The article analyzes how researchers can use Codex for repetitive data processing and aligning code with literature, but warns against overreliance on its statistical interpretations, silent parameter changes, and unverified formulas without independent validation.
The author shares a 10-run experiment showing that structured Codex Skills reduce research draft time from 40 to 20 minutes, cut manual corrections from 5–7 rounds to 2–3, and improve first-pass rates by codifying input rules, processing flows, and output contracts for repetitive tasks.
The article argues that investing time in creating Codex research skills is worthwhile only for repetitive tasks like weekly paper tracking, experiment reproduction, and team onboarding, advising researchers to start small, validate with real runs, and prioritize traceability over automation speed.
The author details a step‑by‑step auto‑research workflow that uses Claude Code and Codex across multiple medical imaging challenges, from dataset preparation and model training on RTX 4090 GPUs to AI‑generated draft writing, iterative Codex review, and ultimately achieving a conference paper acceptance.
Japanese theoretical physicist Yuji Tachikawa recounts how Claude Fable 5 identified a hidden calculation error, proposed a novel approach, wrote SymPy code to verify it, and ultimately resolved a six‑month‑old string‑theory problem, sparking debate on AI’s emerging research intuition.
The author details a half‑year of using AI tools such as Claude Code, OpenScholar and PaperQA2 across literature search, code debugging, manuscript drafting and student mentoring, highlighting concrete speed gains, pitfalls like citation hallucinations, and practical guidelines for safe adoption in scientific computing.
