arXiv:2605.22866v1 Announce Type: cross 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. That requirement fails for third-party APIs, opaque endpoints, and agentic orchestrators that concentrate routing on a few tools, leaving most coalitions un-evaluable from the deployed orchestrator. We introduce BOHM, which extracts a hierarchical attribution
The proliferation of compound AI systems and a growing reliance on third-party APIs necessitates better methods for understanding and attributing actions within these opaque, multi-component architectures.
Improved attribution methods are critical for debugging, ensuring reliability, and establishing accountability in complex AI systems, especially as their use in critical applications expands.
The introduction of BOHM potentially provides a zero-cost, hierarchical attribution method that overcomes limitations of current Shapley-based approaches when dealing with opaque systems and third-party APIs.
- · AI developers
- · AI ethicists
- · Auditors of AI systems
- · Developers of legacy attribution tools
Developers gain a more practical method for understanding the contribution of individual components within their complex AI systems.
This improved understanding could accelerate the development and deployment of more reliable and interpretable compound AI systems.
Enhanced interpretability might lead to faster regulatory acceptance and broader societal trust in agentic and API-driven AI applications.
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Read at arXiv cs.LG