We've implemented a comprehensive upgrade to the "Find Sources" feature:

1. Enhanced query processing: The system now extracts keywords from user-provided text using NLP techniques before querying our database of academic papers.

2. Multi-stage relevance filtering:

   • Initial results generated from keyword matching against our paper repository

   • Results refined using a machine learning model trained on established relevance signals

   • Results ranked by citation count to prioritize influential papers

   • Final ranking determined through semantic vector search comparing user text against paper abstracts for contextual relevance

3. Performance optimization: The complete search pipeline executes in seconds, delivering results in real-time.

4. Future development: We're exploring applications of these algorithms in automated cross-referencing and validation systems.