Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing

• URL: http://arxiv.org/abs/2511.23321v1
• Date: Fri, 28 Nov 2025 16:23:04 GMT
• Title: Chart2Code-MoLA: Efficient Multi-Modal Code Generation via Adaptive Expert Routing
• Authors: Yifei Wang, Jacky Keung, Zhenyu Mao, Jingyu Zhang, Yuchen Cao,
• Abstract summary: C2C-MoLA is a framework that synergizes Mixture of Experts (MoE) with Low-Rank Adaptation (LoRA)<n>LoRA enables parameter-efficient updates for resource-conscious tuning.<n>Experiments on Chart2Code-160k show that the proposed model improves generation accuracy by up to 17%.
• Score: 20.521717930460692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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