{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ZLP4Y35ILSH3LKLD2X64ARDPZN","short_pith_number":"pith:ZLP4Y35I","canonical_record":{"source":{"id":"2608.05375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-05T19:52:55Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"2af7f6329dffd30aad9db447b5c5f56d58138e4de4f2210281c3d8d5d1be4cdb","abstract_canon_sha256":"132e7dc5b9968f8918521f66f5002e347c691654de5c1159bba852b804309263"},"schema_version":"1.0"},"canonical_sha256":"cadfcc6fa85c8fb5a963d5fdc0446fcb5d953b874fe281c651d0b9f45a28b3d6","source":{"kind":"arxiv","id":"2608.05375","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.05375","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"arxiv_version","alias_value":"2608.05375v1","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05375","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_12","alias_value":"ZLP4Y35ILSH3","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_16","alias_value":"ZLP4Y35ILSH3LKLD","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_8","alias_value":"ZLP4Y35I","created_at":"2026-08-07T00:47:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ZLP4Y35ILSH3LKLD2X64ARDPZN","target":"record","payload":{"canonical_record":{"source":{"id":"2608.05375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-05T19:52:55Z","cross_cats_sorted":["cs.LG","cs.MA"],"title_canon_sha256":"2af7f6329dffd30aad9db447b5c5f56d58138e4de4f2210281c3d8d5d1be4cdb","abstract_canon_sha256":"132e7dc5b9968f8918521f66f5002e347c691654de5c1159bba852b804309263"},"schema_version":"1.0"},"canonical_sha256":"cadfcc6fa85c8fb5a963d5fdc0446fcb5d953b874fe281c651d0b9f45a28b3d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:47:32.776707Z","signature_b64":"2EHYx3nJVA8NfHim4IMBUDWGI2pXE8fDWrcZMjQ+4+pNQJQRFpA7cnG4oQha8RGGHU16PxVecN/KUGWiIE9bCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cadfcc6fa85c8fb5a963d5fdc0446fcb5d953b874fe281c651d0b9f45a28b3d6","last_reissued_at":"2026-08-07T00:47:32.775331Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:47:32.775331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.05375","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-07T00:47:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HP/9ueiQYrOlt0Fvbip8lE97j4lU6XHmQXvxlGir4SKNVzabDXivVtfhrmWIaJ+LdNmyrDvuHNlkiUdgPDoVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:26:20.510393Z"},"content_sha256":"749b192da370cd700c3cf62ef0df34bfa3f2d150ca29f5b5f0ea6fb67e105eb0","schema_version":"1.0","event_id":"sha256:749b192da370cd700c3cf62ef0df34bfa3f2d150ca29f5b5f0ea6fb67e105eb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ZLP4Y35ILSH3LKLD2X64ARDPZN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.MA"],"primary_cat":"cs.AI","authors_text":"Bo-Hong Wang, Elizabeth Kourbatski, Gilles Boire, Hegang Chen, Jun Bai, Marie Hudson, Ruilin Wang, Yue Li, Ziyang Song","submitted_at":"2026-08-05T19:52:55Z","abstract_excerpt":"Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constrained by scarce, heterogeneous, and temporal complexity. Developing effective ML pipelines for such data remains time-consuming and error-prone, while existing automated machine learning (AutoML) systems only partially address this challenge because they largely rely on brute-force search over predefined spaces and lack explicit reasoning and memory. We therefore reformulate AutoML for small clinical data from exhaustive search to reasoning-driven refinement. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05375","kind":"arxiv","version":1},"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/2608.05375/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":"2026-08-07T00:47:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"16vV0HuC5R7mQ8R6PnRvCpkhY7pK4ZJlx6qRzDR+hzRmmbjtKn+lWB9ywxpRQTDA56nR9C6pMQaNhivh7NqSBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:26:20.511013Z"},"content_sha256":"41a5bae50d993f16b308daaff6a39bc651cc4dac398e431c070ae4a3f4fca22a","schema_version":"1.0","event_id":"sha256:41a5bae50d993f16b308daaff6a39bc651cc4dac398e431c070ae4a3f4fca22a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/bundle.json","state_url":"https://pith