{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:S4HKZEL5RF2OMGZSDUHKF77ONN","short_pith_number":"pith:S4HKZEL5","canonical_record":{"source":{"id":"2605.15016","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T16:17:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"50c7407755db2b9d7b8552138f5f2eaeaeed71bdabd267651e9949c9e4cb2c27","abstract_canon_sha256":"e09e42757a0d33bfb618c416caf24c9898e36ee6b5a7455d27997ce945e169a7"},"schema_version":"1.0"},"canonical_sha256":"970eac917d8974e61b321d0ea2ffee6b7833305ba756de9447f9a4784cb1510e","source":{"kind":"arxiv","id":"2605.15016","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15016","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15016v1","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15016","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"pith_short_12","alias_value":"S4HKZEL5RF2O","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"S4HKZEL5RF2OMGZS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"S4HKZEL5","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:S4HKZEL5RF2OMGZSDUHKF77ONN","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15016","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T16:17:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"50c7407755db2b9d7b8552138f5f2eaeaeed71bdabd267651e9949c9e4cb2c27","abstract_canon_sha256":"e09e42757a0d33bfb618c416caf24c9898e36ee6b5a7455d27997ce945e169a7"},"schema_version":"1.0"},"canonical_sha256":"970eac917d8974e61b321d0ea2ffee6b7833305ba756de9447f9a4784cb1510e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:54.726542Z","signature_b64":"NiQY6P2nnDXJP+AfaICnhz/l8opoYDhuLsPTdH2uNmKLJZI6DZM2r53Ed7aJPnvHbu8/11w89S5vUDcpSDpTAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"970eac917d8974e61b321d0ea2ffee6b7833305ba756de9447f9a4784cb1510e","last_reissued_at":"2026-05-17T23:38:54.725938Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:54.725938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15016","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":"2026-05-17T23:38:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c5f9QYQAkItEtHS+1UlN44i/tUpIkFJ1lVxcEDJe1R9rLzkHjBrlKYUt603Qa+7JUgLWMsMc4CRt4jq/AQo0AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T11:23:03.757451Z"},"content_sha256":"7de9bb43b1d4527e3027cb1794ce5aa09608b74affa3735a2f853d047b729a31","schema_version":"1.0","event_id":"sha256:7de9bb43b1d4527e3027cb1794ce5aa09608b74affa3735a2f853d047b729a31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:S4HKZEL5RF2OMGZSDUHKF77ONN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chuanzhi Xu, Xiaozhen Zhong, Zihan Deng","submitted_at":"2026-05-14T16:17:35Z","abstract_excerpt":"As large language models empower healthcare, intelligent clinical decision support has developed rapidly. Longitudinal electronic health records (EHR) provide essential temporal evidence for accurate clinical diagnosis and analysis. However, current large language models have critical flaws in longitudinal EHR reasoning. First, lacking fine-grained statistical reasoning, they often hallucinate clinical trends and metrics when quantitative evidence is textually implied, biasing diagnostic inference. Second, non-uniform time series and scarce labels in longitudinal EHR hinder models from capturi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15016","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":""},"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":"2026-05-17T23:38:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZHnxIntrfXUk3XxE0zB8+nT4sy0X4/lmU4Q8qOt3TEVl0i73+MjTIBtUNnjuORZj6Dqqr+pSe5JCtg/+Cv9zCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T11:23:03.757807Z"},"content_sha256":"8bcc3c4cd49ad350898895243e513358025a4159e9f2d24b7df690bd9d1591f7","schema_version":"1.0","event_id":"sha256:8bcc3c4cd49ad350898895243e513358025a4159e9f2d24b7df690bd9d1591f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/bundle.json","state_url":"https://pith.science/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/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":"2026-06-08T11:23:03Z","links":{"resolver":"https://pith.science/pith/S4HKZEL5RF2OMGZSDUHKF77ONN","bundle":"https://pith.science/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/bundle.json","state":"https://pith.science/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S4HKZEL5RF2OMGZSDUHKF77ONN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:S4HKZEL5RF2OMGZSDUHKF77ONN","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":"e09e42757a0d33bfb618c416caf24c9898e36ee6b5a7455d27997ce945e169a7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T16:17:35Z","title_canon_sha256":"50c7407755db2b9d7b8552138f5f2eaeaeed71bdabd267651e9949c9e4cb2c27"},"schema_version":"1.0","source":{"id":"2605.15016","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15016","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15016v1","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15016","created_at":"2026-05-17T23:38:54Z"},{"alias_kind":"pith_short_12","alias_value":"S4HKZEL5RF2O","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"S4HKZEL5RF2OMGZS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"S4HKZEL5","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:8bcc3c4cd49ad350898895243e513358025a4159e9f2d24b7df690bd9d1591f7","target":"graph","created_at":"2026-05-17T23:38:54Z","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"},"paper":{"abstract_excerpt":"As large language models empower healthcare, intelligent clinical decision support has developed rapidly. Longitudinal electronic health records (EHR) provide essential temporal evidence for accurate clinical diagnosis and analysis. However, current large language models have critical flaws in longitudinal EHR reasoning. First, lacking fine-grained statistical reasoning, they often hallucinate clinical trends and metrics when quantitative evidence is textually implied, biasing diagnostic inference. Second, non-uniform time series and scarce labels in longitudinal EHR hinder models from capturi","authors_text":"Chuanzhi Xu, Xiaozhen Zhong, Zihan Deng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T16:17:35Z","title":"COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15016","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":"sha256:7de9bb43b1d4527e3027cb1794ce5aa09608b74affa3735a2f853d047b729a31","target":"record","created_at":"2026-05-17T23:38:54Z","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":"e09e42757a0d33bfb618c416caf24c9898e36ee6b5a7455d27997ce945e169a7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-14T16:17:35Z","title_canon_sha256":"50c7407755db2b9d7b8552138f5f2eaeaeed71bdabd267651e9949c9e4cb2c27"},"schema_version":"1.0","source":{"id":"2605.15016","kind":"arxiv","version":1}},"canonical_sha256":"970eac917d8974e61b321d0ea2ffee6b7833305ba756de9447f9a4784cb1510e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"970eac917d8974e61b321d0ea2ffee6b7833305ba756de9447f9a4784cb1510e","first_computed_at":"2026-05-17T23:38:54.725938Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:54.725938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NiQY6P2nnDXJP+AfaICnhz/l8opoYDhuLsPTdH2uNmKLJZI6DZM2r53Ed7aJPnvHbu8/11w89S5vUDcpSDpTAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:54.726542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15016","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7de9bb43b1d4527e3027cb1794ce5aa09608b74affa3735a2f853d047b729a31","sha256:8bcc3c4cd49ad350898895243e513358025a4159e9f2d24b7df690bd9d1591f7"],"state_sha256":"ecd040884cdb042a038eb76b883469c9114acd4d2da59691fd6559e0465bd168"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XipEHtLAVaPsGys+gKn2JHm/BEQX/2hgVJmVuOtZoz8PUgC0Bnxr3vmH/cKhKShKPyvffB3XIh0230h7QW0gCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T11:23:03.759624Z","bundle_sha256":"9cbbdc37b8899dfc4e0adbf0d26ba5ee0bfeaf1b71ada7f3b8494a10db356634"}}