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PaperVoyager : Building Interactive Web with Visual Language Models

Dasen Dai, Biao Wu, Meng Fang, Wenhao Wang · Mar 24, 2026 · Citations: 0

Tool Use
Open arXiv Find Implementation RSS feed Shortlist (0)

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

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing document agents mainly transform papers into static artifacts such as summaries, webpages, or slides, which are insufficient for technical papers involving dynamic mechanisms and state transitions. In this work, we propose a Paper-to-Interactive-System Agent that converts research papers into executable interactive web systems. Given a PDF paper, the agent performs end-to-end processing without human intervention, including paper understanding, system modeling, and interactive webpage synthesis, enabling users to manipulate inputs and observe dynamic behaviors. To evaluate this task, we introduce a benchmark of 19 research papers paired with expert-built interactive systems as ground truth. We further propose PaperVoyager, a structured generation framework that explicitly models mechanisms and interaction logic during synthesis. Experiments show that PaperVoyager significantly improves the quality of generated interactive systems, offering a new paradigm for interactive scientific paper understanding.

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 name benchmarks or metrics.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

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

A secondary eval reference to pair with stronger protocol papers.

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

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

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.

"Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"To evaluate this task, we introduce a benchmark of 19 research papers paired with expert-built interactive systems as ground truth."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: General

Evaluation Details

  • Evaluation modes:
  • Agentic eval: Tool Use
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding.

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

Key Takeaways

  • Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding.
  • However, existing document agents mainly transform papers into static artifacts such as summaries, webpages, or slides, which are insufficient for technical papers involving dynamic mechanisms and state transitions.
  • In this work, we propose a Paper-to-Interactive-System Agent that converts research papers into executable interactive web systems.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Tool-use evaluation) against the full paper.
  • 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.

Research Summary

Contribution Summary

  • Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding.
  • In this work, we propose a Paper-to-Interactive-System Agent that converts research papers into executable interactive web systems.
  • To evaluate this task, we introduce a benchmark of 19 research papers paired with expert-built interactive systems as ground truth.

Why It Matters For Eval

  • In this work, we propose a Paper-to-Interactive-System Agent that converts research papers into executable interactive web systems.
  • To evaluate this task, we introduce a benchmark of 19 research papers paired with expert-built interactive systems as ground truth.

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.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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