paper-logic

name	paper-logic
description	Review a LaTeX paper for argument-level logic, cross-section consistency, and house style. Complements /paper-review (sentence-level style) with whole-paper reasoning checks.
when_to_use	Check paper logic, argument structure, cross-section consistency, number reconciliation, terminology drift, review whole paper before submission
argument-hint	["paper-dir-or-section-files"]
allowed-tools	Read Bash(grep *) Bash(wc *) Bash(ls *) Bash(find *)

Academic Writing: Logic & Consistency Review

Review the paper at $ARGUMENTS (a directory of sections or specific files) for argument-level logic and cross-section consistency. If no argument is given, locate the paper sources (e.g., sections/*.tex plus the main file containing the abstract) and confirm the file set with the user.

This skill checks the whole-paper layer: does the argument hold together, do the numbers reconcile, is the terminology stable. For sentence-level prose (nominalizations, weak openings, word choice), run /paper-review per section; for applying fixes, run /paper-fix. Do not duplicate their sentence-level findings. The one exception is section M's mechanical greps (punctuation, agreement), which enforce house style at whole-paper scope because they catch drift that per-section review misses.

All examples below are from a fictional paper about a fictional adaptive caching system. They illustrate the shape of each antipattern; never copy them into your report. Every finding must quote the actual paper under review.

Output contract (non-negotiable)

Your report is incomplete unless ALL of the following hold. Re-walk the checklists until they do.

1. Every checklist ID (A1–A7, B1–B5, C1–C7, M, P) appears in the report, either with findings or with the line [Xn] checked — no findings, plus one sentence saying what you looked at. Silent skips are not allowed.
2. Every finding has all four parts: file:line, a verbatim quote from the paper under review, a problem statement that names the reasoning error (not "this is unclear"), and a concrete fix — for prose problems, a full rewritten sentence, not "consider rephrasing".
3. The three working tables are filled in and printed (term, number, promise), built from the paper under review. Do not write any finding before the tables are complete: most logic findings fall out of the tables, and skipping them is how reviews end up shallow.
4. The mechanical greps in section M were actually run and each hit triaged (finding / false positive, one line each).
5. Calibration: a mature 5-section systems paper typically yields 15–30 findings across severities. If you have fewer than 10, you have under-checked — re-walk A and B with the tables in front of you. If you have zero Must-fix findings, explicitly state which Must-fix categories you verified and how.
6. Findings are sorted Must fix → Should fix → Consider, and the report ends with the three tables, the top-5 list, and the verdict paragraph.

Severity rubric

• Must fix: a reviewer can reject on it — circular argument, numbers that contradict each other, claim without support, garbled contribution item, undefined term a reader cannot decode.
• Should fix: weakens credibility but doesn't break the argument — terminology drift, late definitions, false precision, renumbering debris.
• Consider: judgment calls — paragraph splits, citation placement, hedging word choice when scope is preserved. A dropped scope qualifier or a body-hedged claim turned absolute is A4/A5 (Must or Should), never Consider.

Process

1. Read all section files in order, plus any terminology-conventions file the project keeps (e.g., wording.md) if present. Logic problems are invisible in a single section — never run this skill on one file alone.
2. First pass (build, don't judge): fill the three working tables below.
3. Run the mechanical greps (section M); triage every hit.
4. Second pass (judge): walk checklists A–C against the tables.
5. Walk the per-section protocol (section P).
6. Write the report per the output contract.

The three working tables

Formats below, illustrated with fictional rows. Build them from the actual paper.

Term table

One row per recurring concept. The "names used" column exposes drift.

Concept	Canonical term (per conventions file, if any)	Names actually used (with locations)	First defined at
a unit of admission logic	predicate	predicate (02:§2.2), filter (05:RQ1), policy (05:RQ2)	02:§2.2

A concept with 2+ names, or one name covering 2+ concepts, is a C1 finding. A "first defined at" that comes after a use is a C2 finding.

