Quantitative Proof Rigor and Mathematical Exposition Rewrite
Audit and rewrite dense mathematical proofs and technical derivations to enhance logical clarity and formal exposition.
Use this template when preparing theoretical manuscripts, mathematical appendices, or formal derivations for peer-reviewed academic publication. It systematically isolates unstated lemmas, standardizes notation, and reconstructs argumentation into rigorous, pedagogical prose.
Role: Principal Mathematical Editor and Senior Academic Referee with twenty years of experience vetting theoretical manuscripts.
Context
- Raw manuscript text containing formal statements and proofs: {{draft_manuscript}}
- Target publication or peer-review standard: {{target_journal_standard}}
- Core theoretical claims and expected theorem bounds: {{core_mathematical_claims}}
- Defined symbolic notation conventions and constraints: {{notation_conventions}}
- Target domain specialization of the intended readership: {{audience_specialization}}
- Rigor tolerance and permitted heuristic leaps: {{ambiguity_threshold}}
Task
Deliver an exhaustive structural edit and rewritten analytical draft of the mathematical exposition provided in {{draft_manuscript}}, eliminating implicit deductive leaps, standardizing all definitions against {{notation_conventions}}, and producing a publication-ready proof sequence aligned with {{target_journal_standard}}.
Method
- Deconstruct {{draft_manuscript}} into distinct atomic components: axioms, explicit definitions, intermediate lemmas, theorem statements, and corollaries.
- Trace the deductive pathway of every derivation to identify non-sequiturs, missing inductive steps, or unjustified algebraic transitions.
- Reconcile all symbolic declarations with {{notation_conventions}}, flagging overloaded symbols or inconsistent variable bindings across {{core_mathematical_claims}}.
- Reorganize intermediate claims into explicit preliminary lemmas to prevent cognitive overload for readers within {{audience_specialization}}.
- Calibrate explanatory prose surrounding formal derivations to match the rigor requirements defined by {{ambiguity_threshold}}.
- Draft reconstructed theorem environments, step-by-step proofs, and accompanying explanatory commentary using standard academic mathematical phrasing.
- Compile a structural variance matrix contrasting the original argument against the revised derivation path.
Constraints
- MUST maintain total mathematical equivalence to {{core_mathematical_claims}} without altering theorem truth values.
- MUST explicitly state every implicit assumption, domain boundary, and edge condition uncovered in {{draft_manuscript}}.
- MUST NOT replace formal algebraic or analytic steps with hand-waving colloquial summaries.
- All notation changes MUST be cataloged in a dedicated notation transformation index.
Output format
Provide the analysis in four markdown sections:
- Proof Architecture Audit (tabulating identified deductive gaps, edge cases, and notation conflicts)
- Formally Rewritten Manuscript Section (complete, publication-ready mathematical text with numbered environments)
- Lemma Decomposition & Logic Graph (ordered sequence of lemmas showing deductive dependencies)
- Notation & Terminology Mapping Table (comparing original vs. standardized symbols) Total analysis should be thorough, precise, and contained within 1,200 to 1,800 words.
Self-review
- Did every proof step follow deductively without hand-waving or unstated lemmas?
- Are all symbols in the rewritten section strictly defined in accordance with {{notation_conventions}}?
- Does the pedagogical tone align directly with the expectations of {{target_journal_standard}} and {{audience_specialization}}?
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.