Draft Related Work

Draft Related Work

Produces a Related Work section that positions the paper — every paragraph is a cluster of actually-retrieved prior work plus an explicit delta sentence — formatted to the target venue's conventions. This is a writing skill with two hard gates:

• Anti-hallucination. A citation that was not retrieved and verified in this session does not ship.
• Anti-structural-hole. The clusters the section MUST cover are derived from the paper's own claimed scope, never from whatever find-papers happened to return. A retrieved-but-peripheral corpus produces an outline that positions confidently while leaving a hole exactly where the paper lives — a missing direct-competitor cluster, an absent canonical lineage, a whole expected sub-area with zero citations. That gap is found deterministically and routed back to find-papers as a targeted second pass, not shipped as a silent author to-do.

When to use

• Drafting Related Work for a new paper, given a topic and a target venue.
• Rewriting an existing section reviewers called a "laundry list", "thin", "missing comparisons", or "unclear novelty".
• Folding newly found papers (or a reviewer's "you missed X, Y, Z") into an existing section without breaking its structure.
• NOT for finding papers (use find-papers), reading one paper (fetch-paper), a full thematic survey (literature-review), or checking an existing bibliography only (verify-citations).

Inputs

• The paper's core claim/contributions, AND its sub-tasks/requirements (from the draft's intro/abstract, or ask). These drive the required-cluster enumeration in step 2 — without them the section can only mirror the retrieved pile.
• Target venue — a venues/conferences/<id>.yml profile if one exists (its exemplar_distribution calibrates the section's length/breadth).
• Candidate prior work: output of find-papers / literature-review, the draft's .bib, and/or papers the user names.
• CONTACT_EMAIL env var for the metadata script (prompts if unset).

Process

1. Pin down the venue's conventions. Read venues/conferences/<id>.yml (fields: family, format.template, review.blind, track page limits) and the family file it references. Profiles are a starting point, never ground truth — re-verify anything that affects the draft (template, blind level, page budget) against the live cfp_url before relying on it. Then read references/placement-conventions.md for where the section goes, how long it runs, citation-command style, and self-citation rules at that venue family. When conventions are unclear, confirm empirically: pull 3–5 recent papers from the venue itself (find-papers DBLP toc query, study-exemplars) and observe placement.

2. Derive the REQUIRED clusters from the paper's claimed scope — before looking at what was retrieved. Enumerate the paper's contributions, sub-tasks, and requirements from the brief/intro. For each, name the cluster of direct prior approaches a reviewer would expect to see positioned against, plus the foundational-lineage / canonical anchor that sub-area is built on (the originating method, not only the latest refinement). This list is the target; it does not yet have citations. Driving it from claimed scope (not the corpus) is what makes it generalize across papers and venues, and is what surfaces the hole the retrieved pile hides. Method and worked examples: references/clustering-and-deltas.md (§ Required clusters from claimed scope).

3. Assemble the candidate pool — retrieved papers only. Sources: the user's .bib, find-papers results, the references and citations of the 1–2 closest known papers (find-papers --paper style lookups). Pull deliberately toward the required clusters from step 2, not just whatever a topic search returned. Target 10–25 candidates. A paper enters the pool only with a concrete identifier (DOI or arXiv ID). If the user names a paper without one, find it first; if it cannot be found, say so — never proceed on a guessed reference.

4. Build the clustering worksheet. Run:

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   python3 scripts/gather_candidates.py <DOI> <arXiv-ID> ... [--from-file ids.txt]
   

   It fetches each paper's metadata one polite request at a time (title, year, venue, citation count, abstract, tldr) and prints a worksheet with empty cluster: / delta: slots. Treat the output as transient working material — it contains abstracts; never commit it. Identifiers it flags as unresolved (exit 3) are unverified: park them until cleared. Where the abstract is missing or the paper is pivotal, read it via fetch-paper.

5. Map the pool onto the required clusters and run the coverage gate. Assign each retrieved paper to one of the step-2 required clusters (a paper that fits none is an outlier — keep only if reviewer-expected). Write the assignment into a small plan file (one block per required cluster, listing the cite keys assigned). Tag each key by evidence tier so precision stays visible and the floor cannot be met by padding — append !graph / !keyword / !heuristic to a key the paper is not confirmed to cite (an untagged key means "the paper is known to cite this"):

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   "refs": ["li2018deep", "yao2019computing",          # confirmed-cited
            "smith2023!graph",   # surfaced by citation-graph edge — plausible
            "doe2022!heuristic"] # 'a strong paper would cite this' — weakest
   

   Then run:

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   python3 scripts/check_coverage.py plan.json   # or plan.txt (see --help)
   

   Only confirmed-cited refs count toward the floor. The gate FAILs (exit 3) on any required cluster with zero confirmed cites — including one "covered" only by speculative refs (padding masks a real hole) — and WARNs below the floor (default 2). It also prints a precision estimate (confirmed / total) and caps heuristic-only additions (default 2 across the plan, --heuristic-cap) so the core set stays scope-justified. On a failing or thin cluster, take its emitted second-pass retrieval worklist back to find-papers and fill the gap with confirmed cites, then re-run — do NOT draft over an empty required cluster, satisfy the floor by padding with speculative refs, or ship a gap as a silent author to-do. Only when the gate clears (or the user explicitly accepts a documented thin cluster) proceed to clustering prose.