Number table

One row per quantitative claim. Same fact in multiple places = one row with multiple locations; mismatched forms are B1 findings, reader-derivable-only values are B2 findings.

Fact	Value & form at each location	Locations	Source
hit-rate advantage	"1.8–2.4×" / "15–22 pp" / raw 61 vs 25–34	intro:¶5, eval:§6.2 text, Tab.2	only derivable from Tab.2 by reader arithmetic → B2

Promise table

One row per claim in the abstract, intro, or contributions list.

Promise (quoted, with qualifiers)	Where supported	Qualifier preserved?
"improves tail latency on production workloads"	§6.4 (12 of 80 workloads, selected for cache sensitivity)	NO — eval scopes to subset, intro doesn't → A4

A. Argument-structure antipatterns

A1. Solution vocabulary in the motivation

Motivation/empirical/background sections must describe the problem in problem-domain terms. If they classify the world using the system's own mechanisms, the study stops being independent evidence and begs the question.

Bad (in a workload-study section):

These access patterns map directly to our epoch-counters and ghost-list primitives.

Good (same section):

Three recurring patterns emerge: bursty re-reference, scan pollution, and slow drift. All three require admission decisions that depend on history beyond the current request.

…and in Design: "The three patterns identified in §2 reduce to two mechanisms: epoch counters and ghost lists."

How to check: list the design section's mechanism nouns, then grep for each in every section that precedes the design. Each hit in motivation/background is a finding unless it is citing prior work.

A2. Self-referential categories used as neutral populations

Defining a category by reference to the system, then reporting its size as an independent finding, is circular.

Bad:

…the subset of requests that \sys{} can intercept is \emph{trackable}. […] Our study finds that 73% of requests are trackable.

Good:

A request is \emph{trackable} if it carries an object identifier visible at the proxy layer, independent of any particular system. The study finds 73% of requests are trackable, and \sys{} targets exactly this class.

How to check: for every \emph{}d or defined category, ask "does the definition mention the system?" If yes, every later use of that category's count as a population is a finding.

A3. Broken requirement→challenge→component chains

If the motivation derives N requirements, the design must visibly consume all N. Count both ends and demand an explicit mapping.

Bad: the motivation summary lists four requirements (history-aware admission, low memory, isolation, observability); the design opens "Two key challenges arise" and resolves them with "three techniques"; isolation and observability map to neither.

Good: a mapping sentence at the start of design: "Requirements R1–R2 raise challenge C1, addressed by epoch counters (§4.1) and ghost lists (§4.2); R3 raises C2, addressed by per-tenant partitions (§4.3); R4 is an implementation property, evaluated in §6.3."

How to check: write out three literal lists (requirements, challenges, components) with locations; draw the mapping; report every orphan on either side as a finding.

A4. Conclusion outruns the evidence

Claims scoped to a selected subset must stay scoped in every restatement, and hedging level for the same claim must be identical everywhere.

Bad: eval says "These results suggest that history-aware admission improves tail latency on the 12 cache-sensitive workloads"; conclusion says "\sys{} improves tail latency on production workloads."

Good: the conclusion repeats both the hedge and the scope, or the eval explicitly justifies upgrading the claim.

How to check: for each promise-table row, diff the qualifiers ("suggest", "on the subset", "up to", "in our setting") between the eval sentence and every restatement in abstract, intro, and conclusion. Any dropped qualifier is a finding.

A5. Absolute claims for non-absolute limitations

Reserve "cannot", "never", "impossible", and "guarantees" for structural, by-construction facts (a stateless mechanism cannot track cross-event state; tool-call interception cannot observe effects outside the tool boundary). Limitations that are a matter of cost or effort ("an administrator could, with enough work") take "hard", "impractical", or "rarely". Also check the reverse drift: a claim hedged in the body must not strengthen to an absolute in the abstract or intro.

