{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:P6JHRJOBPZZWXW7YOXNFZEBVMG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fc777af2085e7333c19840ffa1c96d2831bb9d6d8227d13eaf81eb4aa8050d08","cross_cats_sorted":["cs.CE","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-08T04:03:47Z","title_canon_sha256":"31c8708cbd35e80ee713920c260625efb9c27fb935a1c388e18a4c8bcfe03054"},"schema_version":"1.0","source":{"id":"2502.05439","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05439","created_at":"2026-07-05T10:55:54Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05439v2","created_at":"2026-07-05T10:55:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05439","created_at":"2026-07-05T10:55:54Z"},{"alias_kind":"pith_short_12","alias_value":"P6JHRJOBPZZW","created_at":"2026-07-05T10:55:54Z"},{"alias_kind":"pith_short_16","alias_value":"P6JHRJOBPZZWXW7Y","created_at":"2026-07-05T10:55:54Z"},{"alias_kind":"pith_short_8","alias_value":"P6JHRJOB","created_at":"2026-07-05T10:55:54Z"}],"graph_snapshots":[{"event_id":"sha256:38eaca731118891d07e5600dd79a83d98de24061657fe33d80bf31c7409b71f0","target":"graph","created_at":"2026-07-05T10:55:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.05439/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities across diverse domains. This paper explores agentic system workflows in the financial services industry. In particular, we build agentic crews with human-in-the-loop module that can effectively collaborate to perform complex modeling and model risk management (MRM) tasks. The modeling crew consists of a judge agent and multiple agents who perform specific tasks such as exploratory data analysis, feature engineering, m","authors_text":"Arjun Ravi Kannan, Ashkan Golgoon, Izunna Okpala","cross_cats":["cs.CE","cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-08T04:03:47Z","title":"Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05439","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:03d889d841879179445c8511b4bb7f847d2850818d8d9f8c39b1cbcaf91ec744","target":"record","created_at":"2026-07-05T10:55:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fc777af2085e7333c19840ffa1c96d2831bb9d6d8227d13eaf81eb4aa8050d08","cross_cats_sorted":["cs.CE","cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-08T04:03:47Z","title_canon_sha256":"31c8708cbd35e80ee713920c260625efb9c27fb935a1c388e18a4c8bcfe03054"},"schema_version":"1.0","source":{"id":"2502.05439","kind":"arxiv","version":2}},"canonical_sha256":"7f9278a5c17e736bdbf875da5c9035619a81851321f2792541fa6db43d25ded1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f9278a5c17e736bdbf875da5c9035619a81851321f2792541fa6db43d25ded1","first_computed_at":"2026-07-05T10:55:54.543050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:55:54.543050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zvGUY/H/+SL2BJE6q+TQIui1J1ZQ6AyfUua3zVF5LflR/V95+Caq9AUNEGiccIe7lweCwYMSp1p66A7Z2Ux6AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:55:54.543597Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05439","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:03d889d841879179445c8511b4bb7f847d2850818d8d9f8c39b1cbcaf91ec744","sha256:38eaca731118891d07e5600dd79a83d98de24061657fe33d80bf31c7409b71f0"],"state_sha256":"aa22b2b14c40ecc978d90a2fa3d8aaacb1e251aab1d95c57b4db90fec93f0598"}