Multilingual pre-training under severe data con straints can suffer from limited representational capacity and uneven tokenization across lan guages. Addressing the BabyLM 2026 Multi lingual Track, we study a compact pre-training recipe under the 100M-word budget. Our main contribution is a Calibrated Regex-Guided Byte-Level BPE (Regex-BBPE) tokenizer that combines script-aware pre-tokenization with byte-premium-adjusted tokenizer training in a 16K vocabulary. We pair this tokenizer with the previously proposed Attention Residuals mechanism and the Muon optimizer, and eval uate three English/Dutch/Chinese mixing ra tios. Among the tested mixtures, the Chinese dominant configuration (20% English, 20% Dutch, 60% Chinese) gives the strongest ag gregate fine-tuning results. Zero-shot results are mixed and remain slightly below the offi cial GPT-2 trilingual baseline on average, while fine-tuning improves the language-level aver ages by 8.55, 9.55, and 10.09 accuracy points for English, Dutch, and Chinese, respectively. The mixing-ratio experiments also show differ ent sensitivity patterns for the two West Ger manic languages and Chinese; we treat this as an observation within the tested language triple rather than a general cross-lingual law.