A6. A leap presented as an observation

"X has property P; this makes X the natural Y" is an argument, not a fact. Either rebut the obvious objection in place or forward-reference where it is handled.

Bad:

Application developers already know their access patterns. This makes them the natural authors of admission policy.

Good:

Application developers already know their access patterns, so they hold the context that admission policy needs. Letting tenants author policy raises an isolation problem, which we address by bounding each tenant's policy to its own partition (§4.3).

How to check: grep for natural|clearly|obviously|therefore|this makes| this means in intro/motivation; for each hit, ask "what objection would a hostile reviewer raise here, and is it answered or forward-referenced?"

A7. Two arguments packed into one paragraph

One paragraph, one claim. How to check: for each intro/motivation paragraph, write its one-line claim; if the line needs "and also", split the paragraph and report it, naming both claims.

B. Quantitative-consistency antipatterns

B1. Numbers that don't reconcile across sections

Every number in abstract/intro/conclusion must appear verbatim in (or be trivially derivable from a single labeled place in) the evaluation, and the same result must keep one canonical form.

Bad: intro "1.8–2.4×"; eval text "15–22 percentage points" and "2×"; table raw counts — three forms, no anchor sentence connecting them.

Good: one headline form, printed next to its table, reused verbatim: "serves 61 of 90 bursty workloads from cache, 1.8–2.4× the 25–34 of the baselines (Table 2)".

B2. Forced reader arithmetic

If the headline claim is a ratio or delta, print it beside the table it comes from. How to check: for each abstract/intro number, search the eval for it verbatim; if you had to compute it from table cells, so will the reviewer — that is the finding, and the fix is an anchor sentence.

B3. Denominator hygiene

Every rate names numerator and denominator at first use; exclusions are stated before the rate; subset selection states the rule, counts the remainder, and scopes conclusions (cross-check A4).

Bad: "\sys{} eliminates 70% of avoidable misses. […two paragraphs later…] Cold-start runs are excluded from the miss-rate denominator."

Good: "Of 120 avoidable misses, \sys{} eliminates 84 (70%). The 40 cold-start runs are excluded up front because no admission policy can serve a first access."

How to check: grep for % and rate; for each, write value = N/D with both numbers named in the same paragraph. Missing N or D, or a post-hoc exclusion, is a finding.

B4. False precision and unsourced constants

Significant digits must match measurement reliability; constants from outside the experiment need citations.

Bad: "$0.0314 per query […] At typical cloud egress rates, about $12.47 per workload." (four significant digits against an uncited price assumption)

Good: "about $0.03 per query, versus roughly $12 per workload at list-price cloud egress~\cite{cloudpricing2026}".

How to check: grep for \$[0-9], orders of magnitude, and 3+ significant-digit values; each needs a source (measurement, citation, or shown derivation).

B5. Metrics doing hidden work

If the metric definition makes an outcome count favorably for one system and unfavorably for another by construction, disclose it in the sentence that makes the comparison, not paragraphs later.

Bad: praising an ablation's zero wrongful evictions in one paragraph, and only later noting that the metric counts an object re-fetched within the same epoch as "retained" for the ablation but "evicted" for the full system.

Good: "the ablation's zero wrongful evictions partly reflects the metric: without prefetch, re-fetches land in the same epoch and score as retained, while the same objects score as evicted under full \sys{}."

How to check: for every cross-system comparison, re-read the metric definition and ask "could two systems with identical behavior score differently because of how outcomes are labeled?" If yes, the disclosure must be co-located with the comparison.

C. Consistency and bookkeeping antipatterns

C1. Terminology drift

One concept, one term, throughout — enforce the project's terminology conventions file where present. Flag (a) near-synonym alternation for one referent, (b) one word for two concepts. The term table makes both visible; report each drifting concept as one finding listing all locations.

C2. Defined-before-used

Every acronym, notation, configuration name, and dataset label is defined before first use — including figure captions and table headers, which readers hit out of order.

