing the backbone, translating the test question, or querying paired high-resource states. On all 14,042 official MMMLU examples in each of four Yoruba/Swahili model–language settings, REAL improves Qwen3-8B and Llama-3.1-8B-Instruct by 6.65–11.12 percentage points. Across 56,168 predictions, accuracy rises by 9.04 points while preserving 98.56% of initially correct answers. With the same candidate library, verification adds 2.01 points over base-only selection and 1.51 over confidence gating. Permuting exact residual correspondence retains the gain under separately calibrated field-specific verification, whereas low-resource-only residuals trail by 4.53 points. On a full-test, automatically translated Tibetan extension, REAL gains 3.55 and 2.51 points across the two models. On AfriQA free generation, the library contains 3.38–8.06 F1 points of preserving-oracle headroom across four models, although the tested selectors do not deliver reliable gains. Together, these results establish repair-aware selection as an effective way to turn cross-lingual interventions into accuracy gains for low-resource choice scoring.