PaperBanana × PaperVizAgent
PaperBanana and PaperVizAgent turn source notes into a reviewable first draft.
figures generated
researchers
avg. generation
Research signals
Ground the figure, plan the panels, review the result, then export without rebuilding the layout.
Context before canvas
01
source grounding
PaperVizAgent reads the research brief before panel structure is planned.
Manuscript handoff
03
exports
Move drafts into paper, poster, or slide workflows as PNG, SVG, or PDF.
Structured QA
05
review passes
Check labels, chart fit, and figure hierarchy before export.
Lab consistency
1x
shared visual system
Keep visual decisions steadier across papers, decks, and review rounds.
Create
Paste context, set the figure goal, and let PaperVizAgent prepare a structured first draft.
Source
Panel draft for encoder-decoder attention flow.
Agent protocol
PaperVizAgent keeps reading, planning, styling, rendering, and review separate so each step is easier to inspect.
PaperVizAgent extracts study context, terminology, and structural cues.
The figure is mapped into panels, labels, flows, or chart components.
Research-oriented layout rules keep color, annotation, and spacing restrained.
A draft figure is built with publication, slide, or poster composition in mind.
Semantic fit, chart legibility, and presentation quality are checked.
Pricing
Start with the figure volume your research workflow actually needs.
Perfect for individual researchers getting started.
One-off paper or class-project figures.
Billed $118.80/yr
100 credits
Credits
100
Exports
PNG
Resolution
2K
Support
For active researchers who publish regularly.
Regular manuscript, poster, and deck production.
Billed $166.80/yr
400 credits
Credits
400
Exports
PNG + SVG
Resolution
4K
Support
Priority
For labs and teams with high-volume needs.
Labs, teams, and higher-volume workflows.
Billed $478.80/yr
1,500 credits
Credits
1,500
Exports
PNG + SVG + PDF
Resolution
4K + batch
Support
Dedicated
Start with the plan that feels right for now. You can cancel anytime before your next billing cycle.
Research feedback
The useful signal is not decoration. It is whether a lab can discuss the figure structure before final production.
18+
active labs and research groups
46 sec
median time to first draft
4.8/5
reviewer clarity score
A structured first draft appears quickly enough that students can focus on revision quality instead of rebuilding the figure from scratch.
Prof. Lin Qian
Professor of Biomedical Engineering
North River University
The hierarchy is clearer from the start, so our review rounds move faster before final publication polishing begins.
Dr. Helena Zhou
Senior Research Scientist
Institute for Intelligent Materials
Dense workflows become concise visuals that collaborators understand immediately, especially for talks, grants, and proposal reviews.
Dr. Marcus Tan
Research Fellow
Center for Applied Systems Research
Examples
Review output directions before starting your own PaperBanana workflow.
Quantitative Research
Combines a distribution bar chart with a hypotheses-versus-outcomes heatmap so readers can scan evidence balance and study alignment in one figure.
Generative Workflow
Maps how drawings, constraints, and text feed parameter sampling, orthographic rendering, and final 3D model generation across a staged workflow.
Model Architecture
Places Post-LN, Pre-LN, Peri-LN, and nGPT blocks side by side so structural differences are legible without dense paragraph explanation.
Business Reporting
Pairs a sales treemap with a satisfaction pie chart to show how commercial data can be summarized in a clean two-panel reporting layout.
For researchers
Faster figure preparation, clearer review cycles, and drafts that remain useful after generation.
A structured first draft replaces the slowest part of figure creation: arranging boxes, labels, arrows, and chart hierarchy from a blank canvas.
Teams can critique the story, ordering, and missing evidence earlier because the visual hierarchy is already visible in the first pass.
Chart-oriented workflows aim to preserve the numbers, labels, and emphasis you provide so visual polish does not come at the cost of accuracy.
The same workflow can be tuned for journal figures, grant decks, posters, and teaching visuals without switching tools mid-process.
Researchers can start with an individual plan and move to higher-volume tiers when a lab needs more throughput, richer exports, or team support.
Multi-pass refinement helps tighten annotation clarity, panel ordering, and figure readability before the asset reaches final production.
Explore focused landing pages for the main brand terms and research figure generation searches.
PaperBanana
PaperBanana combines source grounding, panel planning, restrained academic styling, and review-oriented output in one workflow.
Read guidePaperVizAgent
PaperVizAgent makes figure generation more inspectable by splitting the process into source reading, planning, styling, rendering, and review.
Read guideQuality metrics
The workflow checks structure fidelity, numerical consistency, visual polish, and information density.
Semantic fidelity
92/100
Measures how closely the figure structure follows the provided research brief and intended message.
Numerical precision
99.2%
Tracks how consistently chart outputs preserve supplied values and reduce silent visual drift.
Visual polish
4.8/5
Reflects spacing, annotation discipline, and overall figure presentation quality during review.
Information compression
3.1x
Average reduction in explanatory burden when dense methods content is translated into a concise visual summary.
Why it works
Move from research context to a figure worth reviewing without starting from an empty canvas.
Start from source material, not prompt guesswork
ipaperbanana treats your notes, captions, and figure intent as the brief. That keeps panel structure, labels, and sequencing closer to the actual study instead of forcing repeated prompt rewrites.
Get to a reviewable first draft faster
The workflow separates grounding, planning, rendering, and QA into distinct steps so the first output is easier to critique in lab review, advisor feedback, or manuscript iteration.
Keep the output usable after generation
The goal is not novelty for its own sake. Drafts are shaped for export and downstream editing, so teams can keep polishing the figure instead of recreating it from scratch.
FAQ
Plans, exports, data handling, PaperVizAgent, and publication use.
PaperVizAgent is the visualization agent behind PaperBanana. It turns source context into a planned academic figure draft by separating reading, layout planning, rendering, and review.
Start a draft
Create a draft, compare plans, and choose the throughput and export depth that match your research workload.