Juhao Liang

Multilingual large language models (LLMs) are essential for cross-lingual applications, yet pruning them with mixed-language calibration induces *cross-lingual interference*, disproportionately degrading low-resource and script-diverse languages. We introduce ***Lang-Prune***, a drop-in extension to structured pruning that computes importance per language on small calibration sets and aggregates via a Max operator---preserving any structure critical to at least one language. On *aya-expanse-8b* across nine typologically diverse languages, Lang-Prune reduces average perplexity by 62\% at 70\% sparsity relative to mixed-data pruning, and outperforms monolingual pruning on average under the same calibration budget. Lang-Prune generalizes across model families and scales: its advantage amplifies with model size, reaching $4.1\times$ perplexity reduction at *Qwen3-14B* and exhibiting strong zero-shot transfer to out-of-distribution languages. Post-training experiments further show preserved adaptation capacity, with pruned models reaching 59.6\% vs. 50.6\% accuracy on Belebele. Lang-Prune modifies only the importance estimation and aggregation step, preserving LLM-Pruner's coupled-structure mechanics and requiring no additional parameters or retraining.

Multimodal large language models (MLLMs) exhibit substantial performance degradation in non-English visual reasoning, despite the strong multilingual competence of their text-only backbones. While mechanistic evidence from text-only models suggests that non-English inputs are routed through an English-centric latent space, the multimodal implications of this phenomenon remain unexplored. Through rigorous mechanistic analysis, we identify the \textbf{Ghost Anchor} phenomenon: a temporal modality asynchrony where linguistic translation to the English semantic manifold completes in early layers, while visual semanticization remains immature. Consequently, visual signals are physically present yet functionally invisible during the early alignment window. To rectify this, we propose \textbf{ANCHOR}, a training framework employing Proactive Visual Anchoring (PVA) to accelerate early visual semantic emergence, ensuring visual representations proactively guide linguistic translation. Mechanistic interventions confirm that ANCHOR successfully restores the causal influence of visual signals during early translation. Furthermore, extensive experiments on XMMMU, MaXM, and CVQA demonstrate that ANCHOR consistently outperforms standard baselines, achieving robust visual reasoning across both fine-tuned and zero-shot languages.