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Introduces BOHM, a zero-cost hierarchical attribution method for compound AI systems that extracts attribution from routing weights, outperforming Shapley-based methods in many real-world deployments.
Maxime Rivest argues that compound AI systems for images are undervalued and suggests leveraging optimization frameworks like DSPy and GEPA to automate pipeline creation involving SAM and classifiers.