6. Cluster and articulate the delta per cluster. With coverage cleared, group the pool into 3–6 themes along the axis that makes this paper's gap visible, then write one delta sentence per cluster: what the cluster achieves, what it lacks for this paper's problem, what this paper does about it. Method, patterns, and anti-patterns: references/clustering-and-deltas.md. Show the user the cluster plan (cluster names, members, delta sentences) before writing prose — restructuring is cheap now, expensive later.

7. Draft the section. One paragraph per cluster (claim sentence → representative works → limitation → delta), a dedicated paragraph for the single closest competitor, and a closing positioning paragraph. Match the venue: citation commands and self-citation voice per step 1; calibrate length and breadth to the venue family's measured exemplar median, not to maximal coverage — read the profile's exemplar_distribution (related_work band when present) or measure 3–5 recent venue papers via study-exemplars, falling back to the family norm in references/placement-conventions.md only when neither exists. Emit .tex (or markdown if the draft is not LaTeX) plus BibTeX entries for any citation not already in the user's .bib — entries built strictly from retrieved metadata. Add a comparison table only when the criteria in the clustering reference are met.

8. Audit deterministically. Run:

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   python3 scripts/audit_bib.py refs.bib --tex related-work.tex
   

   Fix every blocking finding: cite keys missing from the .bib, duplicate entries, entries with no DOI/eprint/URL (unverifiable — the classic hallucinated-reference shape), incomplete entries. The style census in its output must match the venue's convention.

9. Gate through verify-citations. Every entry cited by the new section gets verified against Crossref/DBLP/S2 before delivery. Anything that fails verification is removed from the prose or explicitly flagged to the user as unconfirmed — never left in silently, and never "fixed" by inventing plausible fields.

Output

• The drafted/rewritten Related Work section (.tex or markdown), clustered, with a delta per cluster, in the venue's citation style.
• New BibTeX entries (metadata only — always safe to keep).
• A short positioning summary: the required clusters derived from scope and whether each cleared the coverage gate, per-cluster delta, the closest competitor and the precise distinction, plus anything left unverified.
• The coverage gate's precision estimate (confirmed-cited / total refs) and any speculative additions (graph/keyword/heuristic tier) called out explicitly as plausible-but-unconfirmed, so the user can prune them before submission rather than treating cluster-floor satisfaction as proof of citation. When the ground truth is a known subset, report recall against that subset separately from this precision band.
• If any required cluster could not be filled, the second-pass retrieval worklist (for find-papers) reported explicitly — never buried as a silent author to-do.

Guardrails

• Never fabricate, embellish, or "reconstruct from memory" a citation; every reference must trace to a retrieval in this session and pass verify-citations. A thin-but-true section beats a padded one.
• The required clusters come from the paper's claimed scope, never from the retrieved corpus alone — positioning only against what happened to be returned leaves a hole where the paper actually lives. A required cluster with zero citations is a blocking gap routed back to find-papers, never a silent author to-do, and never papered over by stretching an adjacent cluster to cover it.
• This is a copilot, not an autopilot: the coverage gate, the second-pass worklist, and any decision to accept a documented thin cluster are surfaced to the user — the skill does not silently decide a gap is acceptable.
• Calibrate length and breadth to the venue family's measured exemplar median, not maximal coverage; over-citing to look thorough reads as survey drift.
• Precision is a first-class quality signal, not just recall. A reference the paper is not confirmed to cite (a citation-graph neighbor, a keyword hit, or a "a strong paper would cite this" hunch) is a hypothesis, not a hit: tag it by tier, never promote it to the same status as a confirmed cite, and cap hunch-only additions. Adjacent/foundational clusters get ranked by load-bearing necessity and trimmed to the floor plus the few most-cited canonical members — defer the rest to an optional pool rather than padding a section beyond what the paper actually carries.
• Never misstate what a cited paper does to inflate the delta — strawman characterizations are the fastest way to a hostile reviewer (who is often the cited author).
• Abstracts and paper text are processed transiently and never committed; worksheet output stays out of the repo.
• Respect the scripts' politeness rails: identifiers one at a time, ≤25 per run, no bulk harvesting.
• Venue profiles can be stale — critical facts get re-verified against the live CFP. Never submit anything on the user's behalf.

References

• references/clustering-and-deltas.md — deriving required clusters from claimed scope, the citation floor and zero-citation gap routing, clustering axes, the per-cluster paragraph pattern, delta phrasing, comparison tables, anti-patterns, handling the closest competitor.
• references/placement-conventions.md — per-community placement and length norms, calibrating length to the venue's measured exemplar median, citation-command styles, blind-level self-citation rules, how to verify conventions empirically.

Scripts

• scripts/gather_candidates.py — fetch metadata for retrieved identifiers into a transient clustering worksheet (polite, one paper at a time).
• scripts/check_coverage.py — deterministic coverage gate: FAIL on any required cluster with zero confirmed-cited citations (including one padded only with speculative refs), WARN below the citation floor, report a precision estimate (confirmed/total) and a per-plan heuristic-tier cap, and emit a targeted second-pass retrieval worklist for find-papers. Cite keys carry an optional evidence-tier marker (key!graph / !keyword / !heuristic); only confirmed (untagged) keys count toward the floor.
• scripts/audit_bib.py — offline citation/bib audit and style census.