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IChart2Code: Benchmarking Multimodal Large Language Models for Interactive Chart Code Generation — arXiv:2609.32413

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32413
License (as archived): CC0-1.0
Archived: 2026-10-07T23:02:09.100Z
SHA-256 of the archived text: 0d1fe8b600aa3c935c54219c8d450584180ef03bee9fcd5544bce578c86ee815
Stable URL of this page: https://metraipolis.com/documento/dc_2aec034838ca

How to cite

IChart2Code: Benchmarking Multimodal Large Language Models for Interactive Chart Code Generation — arXiv:2609.32413 — arXiv (en), https://arxiv.org/abs/2609.32413 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2aec034838ca (Metraipolis archive dc_2aec034838ca, sha256 0d1fe8b600aa3c935c54219c8d450584180ef03bee9fcd5544bce578c86ee815)

BibTeX

@misc{dc2aec034838ca,
  title = {IChart2Code: Benchmarking Multimodal Large Language Models for Interactive Chart Code Generation — arXiv:2609.32413},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32413},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2aec034838ca; sha256 0d1fe8b600aa3c935c54219c8d450584180ef03bee9fcd5544bce578c86ee815},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32413. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Interactive chart code generation requires models to reproduce a reference chart's appearance and underlying data and correctly implement the state changes triggered by specified user interactions. Existing chart-to-code benchmarks focus on static outputs and lack task representations or evaluation protocols for interaction specification, browser execution, and post-interaction verification. We introduce IChart2Code, a benchmark comprising 377 tasks across 20 chart forms and 13 data families, with 1209 interaction requirements in six families. Each task provides a reference screenshot, task-local data, and natural-language interaction requirements, with executable HTML/JavaScript code as the target output. We further develop a browser-based evaluation protocol with an Executability gate and three rubric-guided dimensions: Data Fidelity, Static Visual Correctness, and Interaction Correctness. The protocol tests runtime viability, consistency with task-local data, fidelity of the initial rendering to the reference screenshot, and interaction-induced state changes in a sandboxed browser. A rubric-guided MLLM judge evaluates task-specific items for the three scored dimensions using the collected browser observations and achieves an overall item-level F1 score of 0.8844 against adjudicated human labels. We also propose TRAIL, a trajectory-guided dual-agent framework for interactive chart code generation. An Inspector derives task-specific inspection checks, executes them in the browser, and uses the resulting trajectories to diagnose failures and produce structured repair feedback. Averaged across four MLLMs, TRAIL improves the four evaluation dimensions over direct prompting by 7.89, 4.98, 3.45, and 4.47 percentage points, respectively.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-29 in cs.SE).
Categorie arXiv: Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.32413 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:39:35.183Z dalla pagina https://arxiv.org/catchup/cs.SE/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).