content-refinement-agent

Simulated peer review for LaTeX manuscripts works by applying a six-axis rubric to identify strengths, weaknesses, and questions, then applying iterative targeted revisions to produce a validated paper and an auditable worklog.

AI-assisted writing tracks manuscript revisions by enforcing a strict revision protocol with per-iteration snapshots, automated scoring, and explicit best-snapshot promotion to ensure traceability and reproducibility.

Improving clarity and structure in a research paper is best achieved through an iterative revision loop that simulates expert peer review and applies targeted edits while maintaining an explicit worklog for auditing.

Manuscript editing prevents data leakage by enforcing built-in safety and integrity gates, including anti-leakage constraints and numeric ground-truth verification against the existing experimental log.

Limitations of AI-assisted manuscript editing include its scope being restricted to existing experiments in the paper log, meaning it cannot introduce new experiments and relies on a strict revision protocol for safety.

LaTeX manuscript revision requires citation sanity checks as a built-in integrity gate to maintain quality and reproducibility, ensuring references and formatting remain valid throughout the iterative editing process.