PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelines

In three linesPASC is a conformal prediction method guaranteeing simultaneous coverage across all stages in multi-stage NLP pipelines (NER → NED → entity typing, RAG, agent chains). On CoNLL-2003, PASC achieves 96.4% end-to-end coverage vs 93.4% for Bonferroni and 86.5% for independent CP, 1.7x faster, and maintains robustness under distribution shift (WNUT-17, WikiNEuRal).

Summary generated by Claude — human-verified