Paper2Agent is an AI agent that reads scientific papers and automatically reproduces their results. It's published in Nature and available as a live demo where you can query it about papers and run workflows through GitHub.

• Automates the extraction and reproduction of experimental results directly from published papers
• Reduces manual work scientists spend reverse-engineering methods and validating findings
• Deployed as an interactive agent you can query in real-time about paper contents and methodology

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

This article breaks research down into trainable habits: choose your own problems, broaden your reading beyond trends, write and log every idea, and tighten your experiment loop with solid tooling. It also stresses purposeful exploration, scrutinizing outputs by hand, and building a generous network to compound learning and productivity over time.

• Pick your own research problems by deciding the outcome you want and working backward, rather than copying trending topics—forces originality instead of endless tweaking of existing literature.
• Train "taste" like a muscle by predicting experiment results before running them and scoring those forecasts over time.
• Read beyond trending papers (arXiv hot lists, Slack) into older/cross-field work—mixture of experts (1991), LSTMs (1997), Shannon's 1952 talk—to spot dead ends and promising angles early.
• Keep a running written lab log (hypothesis, setup, expectation, result, belief update) and write public essays, since exposing assumptions in writing guards against self-deception and can shape a field more than dense papers.