VisCoder2: Building Multi-Language Visualization Coding Agents

Yuansheng Ni, Songcheng Cai, Xiangchao Chen, Jiarong Liang, Zhiheng Lyu, Jiaqi Deng, Kai Zou, Ping Nie, Fei Yuan, Xiang Yue, Wenhu Chen · Oct 24, 2025 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

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.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly describe the evaluation setup.

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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 25%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code."

Benchmarks / Datasets

partial

Visplotbench

Useful for quick benchmark comparison.

"VisPlotBench is a benchmark for systematic evaluation, featuring executable tasks, rendered outputs, and protocols for both initial generation and multi-round self-debug."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code."

Human Feedback Details

Uses human feedback: No
Feedback types: None
Rater population: Not reported
Expertise required: Coding

Evaluation Details

Evaluation modes:
Agentic eval: None
Quality controls: Not reported
Evidence quality: Low
Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

Visplotbench

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

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.

Researcher Actions

Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
Use related-paper links to find stronger protocol-specific references.

Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code.
To address these challenges, we introduce three complementary resources for advancing visualization coding agents.
Finally, we present VisCoder2, a family of multi-language visualization models trained on VisCode-Multi-679K.

Why It Matters For Eval

Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code.
To address these challenges, we introduce three complementary resources for advancing visualization coding agents.

Researcher Checklist

Gap: Human feedback protocol is explicit

No explicit human feedback protocol detected.
Gap: Evaluation mode is explicit

No clear evaluation mode extracted.
Gap: Quality control reporting appears

No calibration/adjudication/IAA control explicitly detected.
Pass: Benchmark or dataset anchors are present

Detected: Visplotbench
Gap: Metric reporting is present

No metric terms extracted.

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