{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KWN2DW7WMKBQU2EISCCCT47WVD","short_pith_number":"pith:KWN2DW7W","canonical_record":{"source":{"id":"2606.24604","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T14:04:27Z","cross_cats_sorted":[],"title_canon_sha256":"8d54e6ead82464f5768a50d1325b0d68a1c5950ad5a06b849bc0fbc7651b3743","abstract_canon_sha256":"25bd544f9dc20f1ed213670996664b85ab7c5b1834dcddd407f4c529a9978eec"},"schema_version":"1.0"},"canonical_sha256":"559ba1dbf662830a6888908429f3f6a8ec9cec999ca89b32786802d0e192d04c","source":{"kind":"arxiv","id":"2606.24604","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24604","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24604v1","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24604","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_12","alias_value":"KWN2DW7WMKBQ","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_16","alias_value":"KWN2DW7WMKBQU2EI","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_8","alias_value":"KWN2DW7W","created_at":"2026-06-24T01:15:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KWN2DW7WMKBQU2EISCCCT47WVD","target":"record","payload":{"canonical_record":{"source":{"id":"2606.24604","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T14:04:27Z","cross_cats_sorted":[],"title_canon_sha256":"8d54e6ead82464f5768a50d1325b0d68a1c5950ad5a06b849bc0fbc7651b3743","abstract_canon_sha256":"25bd544f9dc20f1ed213670996664b85ab7c5b1834dcddd407f4c529a9978eec"},"schema_version":"1.0"},"canonical_sha256":"559ba1dbf662830a6888908429f3f6a8ec9cec999ca89b32786802d0e192d04c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:15:36.772280Z","signature_b64":"0DvOA/zpp2yPA2eA2Fqhn7exhuAM/AFgC1Q4QGHm9EKWzNkc9TPZOa9jj+NGh7ksj7E0PbFKrVFr3RswvtVmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"559ba1dbf662830a6888908429f3f6a8ec9cec999ca89b32786802d0e192d04c","last_reissued_at":"2026-06-24T01:15:36.771887Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:15:36.771887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.24604","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":"2026-06-24T01:15:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OENfHhXigRlToQpRiwHul2Zk8HsB6QWAKgi5rAnwJlQP/WEs05+6B2sxok4RFU89Wdcddj7FeX9ssfnQiSTPAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T12:30:05.834306Z"},"content_sha256":"14b45f9d59d41b790e7c1db7f276568740da80dd56bae82309708ebf93517c3a","schema_version":"1.0","event_id":"sha256:14b45f9d59d41b790e7c1db7f276568740da80dd56bae82309708ebf93517c3a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KWN2DW7WMKBQU2EISCCCT47WVD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anala M R, Arya Hariharan, Shreyank N Gowda","submitted_at":"2026-06-23T14:04:27Z","abstract_excerpt":"Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and deco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24604","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/2606.24604/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":"2026-06-24T01:15:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"haJiQUmiI5mJGHScL4e/FdUm+Ck5rr/Bx2eVTDSbScavLyosRsxXDfgIqt4HxusWU+ZwZNKAfh+Q7ur0hHK7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T12:30:05.834701Z"},"content_sha256":"f75639ae2213296a4552e5ec98da2f25176f402b1b5d4f2c96de8d4b1c604332","schema_version":"1.0","event_id":"sha256:f75639ae2213296a4552e5ec98da2f25176f402b1b5d4f2c96de8d4b1c604332"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KWN2DW7WMKBQU2EISCCCT47WVD/bundle.json","state_url":"https://pith.science/pith/KWN2DW7WMKBQU2EISCCCT47WVD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KWN2DW7WMKBQU2EISCCCT47WVD/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":"2026-06-30T12:30:05Z","links":{"resolver":"https://pith.science/pith/KWN2DW7WMKBQU2EISCCCT47WVD","bundle":"https://pith.science/pith/KWN2DW7WMKBQU2EISCCCT47WVD/bundle.json","state":"https://pith.science/pith/KWN2DW7WMKBQU2EISCCCT47WVD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KWN2DW7WMKBQU2EISCCCT47WVD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KWN2DW7WMKBQU2EISCCCT47WVD","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":"25bd544f9dc20f1ed213670996664b85ab7c5b1834dcddd407f4c529a9978eec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T14:04:27Z","title_canon_sha256":"8d54e6ead82464f5768a50d1325b0d68a1c5950ad5a06b849bc0fbc7651b3743"},"schema_version":"1.0","source":{"id":"2606.24604","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.24604","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"arxiv_version","alias_value":"2606.24604v1","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.24604","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_12","alias_value":"KWN2DW7WMKBQ","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_16","alias_value":"KWN2DW7WMKBQU2EI","created_at":"2026-06-24T01:15:36Z"},{"alias_kind":"pith_short_8","alias_value":"KWN2DW7W","created_at":"2026-06-24T01:15:36Z"}],"graph_snapshots":[{"event_id":"sha256:f75639ae2213296a4552e5ec98da2f25176f402b1b5d4f2c96de8d4b1c604332","target":"graph","created_at":"2026-06-24T01:15:36Z","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/2606.24604/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and deco","authors_text":"Anala M R, Arya Hariharan, Shreyank N Gowda","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T14:04:27Z","title":"Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.24604","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":"sha256:14b45f9d59d41b790e7c1db7f276568740da80dd56bae82309708ebf93517c3a","target":"record","created_at":"2026-06-24T01:15:36Z","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":"25bd544f9dc20f1ed213670996664b85ab7c5b1834dcddd407f4c529a9978eec","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-23T14:04:27Z","title_canon_sha256":"8d54e6ead82464f5768a50d1325b0d68a1c5950ad5a06b849bc0fbc7651b3743"},"schema_version":"1.0","source":{"id":"2606.24604","kind":"arxiv","version":1}},"canonical_sha256":"559ba1dbf662830a6888908429f3f6a8ec9cec999ca89b32786802d0e192d04c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"559ba1dbf662830a6888908429f3f6a8ec9cec999ca89b32786802d0e192d04c","first_computed_at":"2026-06-24T01:15:36.771887Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-24T01:15:36.771887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0DvOA/zpp2yPA2eA2Fqhn7exhuAM/AFgC1Q4QGHm9EKWzNkc9TPZOa9jj+NGh7ksj7E0PbFKrVFr3RswvtVmCA==","signature_status":"signed_v1","signed_at":"2026-06-24T01:15:36.772280Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.24604","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:14b45f9d59d41b790e7c1db7f276568740da80dd56bae82309708ebf93517c3a","sha256:f75639ae2213296a4552e5ec98da2f25176f402b1b5d4f2c96de8d4b1c604332"],"state_sha256":"d650dc346c28fed9cc2a4bbd3535ad5ae31d75cdfed95e66500350a2c5dfe30a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9VIlxQTnDwK1IImwZrsdqS5XTctCpOkMd0AhfwENAkoHrGvXYUfpj/DVP+ya4HRrOUXsyO783wVl8qujkQKvBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T12:30:05.836675Z","bundle_sha256":"bad12600232b1f1a12d9629ef060e9e44cc376ad3588ede941e9160cc7531998"}}