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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 tree directly from the routing weights such systems already maintain: leaf attribution is the path product of root-to-leaf routing weights; level-k attribution is the induced distribution over depth-k nodes. The method has zero marginal cost, requires no access to component internals, and provides multi-resolution attribution at every level simultaneously, which flat methods cannot offer at any ev","title":"BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems","url":"https://arxiv.org/abs/2605.22866","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.22866v1 Announce Type: new \nAbstract: 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 tree directly from the routing weights such systems already maintain: leaf attribution is the path product of root-to-leaf routing weights; level-k attribution is the induced distribution over depth-k nodes. The method has zero marginal cost, requires no access to component internals, and provides multi-resolution attribution at every level simultaneously, which flat methods cannot offer at any ev","title":"BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-25T04:43:48Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.22866"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:b7f9edc25a2b9ac305197e3324bbd4ccad5bdf27d288a761f85a3808c5f316653d5346ab5b36bb75e183293b97fea280cce12ee71f7d669e54f8905234ae8b0f","signer":"crovia.substrate","subject":{"observed_at":"2026-05-25T04:43:48Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.22866"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"15435ec776be7a31bdbd60bd83e4914cb1f6f8929de3213e124c9ba38db8a796","leaf_index":149496,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"5cd4e3a29a485fd53d7ab2b73c2aedfb2a0cfdbb0062c8b76d26737e6b5292fb","side":"right"},{"sibling":"b3b680482418879d646bee2efc27b81410518b705a38ec9cc6160b63fa0e631b","side":"right"},{"sibling":"3e655aff4b5b8690b5f1a0b46f4e770e0fc65e402452645dbf38f7a81e919da7","side":"right"},{"sibling":"b87dbe2b43899c74b28ad085c30d33f69521df3a62a61e562518f01cc90713a6","side":"left"},{"sibling":"0973ebb24ae125a119ed08470818481ea7b936dcc098aa6d88ea91b521a4b1c2","side":"left"},{"sibling":"13f293e7219e1da9ee1afaaee2334491d441f9774e29819572c50d793ba1cddf","side":"left"},{"sibling":"f84ffab05727d6f15e45b6dd52a40aa8d141b54111ae32c0adbaa67e72efec6f","side":"left"},{"sibling":"71bef7e86c9fe98d23af0e5458edddd60dee4bc05ee18768643670728a3486fb","side":"left"},{"sibling":"262c75c0e50905dc91ef8de66ed202b3ff2b8b1c3502663b051ae040e34c458e","side":"left"},{"sibling":"85db764d2c320c82e1731739119f4ff1ff347788d3271ec1568027e5f6ad0ad2","side":"left"},{"sibling":"5c4ff01819c3dfe5105c2c31139b878c525a66d2ed2f835e2d6579c68da04611","side":"left"},{"sibling":"c00108054464ebcf24c02562e77444a342511cb2954108a37a79b6e104f1b6d8","side":"right"},{"sibling":"debbc1a6232ea7970009b91bdc2345041b2de5ac59551f955855d579001e512c","side":"right"},{"sibling":"216869846f40bd905626884f58cb67b9019e488b3656946a7808c436ab7339ac","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"e734b0c6d6d13acb5f4cf00367b3913e5bbf1aa717377aa4e4820c6de24b7673","side":"right"},{"sibling":"4bbb7f78e96179bb9cdd06d7207b66b3503ab68e4880438263025b04ebe8f7ec","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":149835,"merkle_root":"51484a548bc0cf3d178eec868e98935c87f0aa83369143b7c96b048585e5ba01","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260525T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-25T05:37:33Z","sig_algorithm":"ed25519","signature":"99a657e53a015ace0bade5d7fbb3f60f950b6d1e1128251a63afcc454a492422cae5efdc0c18503b4f7e62f371bd48f2889be78d9048c4681d6c1e73f754d705","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_55536b39a05a17bbfc76a023da171fb7c49f838a13a9c76fe7a93108967b857b"}}