BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
The article presents BOHM, a new method for hierarchical attribution in compound AI systems. Unlike traditional Shapley-based methods, BOHM operates without requiring access to component internals and offers multi-resolution attribution. The method demonstrates high efficiency and accuracy across various benchmarks, making it a valuable tool for understanding AI system performance.
- ▪BOHM extracts a hierarchical attribution tree directly from routing weights maintained by AI systems.
- ▪The method has zero marginal cost and provides multi-resolution attribution simultaneously.
- ▪In tests, BOHM achieved a Kendall tau of 0.928, while SHAP required significantly more evaluations to reach a tau of 0.980.
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Computer Science > Artificial Intelligence arXiv:2605.22866 (cs) [Submitted on 19 May 2026] Title:BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems Authors:Joss Armstrong View a PDF of the paper titled BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems, by Joss Armstrong View PDF HTML (experimental) Abstract:Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based methods (SHAP), which decompose a coalition value function into per-component marginal contributions and require evaluation of the system on arbitrary component subsets.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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