VisCoder2: Building Multi-Language Visualization Coding Agents
Authors: name: Yuansheng Ni, name: Songcheng Cai, name: Xiangchao Chen, name: Jiarong Liang, name: Zhiheng Lyu, name: Jiaqi Deng, name: Kai Zou, name: Ping Nie, name: Fei Yuan, name: Xiang Yue, name: Wenhu Chen
Organization: TIGER-Lab

Large language models (LLMs) have recently enabled coding agents capable of
generating, executing, and revising visualization code. However, existing
models often fail in practical workflows due to limited language coverage,
unreliable execution, and lack of iterative correction mechanisms. Progress has
been constrained by narrow datasets and benchmarks that emphasize single-round
generation and single-language tasks. To address these challenges, we introduce
three complementary resources for advancing visualization coding agents.
VisCode-Multi-679K is a large-scale, supervised dataset containing 679K
validated and executable visualization samples with multi-turn correction
dialogues across 12 programming languages. VisPlotBench is a benchmark for
systematic evaluation, featuring executable tasks, rendered outputs, and
protocols for both initial generation and multi-round self-debug. Finally, we
present VisCoder2, a family of multi-language visualization models trained on
VisCode-Multi-679K. Experiments show that VisCoder2 significantly outperforms
strong open-source baselines and approaches the performance of proprietary
models like GPT-4.1, with further gains from iterative self-debug, reaching
82.4% overall execution pass rate at the 32B scale, particularly in symbolic or
compiler-dependent languages.