/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`.

Then append the **craft constraints** for the sections being drafted. Pull them from `skills/shared/section_rhetoric.md` — the global rules plus only the templates for Abstract, Method, Experiments, and Conclusion (Intro and Related Work are already written by Step 3; do not re-open them). The App. F.1 prompt specifies what each section must *contain*; the rhetoric templates specify the paragraph roles and their order. Without them the model produces content-complete sections whose paragraphs all make the same kind of move, which the Step 5 reviewer scores down on Logical Flow.

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 to `workspace/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