Overview

MathModelAgent’s writing stage is where computational evidence can be preserved or strengthened without support. Data figures and conceptual diagrams serve different claims; reusable templates provide form but not data provenance. The retained backend assembles Markdown, references, and images, while the newer writing path uses contest-specific Typst and LaTeX contracts. Writing gates and compilation catch structural, path, citation, and layout defects, but a document can still contain target drift, unsupported captions, weak citations, or invalid evaluation. Final semantic review remains necessary.

Learning outcomes

A figure’s form does not transfer the provenance of its example data; current values, labels, captions, and interpretation must come from the problem.
Compilation and writing checks catch enumerable document defects, not whether a caption, citation, metric, or model claim is scientifically sound.
The final document—not an isolated Writer response—is the unit that must preserve section order, references, paths, numbers, and evidence.

Keywords

MathModelAgentfigurestemplatesprovenancecitationsverification

From Result to Paper: Figures, Templates, and Verification

For MathModelAgent, the paper is the last place where computational evidence can be preserved—or quietly transformed into a stronger claim. This chapter follows the path from results and images to figures, references, sections, templates, and acceptance checks. The practical judgment is whether a finished document is merely formatted or whether its numbers, visuals, and citations remain connected to the work that produced them.

A paper makes heterogeneous material look uniform. A table, a chart, a conceptual diagram, a paragraph, and a reference can sit on the same page, even though they support different kinds of statements. MathModelAgent’s writing design is most useful when it keeps those differences visible. The data work must explain the data. The diagram must explain structure. The Writer must connect both to a claim without inventing what the earlier stages did not establish.

Visual form should match the claim

A data figure and a conceptual diagram answer different questions. A trend line, error distribution, ranking chart, correlation view, or sensitivity curve communicates a pattern in measured or computed values. Its axes, legend, units, data source, and caption determine what the reader may infer. A roadmap, processing pipeline, model structure, or decision flow communicates relationships among stages, variables, or choices. It may clarify how a method works, but it does not establish that the method produced a particular value.

The repository assigns those responsibilities to different stages. Coding and visualization produces data-driven charts from actual results. DrawIO handles non-data visuals such as technical routes, solving flows, model structures, and data-processing pipelines. The separation is not just a division of labor. It prevents a conceptual picture from being mistaken for empirical evidence and prevents a chart from being burdened with explaining architecture it cannot show.

The figure-template skill extends this idea with reusable plotting recipes and multiple export formats. Reuse can save time and improve visual consistency, especially when a paper needs repeated chart types. But a template does not transfer the provenance of its example data. The skill explicitly warns that bundled simulated data must not be presented as reproducing a source study exactly. A familiar visual form can be reused; the values, labels, and interpretation must still come from the current problem.

This distinction affects captions and surrounding prose. A caption should tell the reader what the figure represents, while the paragraph should explain why that representation matters for the model. “Figure 3 shows the result” is not enough. A stronger explanation identifies the comparison or pattern the reader should inspect and ties it to the relevant objective, error measure, constraint, or sensitivity question. The figure should make the argument clearer, not carry the argument alone.

Two document paths express two levels of maturity

The retained backend follows a practical Markdown assembly path. The Writer receives code responses, interpreter output, and image names. It can call an OpenAlex-backed paper-search tool, return section content and footnotes, and hand those sections to an output object that orders them, deduplicates references by content, assigns numbers, and writes JSON and Markdown. After a successful task, the router can convert the Markdown result to DOCX. This path is concrete and useful for turning a long run into a readable artifact.

The newer writing skill describes a richer template-oriented contract.

Published by DeepMox

FAQ

Why separate data figures from conceptual diagrams in MathModelAgent?

Data figures communicate patterns in measured or computed values, while conceptual diagrams explain relationships among stages, variables, or choices.

What can MathModelAgent’s writing checks detect?

MathModelAgent’s writing checks can detect missing sections or files, placeholders, broken image paths, some reference and metric problems, and other structural defects; they do not certify the mathematics.

Why can a compiled modeling paper still be wrong?

A compiled modeling paper can still be wrong if its target drifts, captions overstate figures, citations do not support claims, or numbers come from an invalid evaluation design.

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MathModelAgent: From Results to Paper

From Result to Paper: Figures, Templates, and Verification

Visual form should match the claim

Two document paths express two levels of maturity

FAQ