.science/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/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":"2026-08-08T23:26:20Z","links":{"resolver":"https://pith.science/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN","bundle":"https://pith.science/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/bundle.json","state":"https://pith.science/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZLP4Y35ILSH3LKLD2X64ARDPZN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZLP4Y35ILSH3LKLD2X64ARDPZN","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":"132e7dc5b9968f8918521f66f5002e347c691654de5c1159bba852b804309263","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-05T19:52:55Z","title_canon_sha256":"2af7f6329dffd30aad9db447b5c5f56d58138e4de4f2210281c3d8d5d1be4cdb"},"schema_version":"1.0","source":{"id":"2608.05375","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.05375","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"arxiv_version","alias_value":"2608.05375v1","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05375","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_12","alias_value":"ZLP4Y35ILSH3","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_16","alias_value":"ZLP4Y35ILSH3LKLD","created_at":"2026-08-07T00:47:32Z"},{"alias_kind":"pith_short_8","alias_value":"ZLP4Y35I","created_at":"2026-08-07T00:47:32Z"}],"graph_snapshots":[{"event_id":"sha256:41a5bae50d993f16b308daaff6a39bc651cc4dac398e431c070ae4a3f4fca22a","target":"graph","created_at":"2026-08-07T00:47:32Z","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/2608.05375/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constrained by scarce, heterogeneous, and temporal complexity. Developing effective ML pipelines for such data remains time-consuming and error-prone, while existing automated machine learning (AutoML) systems only partially address this challenge because they largely rely on brute-force search over predefined spaces and lack explicit reasoning and memory. We therefore reformulate AutoML for small clinical data from exhaustive search to reasoning-driven refinement. ","authors_text":"Bo-Hong Wang, Elizabeth Kourbatski, Gilles Boire, Hegang Chen, Jun Bai, Marie Hudson, Ruilin Wang, Yue Li, Ziyang Song","cross_cats":["cs.LG","cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-05T19:52:55Z","title":"DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05375","kind":"arxiv","version":1},"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":"sha256:749b192da370cd700c3cf62ef0df34bfa3f2d150ca29f5b5f0ea6fb67e105eb0","target":"record","created_at":"2026-08-07T00:47:32Z","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":"132e7dc5b9968f8918521f66f5002e347c691654de5c1159bba852b804309263","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-05T19:52:55Z","title_canon_sha256":"2af7f6329dffd30aad9db447b5c5f56d58138e4de4f2210281c3d8d5d1be4cdb"},"schema_version":"1.0","source":{"id":"2608.05375","kind":"arxiv","version":1}},"canonical_sha256":"cadfcc6fa85c8fb5a963d5fdc0446fcb5d953b874fe281c651d0b9f45a28b3d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cadfcc6fa85c8fb5a963d5fdc0446fcb5d953b874fe281c651d0b9f45a28b3d6","first_computed_at":"2026-08-07T00:47:32.775331Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-07T00:47:32.775331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2EHYx3nJVA8NfHim4IMBUDWGI2pXE8fDWrcZMjQ+4+pNQJQRFpA7cnG4oQha8RGGHU16PxVecN/KUGWiIE9bCg==","signature_status":"signed_v1","signed_at":"2026-08-07T00:47:32.776707Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.05375","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:749b192da370cd700c3cf62ef0df34bfa3f2d150ca29f5b5f0ea6fb67e105eb0","sha256:41a5bae50d993f16b308daaff6a39bc651cc4dac398e431c070ae4a3f4fca22a"],"state_sha256":"70e2df0c9efa8d63f19defa4adb076e0de0c9ae57ec857ab1e848c8417ab6c7d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WizK0KhUuUj8OaE34TN1VwXgB4UXRPSzR6lyCDMcVk0zTtnikJ9E57CdwMR52tNxYzEGnx2azsRVZMgYWvaCDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:26:20.516058Z","bundle_sha256":"9b15edf63cf5c9c6fe1f85d1877aa324e2ba445df2a0e6c3f071f2eb2e2ba49c"}}