Bad: a figure caption says "CG-32 and CG-128"; the CG-$N$ notation is defined two subsections later; "warm-path configurations" is used in one setup paragraph and explained at the end of a different subsection.

How to check: for each notation in the term table, compare first-definition location against first-use location, treating each figure caption as used at its \begin{figure} line.

C3. Renumbering debris

After RQs/sections/figures are renumbered, stale labels and filenames remain.

Bad: figures named rq1_*.pdf are all cited inside the RQ2 subsection, rq2_*.pdf inside RQ3, and so on — every figure filename is off by one against the prose that cites it.

How to check: grep includegraphics and \label{, and compare each filename/label against the number of the subsection citing it. Also check that anything named in the intro (a benchmark, a dataset, a metric) is introduced by the same name in its own section.

C4. Who-does-what reconstructability

Setups with multiple models/agents/judges/generators state every role once, in one place, before results — who generates the workload, who runs it, who translates configurations, who judges outcomes, and what software or model backs each role.

Good (one place):

Roles: generator G (model A) produces the traces, translator T (model B) writes the configurations, model C is the system under test (replicated with model D), and the outcome judge runs on the same model as the system under test.

How to check: build the roles table yourself from the eval text. Count how many paragraphs you needed. More than one place = finding; a role you cannot resolve at all = Must fix.

C5. Parallelism and completeness in enumerations

Contribution lists, RQ lists, and itemized claims are grammatically parallel, and each item is a complete sentence. A garbled contribution item is a Must fix: it is the most-read sentence after the abstract.

Bad: "An evaluation on our admission benchmark building on the workload study, external latency and cost benchmarks covering batch and interactive workloads." (no main verb; not parallel)

How to check: read each list item aloud as a standalone sentence; check all items share the same grammatical skeleton ("An X that Y").

C6. Agentless claims

Every verification step names the actor and the criterion.

Bad: "We manually corrected samples flagged as requiring double-checking" (flagged by whom, against what rule?); "all of which matched expectations" (whose expectations?).

Good: "the judge flags low-confidence verdicts, and two authors re-label all flagged samples against the written rubric".

How to check: grep flagged|validated|verified|reviewed|matched|confirmed in methodology text; each hit needs a named actor and criterion.

C7. Figure–prose agreement

Numbers repeated in captions must match the prose exactly (count, rounding, units), and each caption must state the takeaway, not just the axes. How to check: for every number inside a \caption{}, find its twin in the body text; flag mismatches and takeaway-free captions.

M. Mechanical greps (run all; triage every hit)

Adjust paths to the actual file set.

# ~ used as "approximately" (renders as non-breaking space; the "about" vanishes)
grep -n '[ (]~[0-9]' *.tex sections/*.tex

# em-dashes in prose, LaTeX and Unicode forms (house style; table --- for N/A is OK)
grep -n -- '---\|—' sections/*.tex | grep -v '& *--- *&\|--- *\\\\'

# subject-verb agreement: "does" followed by a plural subject
grep -nE 'does [^.?]*\b\w+(ies|s)\b.*\b(improve|prevent|reduce|achieve)' sections/*.tex

# e.g./i.e. punctuation consistency (counts should not both be nonzero)
grep -c 'e\.g\.,' sections/*.tex; grep -c 'e\.g\.[^,]' sections/*.tex

# stale figure numbering and labels (compare against citing subsection)
grep -n 'includegraphics\|\\label{fig:\|\\ref{fig:' sections/*.tex

# semicolons joining clauses (house style; lists OK) — triage each
grep -n '; [a-z]' sections/*.tex

# rates and percentages for the B3 denominator audit
grep -n '[0-9]%\|percent\|rate' sections/*.tex

# unsourced money / magnitude claims for B4
grep -n '\\\$[0-9]\|orders of magnitude' sections/*.tex

Also check by reading (no grep possible):

• Citations interrupting the subject–verb path: move cite blocks to the end of the clause when they separate subject from verb by more than ~7 words.
• Anonymity: repo URLs, author-identifying system names, acknowledgment remnants — flag if the venue is double-blind.

