{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CYBW3MU46WTWBEP25I5LYERVB7","short_pith_number":"pith:CYBW3MU4","canonical_record":{"source":{"id":"2501.18724","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-30T19:58:45Z","cross_cats_sorted":[],"title_canon_sha256":"e6c4ccd9d6593261fe280831ca7d0a338c3221ffed6cfd6301a4afca6287640e","abstract_canon_sha256":"33ec55ea6b4b537329d73994ee8bb3268ca13c817ac0f0585f1bf6c100344a4e"},"schema_version":"1.0"},"canonical_sha256":"16036db29cf5a76091faea3abc12350fe725ecfd56ff68944f66bd919c4b1498","source":{"kind":"arxiv","id":"2501.18724","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.18724","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"2501.18724v3","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18724","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"CYBW3MU46WTW","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_16","alias_value":"CYBW3MU46WTWBEP2","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_8","alias_value":"CYBW3MU4","created_at":"2026-07-05T12:05:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CYBW3MU46WTWBEP25I5LYERVB7","target":"record","payload":{"canonical_record":{"source":{"id":"2501.18724","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-30T19:58:45Z","cross_cats_sorted":[],"title_canon_sha256":"e6c4ccd9d6593261fe280831ca7d0a338c3221ffed6cfd6301a4afca6287640e","abstract_canon_sha256":"33ec55ea6b4b537329d73994ee8bb3268ca13c817ac0f0585f1bf6c100344a4e"},"schema_version":"1.0"},"canonical_sha256":"16036db29cf5a76091faea3abc12350fe725ecfd56ff68944f66bd919c4b1498","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:12.574129Z","signature_b64":"tjdAp56gKQidECFGL4xRdnD5piO7yf0Nl4Unb8Is5w8UnAs0ISz0qH7ARdUsW04QgL6BcmrzEU57KTNLKORSBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16036db29cf5a76091faea3abc12350fe725ecfd56ff68944f66bd919c4b1498","last_reissued_at":"2026-07-05T12:05:12.573563Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:12.573563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.18724","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xYgtB+vNf9YkbcuWKTy98ydEgj+WFMkR5vMAmAlk0UJPHQhR6OOMGwOC4IcBXRDC3RxMl4q5UHSUelmJnNsGAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:53:28.990690Z"},"content_sha256":"f0c91384cd845b12cab16dc5dd3c28ada3bec4cdac6c82e68a0d9c25754bf05e","schema_version":"1.0","event_id":"sha256:f0c91384cd845b12cab16dc5dd3c28ada3bec4cdac6c82e68a0d9c25754bf05e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CYBW3MU46WTWBEP25I5LYERVB7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bingsheng Yao, Dakuo Wang, Elizabeth Goldberg, Maya Kruse, Nicholas Derby, Samantha Stonbraker, Shiyue Hu, Yanjun Gao, Yifu Wu","submitted_at":"2025-01-30T19:58:45Z","abstract_excerpt":"Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-enginee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18724","kind":"arxiv","version":3},"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/2501.18724/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":"2026-07-05T12:05:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yzSkb7nUtfLfNILqjGvZGiZ4vOepqlX6Qucs0M3pDyFUvI7tkCsajHtvLC9sI57MXhtY8bAEr8rt7wPffSrVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T01:53:28.991278Z"},"content_sha256":"8ba3b0fd4726805295ce7f03a20f033e718c8e26604e53bcd5c27c795edad8c6","schema_version":"1.0","event_id":"sha256:8ba3b0fd4726805295ce7f03a20f033e718c8e26604e53bcd5c27c795edad8c6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CYBW3MU46WTWBEP25I5LYERVB7/bundle.json","state_url":"https://pith.science/pith/CYBW3MU46WTWBEP25I5LYERVB7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CYBW3MU46WTWBEP25I5LYERVB7/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":"2026-08-11T01:53:28Z","links":{"resolver":"https://pith.science/pith/CYBW3MU46WTWBEP25I5LYERVB7","bundle":"https://pith.science/pith/CYBW3MU46WTWBEP25I5LYERVB7/bundle.json","state":"https://pith.science/pith/CYBW3MU46WTWBEP25I5LYERVB7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CYBW3MU46WTWBEP25I5LYERVB7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CYBW3MU46WTWBEP25I5LYERVB7","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":"33ec55ea6b4b537329d73994ee8bb3268ca13c817ac0f0585f1bf6c100344a4e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-30T19:58:45Z","title_canon_sha256":"e6c4ccd9d6593261fe280831ca7d0a338c3221ffed6cfd6301a4afca6287640e"},"schema_version":"1.0","source":{"id":"2501.18724","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.18724","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"arxiv_version","alias_value":"2501.18724v3","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18724","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_12","alias_value":"CYBW3MU46WTW","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_16","alias_value":"CYBW3MU46WTWBEP2","created_at":"2026-07-05T12:05:12Z"},{"alias_kind":"pith_short_8","alias_value":"CYBW3MU4","created_at":"2026-07-05T12:05:12Z"}],"graph_snapshots":[{"event_id":"sha256:8ba3b0fd4726805295ce7f03a20f033e718c8e26604e53bcd5c27c795edad8c6","target":"graph","created_at":"2026-07-05T12:05:12Z","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/2501.18724/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-enginee","authors_text":"Bingsheng Yao, Dakuo Wang, Elizabeth Goldberg, Maya Kruse, Nicholas Derby, Samantha Stonbraker, Shiyue Hu, Yanjun Gao, Yifu Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-30T19:58:45Z","title":"Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18724","kind":"arxiv","version":3},"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:f0c91384cd845b12cab16dc5dd3c28ada3bec4cdac6c82e68a0d9c25754bf05e","target":"record","created_at":"2026-07-05T12:05:12Z","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":"33ec55ea6b4b537329d73994ee8bb3268ca13c817ac0f0585f1bf6c100344a4e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-30T19:58:45Z","title_canon_sha256":"e6c4ccd9d6593261fe280831ca7d0a338c3221ffed6cfd6301a4afca6287640e"},"schema_version":"1.0","source":{"id":"2501.18724","kind":"arxiv","version":3}},"canonical_sha256":"16036db29cf5a76091faea3abc12350fe725ecfd56ff68944f66bd919c4b1498","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16036db29cf5a76091faea3abc12350fe725ecfd56ff68944f66bd919c4b1498","first_computed_at":"2026-07-05T12:05:12.573563Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:12.573563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tjdAp56gKQidECFGL4xRdnD5piO7yf0Nl4Unb8Is5w8UnAs0ISz0qH7ARdUsW04QgL6BcmrzEU57KTNLKORSBg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:12.574129Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.18724","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0c91384cd845b12cab16dc5dd3c28ada3bec4cdac6c82e68a0d9c25754bf05e","sha256:8ba3b0fd4726805295ce7f03a20f033e718c8e26604e53bcd5c27c795edad8c6"],"state_sha256":"219a15a5aee4ddbb9f68ea6d136f81b6a3dab294094c995f077163d9bd771043"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VB9PDV4R8INP3zaYS+39WGMF/iMrMaaDjSSPLY67OplgObH3MvkIldDAbj2pTsnfHrisozO2wukYRFKi0EQNDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T01:53:29.001984Z","bundle_sha256":"24bb660720c68eabd21ae03737d1dff535f32c8ebc4cb839d00f2b53c3a691dd"}}