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

Enhanced query processing: The system now extracts keywords from user-provided text using NLP techniques before querying our database of academic papers.
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
Performance optimization: The complete search pipeline executes in seconds, delivering results in real-time.
Future development: We're exploring applications of these algorithms in automated cross-referencing and validation systems.

Isaac changelog

Find Sources Feature Update