paper-orchestra (Orchestrator)

The workspace MUST contain:

scripts/init_workspace.py will scaffold this layout. scripts/validate_inputs.py will check it before the pipeline runs.

Pipeline (read references/pipeline.md for the full diagram)

Step 1: Outline           ──▶  outline.json                       (1 LLM call)
Step 2: Plotting     ─┐
                      ├──▶  figures/*.png + captions.json         (~20-30 calls)
Step 3: Lit Review   ─┘                                           (~20-30 calls)
                          intro_relwork.tex + refs.bib

Step 4: Section Writing  ──▶  drafts/paper.tex                    (1 LLM call)
Step 5: Content Refine   ──▶  final/paper.tex + final/paper.pdf   (~5-7 calls, ~3 iters)

Step 2 and Step 3 are independent and MUST run in parallel when your host supports parallel sub-agents. If not, run Step 3 first (it has the longer wall time due to Semantic Scholar rate limits) and Step 2 second.

Critical pre-instruction (read once, apply always)

Before any LLM call that writes paper content (outline, intro/related work, section writing, refinement), you MUST prepend the Anti-Leakage Prompt at references/anti-leakage-prompt.md to your system prompt. This is verbatim from Appendix D.4 of the paper and prevents pre-training-data leakage. The paper applies it uniformly across all baselines for fair comparison; we apply it for fidelity and to keep generated papers grounded in the user's inputs.

Step-by-step execution

0. Scaffold, check for missing inputs, and validate

python skills/paper-orchestra/scripts/init_workspace.py --out workspace/
python skills/paper-orchestra/scripts/validate_inputs.py --workspace workspace/

Before failing on missing inputs, check whether aggregation can supply them:

If validation still fails after aggregation (e.g. template.tex or conference_guidelines.md are missing), stop and tell the user exactly which files remain outstanding.

Also probe the TeX installation (once per workspace, result cached):

python skills/paper-orchestra/scripts/check_tex_packages.py \
    --out workspace/tex_profile.json

The Section Writing Agent reads tex_profile.json to decide which LaTeX patterns to use (e.g., Figure~\ref{} vs \cref{}, whether to include \usepackage{microtype}, etc.). This eliminates compile-time package failures that previously required iterative manual edits.

1. Outline (Step 1 — 1 LLM call)

Load skills/outline-agent/SKILL.md and follow it. Output: workspace/outline.json. Validate with python skills/outline-agent/scripts/validate_outline.py workspace/outline.json. Halt the pipeline if validation fails — every downstream agent depends on the schema.

2 ∥ 3. Plotting and Literature Review (in parallel)

Parse outline.json. Extract:

• outline.plotting_plan → drives Step 2
• outline.intro_related_work_plan → drives Step 3

If your host supports parallel sub-agents (Claude Code's Agent tool with multiple concurrent calls; Cursor's parallel agents; Antigravity's worker pool), spawn two concurrent sub-tasks:

• Sub-task A: load skills/plotting-agent/SKILL.md, execute the plotting plan, produce workspace/figures/<figure_id>.png for every entry, plus workspace/figures/captions.json.
• Sub-task B: load skills/literature-review-agent/SKILL.md, execute the research strategy, produce workspace/drafts/intro_relwork.tex and workspace/refs.bib.

If your host does not support parallel sub-agents, run Sub-task B first (it has slower wall-clock due to Semantic Scholar QPS limits) then Sub-task A. The artifacts are independent, so order doesn't affect correctness.

4. Section Writing (Step 4 — ONE single multimodal LLM call)

Load skills/section-writing-agent/SKILL.md and follow it. This is one single call in the paper (App. B: "Section Writing Agent (1 call)") — do not split it per section. The agent receives:

• outline.json
• idea.md, experimental_log.md
• intro_relwork.tex (already-filled from Step 3 — preserve verbatim)
• refs.bib (the citation map)
• conference_guidelines.md
• The actual figure image files from workspace/figures/ (multimodal input)

Output: workspace/drafts/paper.tex (a complete LaTeX document).

Then run the deterministic gates:

python skills/section-writing-agent/scripts/orphan_cite_gate.py workspace/drafts/paper.tex workspace/refs.bib
python skills/section-writing-agent/scripts/latex_sanity.py workspace/drafts/paper.tex
python skills/paper-orchestra/scripts/anti_leakage_check.py workspace/drafts/paper.tex

If any gate fails, the host agent must fix the issue (re-prompting the writing step with the gate's error report) before proceeding.

5. Content Refinement (Step 5 — ~3 iterations, ~5-7 calls)

Load skills/content-refinement-agent/SKILL.md and follow it. The skill implements the loop with strict halt rules from halt-rules.md. Maintain workspace/refinement/worklog.json and snapshot each iteration into workspace/refinement/iter<N>/.

Halt conditions (any one triggers the loop to stop and accept the current best snapshot):

1. Iteration count reaches the cap (default 3, see halt-rules.md).
2. Overall score from the simulated reviewer decreases vs the previous iteration → revert to previous snapshot, halt.
3. Overall score ties but at least one sub-axis decreases while none gain compensatingly (negative net sub-axis change) → revert, halt.
4. Reviewer issues no new actionable weaknesses.

The accepted snapshot is copied to workspace/final/paper.tex.

6. Compile and finalize

cd workspace/final && latexmk -pdf paper.tex

Then write workspace/provenance.json capturing input file hashes, outline hash, refs hash, figure hashes, and final tex/pdf hashes (helper: scripts/snapshot.py in the orchestrator scripts dir if you want a one-shot; otherwise the host agent computes hashes inline).

Report to the user: the path to workspace/final/paper.pdf, a brief summary of which sections were drafted, citation count, refinement iterations completed, and any gates that failed mid-pipeline.

Workspace layout

See references/io-contract.md. Summary:

workspace/
├── inputs/                          # user-provided
│   ├── idea.md
│   ├── experimental_log.md
│   ├── template.tex
│   ├── conference_guidelines.md
│   └── figures/                     # optional pre-existing figures
├── outline.json                     # Step 1 output
├── figures/                         # Step 2 output
│   ├── <figure_id>.png
│   └── captions.json
├── refs.bib                         # Step 3 output
├── drafts/                          # Step 3 + Step 4 output
│   ├── intro_relwork.tex
│   └── paper.tex
├── refinement/                      # Step 5 working dir
│   ├── worklog.json
│   ├── iter1/
│   ├── iter2/
│   └── iter3/
├── final/                           # accepted snapshot + compiled PDF
│   ├── paper.tex
│   └── paper.pdf
└── provenance.json                  # input/output hashes for reproducibility

Cost budget (from paper App. B)

Total: ~60–70 LLM calls per paper, ~40 minutes wall-time on the paper's setup. Budget breakdown:

Host integration

See references/host-integration.md for per-host invocation details (Claude Code, Cursor, Antigravity, Cline, Aider, OpenCode).

Resources

• references/pipeline.md — full step-by-step flow + parallelism rules + halt rules
• references/io-contract.md — workspace layout, input file schemas
• references/anti-leakage-prompt.md — verbatim from App. D.4, prepend to every writing call
• references/paper-summary.md — 1-page distillation of arXiv:2604.05018
• references/host-integration.md — per-host invocation guide
• scripts/init_workspace.py — scaffold workspace dir tree
• scripts/validate_inputs.py — verify (I, E, T, G) before running
• scripts/anti_leakage_check.py — grep draft for leaked author names/emails/affils