CoPaper.AI Blog
Deep dives into the design philosophy, technical architecture, and product thinking behind CoPaper.AI.
We are excited to announce the open-source release of StatsPAI — a comprehensive Python package that consolidates 150+ causal inference and econometric methods into one cohesive API, bridging the gap between R, Stata, and modern Python.
CoPaper.AI introduces the P1-P2-P3-P4 four-part empirical paper architecture — a framework that inherits the best of IMRaD and the five-chapter dissertation format while being purpose-built for AI-assisted academic writing.
Fonts and sizes seem trivial, but they're one of the most overlooked — and most error-prone — parts of academic publishing. This guide covers formatting rules for major journals and gives you copy-paste-ready Matplotlib configs.
How we upgraded two core modules — Paper Refinement and Data Analysis — from monolithic single-agent architectures to coordinated multi-agent systems, achieving better accuracy, lower costs, and unlocking new capabilities.
As AI coding agents like Claude Code, Codex, and Cursor increasingly participate in empirical research, the underlying ecosystem needs a unified, stable, and standardized Python statistics package. StatsPAI is built for exactly this scenario — designed for both researchers and agents.