This work proposes a retrieval-augmented LLM pipeline that identifies and optimizes problematic contract revisions using synthetic data, semantic retrieval, and acceptability-based alignment.

This work proposes a two-stage document classification system that filters and routes regulatory documents using an ensemble of classical classifiers and a DocBERT-based multi-label model, achieving strong performance on real-world industrial data from Con Edison.