BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
Researchers have introduced BOHM, a novel method for attributing contributions within compound AI systems that utilize hierarchical routing. Unlike traditional Shapley-based methods, BOHM leverages existing routing weights, offering a zero-cost attribution solution that is particularly effective for systems with opaque components or agentic orchestrators. The method provides multi-resolution attribution across all levels of the hierarchy simultaneously, demonstrating strong correlation with Shapley values on various benchmarks while requiring significantly fewer evaluations. AI
IMPACT Provides a more efficient method for understanding how complex AI systems make decisions, potentially improving debugging and interpretability.