paper-writing

name

paper-writing

description

Outline-and-story-first academic paper writing with hard anti-hallucination gates. Drafts a markdown bullet outline plus a first-time-reader story, reviews it with a context-isolated agent, then converts to LaTeX following a user-provided conference template. Verifies every citation against Crossref/DBLP/Semantic Scholar before it enters the paper, blocks invented jargon and fabricated numbers, and calls natural-writing for prose. Human in the loop with confirmation stops; not autonomous. English and Japanese. Use for 論文, conference submissions, related work, rebuttals, and research-based slides.

Paper Writing

Build a bullet-point outline and a first-time-reader story first, confirm at each step, then turn it into LaTeX for a user-provided conference template. The overriding goal is to prevent fabricated citations and invented jargon.

What this is, and is not

• Human in the loop. Each phase ends with a stop: present the result and wait for the user. Do not run research-to-final autonomously.
• If asked only to explain or to check a design, do that and nothing else. Do not jump ahead and start writing prose.
• Always apply the natural-writing skill to any prose this skill produces (it owns the generic anti-LLM checklist for English and Japanese). Use deep-research for source gathering. Run the heaviest proofreading pass on Opus (claude-opus-4-8).
• This is not an AI humanizer or detector-evasion tool. See reference/anti-hallucination.md.

Material Passport (resumable state)

One paper = one directory, one git worktree. Never split one paper across numbered directories. Keep passport.yaml as an append-only ledger linking: research plan → citation candidate pool → paragraph specs → per-phase confirmation state → verification results. This lets a session be interrupted, resumed, or entered mid-pipeline. Schema: schemas/outline.schema.json and schemas/citation.schema.json.

Workflow

Each phase can be invoked on its own. Finish a phase, then stop for confirmation.

1. Research 2. Story / outline 3. Paragraph specs
2. Draft / LaTeX 5. Citation gate 6. Proofread

Phase 1 — Research

Call deep-research to map the topic, prior work, and a citation candidate pool. Every pool entry carries a stable id (DOI / arXiv / PMID / DBLP) and API-derived canonical metadata. Knowledge Isolation Directive: factual content — claims, citations, data, methods — comes only from session materials. If the outline needs something the materials do not cover, write [MATERIAL GAP] rather than filling it from memory. The writing skill is unrestricted; only factual content is restricted. → Stop: confirm the pool and the research summary.

Phase 2 — Story / outline

Write the whole structure as a markdown bullet list plus a first-time-reader story, at coarse granularity (roughly "what goes in each section"). See reference/story-outline.md (Context-Content- Conclusion, write-backwards order). Then spawn a context-isolated first-reader agent (a fresh agent that does NOT get this conversation's context) to audit the flow of questions and flag undefined terms — see reference/context-isolation.md. Tell the user before launching that agent. → Stop: confirm structure, story, and the first-reader flags.

Phase 3 — Paragraph specs

For each paragraph, write one line stating what it says and which pooled citation supports it (templates/paragraph-spec.md). Do not write prose yet. → Stop: confirm the specs.

Phase 4 — Draft / LaTeX

Draft from the specs. For synthesis-heavy sections (related work, discussion), prefer edit-not-write: the human drafts, and this skill adds inline comments on weak spots instead of replacing the prose, to keep the author's voice. Default to results-first / write-backwards order: conclusion → results → methods → discussion → intro → abstract → title.

Fill the user's conference template section by section and run a compile/lint loop. Papers go to LaTeX→PDF; slides hand off to the hayao-slides repo. See reference/latex-templating.md.

Long-output rule (required): write long body text directly to files with Write/Edit; do not dump full drafts into chat — show only a one-paragraph summary. This avoids losing a session to the output-token cap. When drafting in parallel, isolate agents in separate git worktrees so they do not overwrite each other. → Stop: confirm each section (show the diff plus flags only).

Phase 5 — Citation gate (most important)

In-text citations may use only ids from the candidate pool. Anything without a source becomes a [CITATION NEEDED] placeholder. Run scripts/verify_citations.py to check every citation; fall back to the manual curl procedure in reference/citation-gate.md when scripts can't run.

• Existence: match against Crossref → DBLP → Semantic Scholar → OpenAlex → arXiv. Never use Google Scholar as an authority (discovery only).
• A resolving DOI is not enough: match title + authors + year + venue + pages together (most fabricated DOIs resolve to an unrelated real paper).
• Faithfulness: confirm the cited work actually supports the claim. Attach a quote (≤25 words) and a locator. Reject post-hoc citations.
• Authors and page numbers must be checked explicitly (a frequent failure).
• BibTeX is fetched from an API, never written by the model (scripts/fetch_bibtex.py; DBLP .bib or Crossref content negotiation).
• Distributional skew: warn if ≥70% of cited sources share one year/region/method/venue.

Unverifiable citations leave the body and are surfaced as UNVERIFIED. → Stop: confirm the verified/rejected summary table.

Phase 6 — Proofread

Apply natural-writing, then run a multi-dimension review with worktree-isolated agents, each judged independently:

• Logic / argumentation (reference/argumentation.md): paragraph = one step of the argument; causal claims state the mechanism in one sentence; no lumping distinct causes; examples actually support the claim; forward references are paid off; never end on a concession; define terms before use.
• Naturalness (reference/naturalness-paper.md): excess-vocabulary scan via scripts/check_prose.py (EN/JP, with a discipline-exception clause — keep field-standard terms; flags are priors, not proof), related work as synthesis not a list, topic-sentence test, per-section burstiness (Methods may be uniform, Discussion most varied), hedging ladder calibrated to evidence.
• Field-aware (reference/field-aware.md): CS/security (explicit threat model, motivation↔artifact, falsifiable contributions), ML, biomedical, humanities.
• Citation consistency: \cite ↔ .bib, no leftover UNVERIFIED.
• LaTeX layout / slides: floats, references, cross-deck duplication.

Then run an adversarial review of the central claim (Reviewer 2 / red-team) in a separate agent. If draft↔critique do not converge in N rounds, escalate to the user. Run the heaviest hallucination-checklist pass over the full text on Opus. Treat smooth, well-formatted prose as a risk signal — verify every claim's grounding. → Stop: present the review and required fixes.

Hard rules (absolute)

See reference/anti-hallucination.md for the full rationale. Never relax these:

• No citation from memory in output. Pool-derived and verified only.
• No invented jargon. A technical term is either attestable in existing literature (citable) or introduced by this paper with a definition and a novelty flag. Otherwise ask the user.
• No fabricated statistics, numbers, datasets, equations, or tools. Unknowns become [UNVERIFIED].
• Do not rely on prompt-level "don't fabricate" instructions — always go through external verification and human review.
• No detector-evasion. Optimize for genuine quality, not for "looking human".

Files

• reference/ — story-outline, context-isolation, citation-gate, argumentation, naturalness-paper, field-aware, latex-templating, anti-hallucination
• schemas/ — outline.schema.json, citation.schema.json
• scripts/ — verify_citations.py, fetch_bibtex.py, check_prose.py
• templates/ — paragraph-spec.md
• passport.yaml — runtime state (gitignored)