Benchmark Paper

Benchmark Paper

Produces a venue-fit scorecard: how closely a draft matches the measurable patterns of recent award-winning and top-cited papers at the target venue. This is a conformance gauge to help an author find gaps before submitting — it is not a prediction of acceptance, a best-paper forecast, or a judgment of scientific merit.

Pairs naturally with simulate-reviewers (which red-teams content quality) and study-exemplars (which it calls to build the comparison corpus). Run preflight-check first — a desk-reject defect makes any score moot.

When to use

• The author wants a single, shareable readiness signal before submitting.
• The author asks how their draft stacks up against the venue's strongest recent papers.
• The author wants to know which dimensions are weakest relative to exemplars, ranked by fixability.

When NOT to use it (say this plainly to the user)

• It cannot judge whether the science is novel, correct, or important — the things that actually win awards. Use simulate-reviewers for content critique.
• It cannot predict acceptance or a best-paper award. Anyone who claims a tool can is selling false precision.
• A high score on a flawed paper is meaningless. Conformance to form ≠ quality of substance.

Inputs

• The draft: a .tex file (or compiled PDF / markdown), with .bib if available.
• The target venue id (e.g. sigspatial-2026) → its profile in venues/.
• Optional: a corpus size N (default 8 exemplars) and a recency window (default last 5 years).

Process

1. Resolve the venue profile from venues/conferences/<venue>.yml and its family: profile in venues/families/<family>.yml. If missing, ask the user or have add-venue-profile create it. Re-verify the venue is correct before scoring. Apply the staleness gate: profiles are year-versioned (verified.valid_window, verified.last_verified_against_cfp). Do not assert a hard format constraint (page limit, column count, mandatory section, deadline) from a profile whose valid_window does not include the target cycle without a fresh CFP check first; if you cannot check, mark it needs-verification and disclose that the basis was a year-mismatched profile (see references/scoring-rubric.md "Staleness gate"). A year-mismatched profile may still inform priors (the exemplar distribution, the modal skeleton).
2. Build the on-family exemplar corpus. The distribution dimensions (section architecture, citation density, abstract structure, figure/table conventions) must be scored against an on-family distribution — same venue family — never an off-family proxy. Resolve it in priority order: (a) a live study-exemplars corpus for the target venue — preferred; invoke study-exemplars to fetch (on demand, legally, transiently) N recent best-paper awardees and top-cited papers at the venue and extract their feature profile, never bundling or storing paper text; (b) the family profile's exemplar_distribution block as a fallback prior (disclose it is from the family prior, carry its confidence); (c) if neither exists for this family, reduce N and disclose no on-family exemplar distribution on those dimensions — do not borrow another family's numbers. If award lists are unavailable, fall back to top-cited and say so. The corpus basis (live vs family-prior vs none) must be disclosed.
3. Extract the draft's comparable features along the dimensions in references/scoring-rubric.md (section architecture, contribution framing, evaluation rigor signals, claim/citation density, abstract structure, figure/table conventions, reproducibility artifacts). Use verify-citations output if present so the citation-integrity dimension is grounded. Detect the realization level of each dimension (absent/planned/drafted/complete): a dimension whose evidence is dominated by honest [RESULT]/[TBD]/\todo placeholders is planned, not weak. Record it in the features JSON realization field.
4. Score each dimension as conformance to the exemplar distribution, with an explicit basis for every number (what was measured, what the exemplar range was). For planned/absent dimensions, score the completeness and specificity of the design (named baselines, datasets, ablation list, matched-budget protocol, variance/significance policy) — never realized numbers — and mark deferred [RESULT] slots as deferred-but-specified in the basis. Run python3 scripts/scorecard.py features.json --venue <id> to compute and render deterministically — do not eyeball the aggregate. The script caps planned dimensions below the "within range" band and relabels the index plan-conformance when any dimension is unrealized, so it is never silently compared against an executed paper.
5. Report: the scorecard (per-dimension 0–10 + venue-fit/plan-conformance index), the 3 weakest dimensions ranked by fixability, concrete fixes tied to exemplar patterns, and the mandatory caveat block. For a plan, frame the gap as experiments not run (path: specify → run), not design is weak. Offer the one-line shareable summary the user can screenshot.

Output

A scorecard.md containing:

• Venue-fit index (0–10) with a one-line plain-English band (e.g. "structurally in line with recent {venue} winners; evaluation section is the gap"). For a plan/outline the index is labeled plan-conformance and must not be compared against executed papers.
• Per-dimension table: score, realization level, exemplar range, your value, basis.
• Top fixes ranked by impact × ease.
• Corpus disclosure: which papers formed the basis, award-vs-cited, recency, and whether the distribution was a live on-family corpus, a family-profile prior, or absent (no on-family exemplar distribution).
• Format-basis disclosure when any hard constraint came from a year-mismatched profile (flag it needs-verification until checked against the live CFP).
• Caveats (always): conformance not quality; not an acceptance/award prediction; human judgment required.

Guardrails

• Every score states what was measured and the exemplar range it was compared against. No bare numbers.
• Never present the index as a probability of acceptance or an award. Refuse to, if asked — explain why.
• Corpus is fetched on demand from open-access sources, processed transiently, never stored or committed (see study-exemplars).
• Never fabricate exemplar data to fill the corpus; if you can't reach N papers, score against fewer and disclose it.
• This is a copilot signal, not a verdict. The author decides.

Memory

Uses the shared .paper-memory/ convention in the user's paper directory (full spec: paper-memory-convention.md).

• At start: read .paper-memory/profile.yml (vertical, venue tier) to pick exemplar emphasis, and lessons.md to recall which dimensions were weak last run and any recurring gaps for this author — lead with them.
• At end: append durable findings in the shared format - [YYYY-MM-DD] (benchmark-paper | <scope>) weak-dimension -> recommendation (via reflect-and-improve's reflect_log.py append, which dedupes and dates). A dimension that lags exemplars across drafts is recurring; a one-time gap is this-paper. Do not log the full scorecard, only the lasting takeaways.
• Create .paper-memory/ on demand if absent and offer to add it to the project .gitignore. It is local-only; never upload it or copy it into this repo.