Running AI agents to automate outreach at scale

Hugging Face, a hub for open and collaborative machine learning, plays a pivotal role in enhancing the discoverability and documentation of AI research artifacts through metadata tags and linking to relevant papers. The platform aims to streamline the process of making research outputs available by supporting features like Hugging Face Paper Pages and facilitating better visibility compared to platforms like Google Drive or Dropbox. In an effort to automate outreach to AI researchers and improve the availability of their work on Hugging Face, a workflow powered by large language models (LLMs) was developed to replace manual processes. This workflow, inspired by Anthropic's insights on building effective AI agents, involves identifying GitHub URLs of papers, classifying them based on the existence and novelty of artifacts, and creating GitHub issues or pull requests. Despite the rising trend of autonomous agents, the workflow remains crucial for predictable automation of known steps, with plans to possibly transition to a fully autonomous agent in the future. The initiative demonstrates how leveraging LLMs and automation can significantly scale contributions, as evidenced by over 14,000 contributions made by the user account implementing this workflow.