section-writing-agent

name

section-writing-agent

description

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".

Section Writing Agent (Step 4)

Faithful implementation of the Section Writing Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 4, App. F.1 pp. 47–49).

Cost: ONE LLM call (App. B: "Section Writing Agent (1 call): A single, comprehensive multimodal call to draft and compile the complete LaTeX manuscript"). Do NOT split this into per-section calls — the paper explicitly designs it as one comprehensive call so the model can maintain global coherence across sections.

Inputs

• workspace/outline.json — the master plan
• workspace/inputs/idea.md — technical details
• workspace/inputs/experimental_log.md — raw data for tables and qualitative analysis
• workspace/drafts/intro_relwork.tex — the template with Intro + Related Work already filled in by Step 3. This is your starting point. The preamble, package list, style, and the two pre-filled sections must be preserved verbatim.
• workspace/citation_pool.json — the citation map ({key, title, abstract} for each verified paper)
• workspace/refs.bib — the BibTeX file
• workspace/inputs/conference_guidelines.md — formatting rules
• workspace/figures/ — the actual PNG files from Step 2 (used as multimodal vision input!)
• workspace/figures/captions.json — caption text per figure_id
• workspace/tex_profile.json — TeX package availability flags (written by check_tex_packages.py at Step 0). Read this before generating any LaTeX. It tells you which packages are installed so you select the right cross-reference pattern, font packages, etc. before you write — not after you try to compile.

Output

• workspace/drafts/paper.tex — the complete LaTeX paper, with all sections filled. The Step 5 Refinement Agent will iterate on this file.

How to do it

0.5. Read tex_profile.json and select LaTeX patterns

Before composing the prompt, read workspace/tex_profile.json and apply these rules to every LaTeX choice in the generated paper:

Profile flag	True → use	False → use instead
use_cleveref	\cref{fig:X}, \cref{tab:Y}	Figure~\ref{fig:X}, Table~\ref{tab:Y}
use_nicefrac	\nicefrac{a}{b}	$a/b$
use_microtype	\usepackage{microtype}	omit the line
use_t1_fontenc	\usepackage[T1]{fontenc}	omit the line

If tex_profile.json does not exist (old workspace), default to the safe fallback column (no cleveref, no nicefrac, no microtype, no T1 fontenc).

1. Pre-extract metrics from the experimental log

Run the deterministic helper:

python skills/section-writing-agent/scripts/extract_metrics.py \
    --log workspace/inputs/experimental_log.md \
    --out workspace/metrics.json

This parses the ## 2. Raw Numeric Data section's markdown tables into structured JSON. The Section Writing Agent uses this to construct LaTeX booktabs tables without re-deriving values from raw text. Read references/latex-table-patterns.md for the booktabs conventions.

2. Compose the prompt and make ONE multimodal call

Load references/prompt.md (verbatim Section Writing Agent prompt from App. F.1). Prepend the Anti-Leakage Prompt from ../paper-orchestra/references/anti-leakage-prompt.md.

The user message contains:

• outline.json — full content
• idea.md — full content
• experimental_log.md — full content (tables AND prose)
• intro_relwork.tex — full content (this becomes template.tex for the prompt)
• citation_pool.json — full content (becomes citation_map.json)
• conference_guidelines.md — full content
• figures_list — array of {figure_id, filename, caption} from captions.json and the file listing
• The actual figure PNGs as multimodal image inputs, so the model can visually inspect them and write accurate descriptions / refer to them correctly in the prose.

If your host LLM has no vision input, fall back to text-only mode: pass the captions in captions.json as descriptions and tell the agent it cannot see the images directly. Quality drops noticeably (the paper notes that visual grounding measurably improves figure-text alignment), but the pipeline still completes.

3. Save the output

The agent's response is wrapped in \``latex ... ```fences. Extract the LaTeX code and save toworkspace/drafts/paper.tex`.

4. Run the deterministic gates

# Orphan citation gate: every \cite{KEY} must exist in refs.bib
python skills/section-writing-agent/scripts/orphan_cite_gate.py \
    workspace/drafts/paper.tex workspace/refs.bib

# Latex sanity: matched braces, matched begin/end, no unescaped specials
python skills/section-writing-agent/scripts/latex_sanity.py \
    workspace/drafts/paper.tex

# Anti-leakage post-check: no author names, emails, affiliations
python skills/paper-orchestra/scripts/anti_leakage_check.py \
    workspace/drafts/paper.tex

If any gate fails, re-prompt the writing call with the gate's error report appended to the user message and ask the agent to fix the specific issues. Do NOT try to fix the gate violations by hand — the model needs to see its own mistakes.

