{"ID":2835590,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.23321","arxiv_id":"2511.23321","title":"Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing","abstract":"Chart-to-code generation is a critical task in automated data visualization, translating complex chart structures into executable programs. While recent Multi-modal Large Language Models (MLLMs) improve chart representation, existing approaches still struggle to achieve cross-type generalization, memory efficiency, and modular design. To address these challenges, this paper proposes C2C-MoLA, a multimodal framework that synergizes Mixture of Experts (MoE) with Low-Rank Adaptation (LoRA). The MoE component uses a complexity-aware routing mechanism with domain-specialized experts and load-balanced sparse gating, dynamically allocating inputs based on learnable structural metrics like element count and chart complexity. LoRA enables parameter-efficient updates for resource-conscious tuning, further supported by a tailored training strategy that aligns routing stability with semantic accuracy. Experiments on Chart2Code-160k show that the proposed model improves generation accuracy by up to 17%, reduces peak GPU memory by 18%, and accelerates convergence by 20%, when compared to standard fine-tuning and LoRA-only baselines, particularly on complex charts. Ablation studies validate optimal designs, such as 8 experts and rank-8 LoRA, and confirm scalability for real-world multimodal code generation.","short_abstract":"Chart-to-code generation is a critical task in automated data visualization, translating complex chart structures into executable programs. While recent Multi-modal Large Language Models (MLLMs) improve chart representation, existing approaches still struggle to achieve cross-type generalization, memory efficiency, and...","url_abs":"https://arxiv.org/abs/2511.23321","url_pdf":"https://arxiv.org/pdf/2511.23321v1","authors":"[\"Yifei Wang\",\"Jacky Keung\",\"Zhenyu Mao\",\"Jingyu Zhang\",\"Yuchen Cao\"]","published":"2025-11-28T16:23:04Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Mixture of Experts\",\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}