House style (always enforce)

• No em-dashes (---) in paper text. Use commas, parentheses, or restructure. Table cells using --- for "not applicable" are OK.
• Avoid semicolons joining independent clauses. Use periods, conjunctions (", and", ", but"), or causal connectors ("because", "so", "since"). Semicolons are OK inside parenthetical lists.
• Academic prose, not notes. Causal connectors and flowing sentences, not strings of short declaratives. Concrete examples before abstract definitions. Every design decision states its "why" before its "what".

P. Per-section protocol

Abstract / Intro: every number traced to the eval (B1/B2); every claim in the promise table with qualifiers (A4); one paragraph = one claim (A6); "we argue/we observe" steps checked for leaps (A5); contribution list checked for parallelism (C5) and 1:1 mapping to sections.

Background / Motivation / Empirical or workload study: no solution vocabulary (A1); categories defined system-independently (A2); the closing summary's requirements recorded for A3; counts here are the canonical source for every later population claim — record them in the number table.

Design: opening consumes all requirements (A3); each mechanism's "why" traces to a motivation finding by explicit reference; terms introduced here checked against the conventions file (C1/C2).

Implementation: numbers (LoC, limits) recorded in the number table; "future work" admissions cross-checked against any capability claimed earlier (A4).

Evaluation: roles stated once (C4); every rate's denominator audited (B3); metric-construction biases disclosed at the comparison (B5); subset selections scoped (A4); figure filenames vs section numbers (C3); captions vs prose (C7); notation defined before the first figure that uses it (C2).

Related work: each contrast sentence states a checkable difference, not adjectives; no claims about your own system that the eval did not support.

Conclusion: pure restatement — any number or scope not identical to the eval's form is a finding (A4/B1).

Calibration example (expected depth per finding)

Fictional findings, at the depth every real finding must match:

[A2][Must] sections/02-motivation.tex:217 "the subset of requests that
\sys{} can intercept is \emph{trackable}"
  Problem: the category is defined by what the system can do, then §6.1
  ("our study finds 73% of requests are trackable") uses its size as a
  neutral population — the denominator of the coverage claim is circular.
  Fix: "A request is trackable if it carries an object identifier visible at
  the proxy layer, independent of mechanism." State the \sys{} alignment
  once, separately.

[B3][Must] sections/05-evaluation.tex:514+520 "eliminates 70% of avoidable
misses" … "cold-start runs are excluded from the miss-rate denominator"
  Problem: the exclusion that shapes the headline rate is disclosed two
  paragraphs after the rate; a reviewer reading linearly recomputes the rate
  with different assumptions.
  Fix: fold numerator, denominator, and exclusion into the first statement:
  "Of 120 avoidable misses, \sys{} eliminates 84 (70%). The 40 cold-start
  runs are excluded up front because no admission policy can serve a first
  access."

[C3][Should] sections/05-evaluation.tex:91,207,244 figures rq1_pipeline.pdf,
rq1_hitrate.pdf, rq1_breakdown.pdf all cited inside §RQ2
  Problem: figure filenames carry a stale RQ numbering (every eval figure is
  off by one), signaling unmaintained renumbering to reviewers.
  Fix: rename the files to match the current RQ numbers and update the
  \includegraphics paths.

Output format

1. Coverage checklist first: one line per ID (A1–A7, B1–B5, C1–C7, M, P), each findings: N or checked — no findings (looked at: …).
2. Findings, grouped by ID, sorted Must fix → Should fix → Consider, each in the four-part format above.
3. The three working tables (term, number, promise), filled in from the paper under review.
4. Top 5 highest-leverage fixes.
5. Verdict paragraph: does the chain motivation → requirements → design → evaluation → conclusion close, and where are the weakest links?