Critical rules from the prompt

These are excerpted from references/prompt.md (App. F.1, pp. 47-49). The host agent MUST honor them on the writing call:

Existing-content preservation

• DO NOT modify the text, style, or content of sections that are already filled in intro_relwork.tex. Preserve Intro + Related Work verbatim.
• Keep the preamble (packages, document class, style) exactly as is.
• Come up with a good title if one is missing. Fill author names if missing (but the Anti-Leakage Prompt says not to invent real ones — use a placeholder like "Anonymous Authors" for double-blind).

Data and tables

• Build LaTeX tables for the experimental results.
• Extract numeric values directly from experimental_log.md. Do not hallucinate numbers — use the exact values in the log.
• Use the booktabs package format: \toprule, \midrule, \bottomrule.
• All tables must appear before the Conclusion section, unless they are explicitly placed in an Appendix.

Citations

• The outline.json provides citation_hints per subsection. For each hint, find the matching key in citation_pool.json (by title or content) and use that exact key in \cite{...}.
• Use ONLY keys from refs.bib. Inventing or guessing keys violates the Lit Review Agent's verified pool.
• Read the abstract from citation_pool.json for the papers you cite. Use the abstract context to write specific, accurate sentences about those works — not generic "[A, B] proposed methods for X".

Writing content

• Write the missing sections following outline.json's section_plan structure exactly. Hierarchy rule: if 4.1 exists, 4.2 must exist.
• Use formal mathematical equations, notations, and definitions where appropriate AND directly supported by idea.md or experimental_log.md. Do not hallucinate math. Do not use complex math just for the sake of it.
• Always provide detailed ablation studies and qualitative analysis of the experimental results: what worked, what does not, and why.
• Optional: discuss limitations and future work at the end.
• If you put anything in the Appendix, the Appendix section appears AFTER the References section, on a fresh new page.

Figures and visual fidelity

• You are being given the actual image files of the figures. You MUST describe them faithfully and accurately. Do NOT hallucinate interpretations that contradict the visual evidence in the plots.
• Use ALL of the figures provided in figures/. Use the exact filenames including extensions (e.g., .png) in your \includegraphics commands.
• DO NOT merge or group multiple figures into one display.
• If the paper is in a 2-column format, prefer single-column figures (\begin{figure}) unless they are very wide.
• All figures must appear before the Conclusion section, unless explicitly in the Appendix.
• Refine the captions if necessary, but they are already provided in captions.json and should generally be used as-is.
• Do NOT include "Figure X" in the caption text — LaTeX handles numbering.

Style

• Adopt the tone of a top-tier ML conference paper: dense, objective, technical.
• Match the indentation and spacing style of the original template.tex. Do not change the overall LaTeX style.

LaTeX integrity

• The output must compile flawlessly out-of-the-box.
• All \begin{X} must match a \end{X} (e.g., \begin{figure*} must be closed with \end{figure*}, not \end{figure}).
• DO NOT change \usepackage[capitalize]{cleveref} to \usepackage[capitalize]{cleverref} — there is no cleverref.sty.
• Always emit \clearpage immediately before \bibliographystyle{...}. Without it, figures deferred by LaTeX's float algorithm will appear inside or after the References section — a hard-to-spot layout defect that only shows up in the compiled PDF. \clearpage forces all pending floats to be output before the bibliography starts. See references/latex-table-patterns.md for details.
• Cross-references: prefer Figure~\ref{fig:X} and Table~\ref{tab:Y} over bare \ref{fig:X}. This is necessary when cleveref is unavailable and produces readable prose in all cases. Use \cref{...} only when cleveref.sty is confirmed present.

Output format

• Wrap the full updated template.tex in \``latex ... ````.
• The previously empty sections should now be filled.
• Previously filled sections (Intro, Related Work) should remain mostly untouched; only adjust for consistency purposes.

Resources

• references/prompt.md — verbatim Section Writing Agent prompt from App. F.1
• references/latex-table-patterns.md — booktabs rules + table-from-log examples
• references/figure-integration.md — \includegraphics, 2-column handling, placement
• scripts/extract_metrics.py — markdown tables in experimental_log → JSON
• scripts/latex_sanity.py — unmatched braces, env mismatches, specials
• scripts/orphan_cite_gate.py — every \cite{KEY} exists in refs.bib