VisCoder2: Building Multi-Language Visualization Coding Agents
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.
6901768a646208eac0d1f37d
Yuansheng Ni
claimed_verified
2025-10-29T12:45:46.613Z
64de37ee5e192985054be575
https://cdn-avatars.huggingface.co/v1/production/uploads/64de37ee5e192985054be575/fVV7JQMtp_J3uFqszJJHH.jpeg
Yuansheng Ni
yuanshengni
user
yuanshengni
6901768a646208eac0d1f37e
Songcheng Cai
claimed_verified
2025-10-29T12:45:43.616Z
670d7aed372cb8fadbd270bb
/avatars/652c8b90d492e3e5db6c735d69ae0991.svg
Songcheng Cai
SongchengCai
user
SongchengCai
6901768a646208eac0d1f37f
Xiangchao Chen
claimed_verified
2025-10-31T14:28:15.337Z
684c8b7349cb60c8c190d0a0
https://cdn-avatars.huggingface.co/v1/production/uploads/684c8b7349cb60c8c190d0a0/8916yYbA4GrAI065DWoow.jpeg
Xiangchao Chen
UlyssesXC
user
UlyssesXC
6901768a646208eac0d1f380
Jiarong Liang
claimed_verified
2025-12-02T08:50:24.381Z
68cccfe16e4618473d571b1f
https://cdn-avatars.huggingface.co/v1/production/uploads/68cccfe16e4618473d571b1f/xeF5Ojc5Q1qvtnWy7XoQL.jpeg
Jiarong Liang
lllqaq
user
lllqaq
6901768a646208eac0d1f381
Zhiheng Lyu
6901768a646208eac0d1f382
Jiaqi Deng
6901768a646208eac0d1f383
Kai Zou
6901768a646208eac0d1f384
Ping Nie
claimed_verified
2025-10-29T12:45:39.951Z
65358802a920f38780b3248a
/avatars/9415510b598079973c2b0436ad12db9c.svg
Ping Nie
pingnieuk
user
pingnieuk
6901768a646208eac0d1f385
Fei Yuan
6901768a646208eac0d1f386
Xiang Yue
6901768a646208eac0d1f387
Wenhu Chen
VisCoder2: Building Multi-Language Visualization Coding Agents
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.
VisCoder2, a multi-language visualization model trained on VisCode-Multi-679K, outperforms open-source baselines and approaches proprietary models in visualization coding tasks, especially with iterative self-debugging.
large language models coding agents visualization code language coverage execution reliability iterative correction mechanisms VisCode-Multi-679K VisPlotBench multi-turn correction dialogues multi-language tasks VisCoder2 execution pass rate symbolic languages compiler-dependent languages