This paper argues that traditional academic articles hide failed experiments and leave out key implementation details, creating a “narrative tax” and an “engineering tax” that limit reproducibility. It proposes replacing static papers with ARA research packages—complete, executable bundles containing code, pipelines, and failure logs—so AI agents can fully understand and build on the work.

• 37 researchers from Stanford, CMU, Michigan and other top schools are pushing to replace traditional papers with "AI-ready research packages" (ARA)
• Papers hide a "narrative tax" (failed experiments and dead ends erased for a clean success story) and an "engineering tax" (missing implementation details AI needs to reproduce results)
• ARA packages would include full datasets, executable pipelines, complete code, decision logs, and records of failed attempts, not just a polished writeup
• Proposes judging research impact by whether an AI can rerun and build on it, rather than by narrative quality or page count