{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QMKIA63R4LMRINGNBOLT6K7MCE","short_pith_number":"pith:QMKIA63R","canonical_record":{"source":{"id":"2602.18895","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-02-21T16:35:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"628d96c1c08ae0eafcce01caf494a7d366b49d038fb6abbc1c0f6ad051e5632f","abstract_canon_sha256":"bd34da2192cb54ca0c8921ca6980800694c6b85179ba6d2ab772ae2e694807a2"},"schema_version":"1.0"},"canonical_sha256":"8314807b71e2d91434cd0b973f2bec111a0fff55a7196dfd2aaaf402b51e6e36","source":{"kind":"arxiv","id":"2602.18895","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.18895","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2602.18895v2","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.18895","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"QMKIA63R4LMR","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"QMKIA63R4LMRINGN","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"QMKIA63R","created_at":"2026-05-20T00:04:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QMKIA63R4LMRINGNBOLT6K7MCE","target":"record","payload":{"canonical_record":{"source":{"id":"2602.18895","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-02-21T16:35:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"628d96c1c08ae0eafcce01caf494a7d366b49d038fb6abbc1c0f6ad051e5632f","abstract_canon_sha256":"bd34da2192cb54ca0c8921ca6980800694c6b85179ba6d2ab772ae2e694807a2"},"schema_version":"1.0"},"canonical_sha256":"8314807b71e2d91434cd0b973f2bec111a0fff55a7196dfd2aaaf402b51e6e36","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:26.234819Z","signature_b64":"0PqmwOwDJedRNmZ58+u6PiuS9AJt+ktCdM1j/Bda2e6E3PF2m7DgPblsf/EMHcac385iuPeGzxa/Mi5rccrPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8314807b71e2d91434cd0b973f2bec111a0fff55a7196dfd2aaaf402b51e6e36","last_reissued_at":"2026-05-20T00:04:26.233977Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:26.233977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.18895","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JzqbfH9UfYXIWT3mBNYYcROPBCyk2n45lNthOA4kD5KJ9KWs7ORVvrdKEmyqrafeFn8tpZCsc4Cjs0FNG+WBDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T22:33:31.253332Z"},"content_sha256":"00fa5f7f0e34720458fd51e866eac8c94f59b6248daa07b38e1f7d04c6da0fd3","schema_version":"1.0","event_id":"sha256:00fa5f7f0e34720458fd51e866eac8c94f59b6248daa07b38e1f7d04c6da0fd3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QMKIA63R4LMRINGNBOLT6K7MCE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.RM","authors_text":"Dingyuan Liu, Liya Li, Wenxi Geng, Yiqing Wang","submitted_at":"2026-02-21T16:35:06Z","abstract_excerpt":"Large language models (LLMs) have shown promise in translating model-based explanations into human-readable narratives. This study evaluates whether LLMs can serve as post-hoc explainability interfaces for credit risk models, focusing on their ability to preserve feature-importance rankings and generate autonomous explanations. Using a LendingClub dataset, we compare LLM outputs with SHAP and coefficient-based attributions on three major LLMs, including GPT-4-turbo, Claude-Sonnet-4.5, and Gemini-2.5-Flash. Results indicate that LLMs reliably reproduce reference rankings under controlled prompt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.18895","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.18895/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rtHuOjF2lAZG5Lu0PEMHw3YjIfZZQskFl7evtGFgU+kuYTb8/isP6vy6C+0bQ8WIR3PaGdmi6Vprp5PaZBkLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T22:33:31.253722Z"},"content_sha256":"5ac486a84929f2d4af7e2625b37c707c8c353aa9ded4637cd9f500615f55c9bf","schema_version":"1.0","event_id":"sha256:5ac486a84929f2d4af7e2625b37c707c8c353aa9ded4637cd9f500615f55c9bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QMKIA63R4LMRINGNBOLT6K7MCE/bundle.json","state_url":"https://pith.science/pith/QMKIA63R4LMRINGNBOLT6K7MCE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QMKIA63R4LMRINGNBOLT6K7MCE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T22:33:31Z","links":{"resolver":"https://pith.science/pith/QMKIA63R4LMRINGNBOLT6K7MCE","bundle":"https://pith.science/pith/QMKIA63R4LMRINGNBOLT6K7MCE/bundle.json","state":"https://pith.science/pith/QMKIA63R4LMRINGNBOLT6K7MCE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QMKIA63R4LMRINGNBOLT6K7MCE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QMKIA63R4LMRINGNBOLT6K7MCE","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":"bd34da2192cb54ca0c8921ca6980800694c6b85179ba6d2ab772ae2e694807a2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-02-21T16:35:06Z","title_canon_sha256":"628d96c1c08ae0eafcce01caf494a7d366b49d038fb6abbc1c0f6ad051e5632f"},"schema_version":"1.0","source":{"id":"2602.18895","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.18895","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2602.18895v2","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.18895","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"QMKIA63R4LMR","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"QMKIA63R4LMRINGN","created_at":"2026-05-20T00:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"QMKIA63R","created_at":"2026-05-20T00:04:26Z"}],"graph_snapshots":[{"event_id":"sha256:5ac486a84929f2d4af7e2625b37c707c8c353aa9ded4637cd9f500615f55c9bf","target":"graph","created_at":"2026-05-20T00:04:26Z","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/2602.18895/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have shown promise in translating model-based explanations into human-readable narratives. This study evaluates whether LLMs can serve as post-hoc explainability interfaces for credit risk models, focusing on their ability to preserve feature-importance rankings and generate autonomous explanations. Using a LendingClub dataset, we compare LLM outputs with SHAP and coefficient-based attributions on three major LLMs, including GPT-4-turbo, Claude-Sonnet-4.5, and Gemini-2.5-Flash. Results indicate that LLMs reliably reproduce reference rankings under controlled prompt","authors_text":"Dingyuan Liu, Liya Li, Wenxi Geng, Yiqing Wang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-02-21T16:35:06Z","title":"Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.18895","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":"sha256:00fa5f7f0e34720458fd51e866eac8c94f59b6248daa07b38e1f7d04c6da0fd3","target":"record","created_at":"2026-05-20T00:04:26Z","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":"bd34da2192cb54ca0c8921ca6980800694c6b85179ba6d2ab772ae2e694807a2","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.RM","submitted_at":"2026-02-21T16:35:06Z","title_canon_sha256":"628d96c1c08ae0eafcce01caf494a7d366b49d038fb6abbc1c0f6ad051e5632f"},"schema_version":"1.0","source":{"id":"2602.18895","kind":"arxiv","version":2}},"canonical_sha256":"8314807b71e2d91434cd0b973f2bec111a0fff55a7196dfd2aaaf402b51e6e36","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8314807b71e2d91434cd0b973f2bec111a0fff55a7196dfd2aaaf402b51e6e36","first_computed_at":"2026-05-20T00:04:26.233977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:26.233977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0PqmwOwDJedRNmZ58+u6PiuS9AJt+ktCdM1j/Bda2e6E3PF2m7DgPblsf/EMHcac385iuPeGzxa/Mi5rccrPCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:26.234819Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.18895","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00fa5f7f0e34720458fd51e866eac8c94f59b6248daa07b38e1f7d04c6da0fd3","sha256:5ac486a84929f2d4af7e2625b37c707c8c353aa9ded4637cd9f500615f55c9bf"],"state_sha256":"1df8ba0d56dcf2b23eb249fac5563b470a9ef8ef0d90afec142211380a15a224"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qQO3T1+4AZ5JjMLbHHAX+9S+0Y4yE2RutmxqRPWMTTReGPYZN3yHcRZXkxP+QvjtVBOjS6ONuJgVZP/I2iFgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T22:33:31.255779Z","bundle_sha256":"f84a3186d0e36544825354b4f65fc3640b24f224924fc7d7d5ac7adbbc52e4d0"}}