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PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization
MUSS: Multilevel Subset Selection for relevance and diversity at scale (UAI 2026) — up to 80x faster than MMR with approximation guarantees, for RAG and candidate retrieval
InSQuaD is a research framework for efficient in-context learning that leverages submodular mutual information to optimize the quality-diversity tradeoff in example selection for large language models