aris-autonomous-ml-research

Automate machine learning research workflows by orchestrating idea discovery, cross-model adversarial review loops, and automated paper writing into a single hands-off pipeline. You can initiate the full process with a single prompt and wake up to a scored, compiled paper.

Cross-model review in autonomous ML research uses an adversarial reviewer model, such as Codex, to identify blind spots and improve work quality. It integrates various LLM providers to critically evaluate research outputs during the automated pipeline.

Automated paper writing supports LaTeX-based PDF compilation with anti-hallucination citations. It integrates directly with Zotero and Obsidian to manage literature and ensure references are accurately compiled into the final research document.

You can use an adversarial LLM to find blind spots in generated research by running cross-model review loops. The pipeline leverages different LLM providers to critique and score work, enhancing the quality of the final paper.

Run an overnight autonomous research pipeline by executing a single command like `/research-pipeline "your research direction"`. This triggers idea generation, literature review, and paper compilation, enabling hands-off research while you sleep.