{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PHK4WRJE5CWAN6JYTRUTNN23CB","short_pith_number":"pith:PHK4WRJE","canonical_record":{"source":{"id":"2605.20297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T11:28:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dcf0549bec2890baa16ad140c92aa486a1549b7be53110a901a7f47e0cdc6309","abstract_canon_sha256":"2600234032a4235faee94ea2a223f46ec537447f1bf5882a5e9a928d56ca9b75"},"schema_version":"1.0"},"canonical_sha256":"79d5cb4524e8ac06f9389c6936b75b10490957442d781b05f0e07f551bde68d6","source":{"kind":"arxiv","id":"2605.20297","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.20297","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.20297v1","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20297","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"PHK4WRJE5CWA","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"PHK4WRJE5CWAN6JY","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"PHK4WRJE","created_at":"2026-05-21T00:04:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PHK4WRJE5CWAN6JYTRUTNN23CB","target":"record","payload":{"canonical_record":{"source":{"id":"2605.20297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T11:28:16Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dcf0549bec2890baa16ad140c92aa486a1549b7be53110a901a7f47e0cdc6309","abstract_canon_sha256":"2600234032a4235faee94ea2a223f46ec537447f1bf5882a5e9a928d56ca9b75"},"schema_version":"1.0"},"canonical_sha256":"79d5cb4524e8ac06f9389c6936b75b10490957442d781b05f0e07f551bde68d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T00:04:24.398660Z","signature_b64":"Bwv1VKDwLKovQBRKGrRMvjJkMJF0oZSwwF+FpSP0p5MeZtfeY1RdhkFVIamGqxUUTazefSWaL/ddh+bAjQIZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79d5cb4524e8ac06f9389c6936b75b10490957442d781b05f0e07f551bde68d6","last_reissued_at":"2026-05-21T00:04:24.398079Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T00:04:24.398079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.20297","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":"2026-05-21T00:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ebuNjaU7SMcMJ+lKWWLzRxH+71qO4yXXfBAgoi2MVdtDvFxCO7ACWG6ZrktILJ+i5MZYsEGf92Rx6pFiEZG/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T14:31:22.152647Z"},"content_sha256":"57cde915a5e49c1b81c28f6ce1ba5d947d14be1ca7b19bc2deb34208e460fffd","schema_version":"1.0","event_id":"sha256:57cde915a5e49c1b81c28f6ce1ba5d947d14be1ca7b19bc2deb34208e460fffd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PHK4WRJE5CWAN6JYTRUTNN23CB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ziyuan Gao","submitted_at":"2026-05-19T11:28:16Z","abstract_excerpt":"Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning. Existing methods either apply uniform constraints across all tasks, causing catastrophic forgetting when tasks conflict, or require predefined task groupings that cannot anticipate future task diversity. We introduce MedCRP-CL, a framework that performs online task structure discovery and structure-aware continual learning. Leveraging the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20297","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/2605.20297/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":"2026-05-21T00:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6TWanqSa0AanmLV+fR5RbiDpFvG5/i1S8gzV0WTq4NzgLemuf4EHhMhg6JmKeyvZj3YnUYr3WDL4+cHI+AZ5Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T14:31:22.153514Z"},"content_sha256":"908eaac143512efc5614bd09ecd730a509d838088c1252a8a9ab03650de1f7c0","schema_version":"1.0","event_id":"sha256:908eaac143512efc5614bd09ecd730a509d838088c1252a8a9ab03650de1f7c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/bundle.json","state_url":"https://pith.science/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/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":"2026-05-30T14:31:22Z","links":{"resolver":"https://pith.science/pith/PHK4WRJE5CWAN6JYTRUTNN23CB","bundle":"https://pith.science/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/bundle.json","state":"https://pith.science/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PHK4WRJE5CWAN6JYTRUTNN23CB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PHK4WRJE5CWAN6JYTRUTNN23CB","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":"2600234032a4235faee94ea2a223f46ec537447f1bf5882a5e9a928d56ca9b75","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T11:28:16Z","title_canon_sha256":"dcf0549bec2890baa16ad140c92aa486a1549b7be53110a901a7f47e0cdc6309"},"schema_version":"1.0","source":{"id":"2605.20297","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.20297","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.20297v1","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20297","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"PHK4WRJE5CWA","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"PHK4WRJE5CWAN6JY","created_at":"2026-05-21T00:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"PHK4WRJE","created_at":"2026-05-21T00:04:24Z"}],"graph_snapshots":[{"event_id":"sha256:908eaac143512efc5614bd09ecd730a509d838088c1252a8a9ab03650de1f7c0","target":"graph","created_at":"2026-05-21T00:04:24Z","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/2605.20297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning. Existing methods either apply uniform constraints across all tasks, causing catastrophic forgetting when tasks conflict, or require predefined task groupings that cannot anticipate future task diversity. We introduce MedCRP-CL, a framework that performs online task structure discovery and structure-aware continual learning. Leveraging the ","authors_text":"Ziyuan Gao","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T11:28:16Z","title":"MedCRP-CL: Continual Medical Image Segmentation via Bayesian Nonparametric Semantic Modality Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20297","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":"sha256:57cde915a5e49c1b81c28f6ce1ba5d947d14be1ca7b19bc2deb34208e460fffd","target":"record","created_at":"2026-05-21T00:04:24Z","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":"2600234032a4235faee94ea2a223f46ec537447f1bf5882a5e9a928d56ca9b75","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T11:28:16Z","title_canon_sha256":"dcf0549bec2890baa16ad140c92aa486a1549b7be53110a901a7f47e0cdc6309"},"schema_version":"1.0","source":{"id":"2605.20297","kind":"arxiv","version":1}},"canonical_sha256":"79d5cb4524e8ac06f9389c6936b75b10490957442d781b05f0e07f551bde68d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79d5cb4524e8ac06f9389c6936b75b10490957442d781b05f0e07f551bde68d6","first_computed_at":"2026-05-21T00:04:24.398079Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T00:04:24.398079Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Bwv1VKDwLKovQBRKGrRMvjJkMJF0oZSwwF+FpSP0p5MeZtfeY1RdhkFVIamGqxUUTazefSWaL/ddh+bAjQIZCw==","signature_status":"signed_v1","signed_at":"2026-05-21T00:04:24.398660Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.20297","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57cde915a5e49c1b81c28f6ce1ba5d947d14be1ca7b19bc2deb34208e460fffd","sha256:908eaac143512efc5614bd09ecd730a509d838088c1252a8a9ab03650de1f7c0"],"state_sha256":"ac94bbf1d03890efd8a415b5c538415cf01253da65e7c79fac1f105ab5db56e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KsZyJZy5Cdf40t1ychzpbc74GQuAI5ndx/HYo/REHymqIH4UFei06XIVqCWCjDKIu3fxZqKs6gg9Ck4Myb38Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T14:31:22.158231Z","bundle_sha256":"ac9e9c5082e08504d2faeb404e070b9c262d10ec86d1c451840a9ca8468a2c6f"}}