{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:S33A4WWHO6G4N5N4TOBLKVVUQW","short_pith_number":"pith:S33A4WWH","canonical_record":{"source":{"id":"2608.05808","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T09:42:13Z","cross_cats_sorted":[],"title_canon_sha256":"1e610d5ab126c24a3a0166da027fe53f08c12befde00f6cfc7993fe4c6e11047","abstract_canon_sha256":"e9cf21f2ad7d3d1375be4fc87a69ecbeee5ec893a99f83a8385455da2c32223c"},"schema_version":"1.0"},"canonical_sha256":"96f60e5ac7778dc6f5bc9b82b556b485bfcfa0dc8c50c0251194b1d953b57732","source":{"kind":"arxiv","id":"2608.05808","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.05808","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"arxiv_version","alias_value":"2608.05808v1","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05808","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_12","alias_value":"S33A4WWHO6G4","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_16","alias_value":"S33A4WWHO6G4N5N4","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_8","alias_value":"S33A4WWH","created_at":"2026-08-07T00:52:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:S33A4WWHO6G4N5N4TOBLKVVUQW","target":"record","payload":{"canonical_record":{"source":{"id":"2608.05808","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T09:42:13Z","cross_cats_sorted":[],"title_canon_sha256":"1e610d5ab126c24a3a0166da027fe53f08c12befde00f6cfc7993fe4c6e11047","abstract_canon_sha256":"e9cf21f2ad7d3d1375be4fc87a69ecbeee5ec893a99f83a8385455da2c32223c"},"schema_version":"1.0"},"canonical_sha256":"96f60e5ac7778dc6f5bc9b82b556b485bfcfa0dc8c50c0251194b1d953b57732","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:52:47.870404Z","signature_b64":"ntX8hNkFzICcqMN2qISqRzmCSQN5HWm2W+XMih5JtGySRl6OdSDHB4L0MLHVyuyJ6kfI7cgrOg4k/KVTqN9wBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96f60e5ac7778dc6f5bc9b82b556b485bfcfa0dc8c50c0251194b1d953b57732","last_reissued_at":"2026-08-07T00:52:47.868997Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:52:47.868997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2608.05808","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":"2026-08-07T00:52:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"39OE/N6yEZqkaWgiIsYCBe3LpIfmEiMQvEw422JIFsf2ZzYDyNhANjYeeHyo7z9kXpzNMuJJ0YYrmIyVt5bVBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:53:23.827499Z"},"content_sha256":"01e9b35d119d54a84d4438009ef6f9ada3293ecfdb5b404460794d6a5ba89a18","schema_version":"1.0","event_id":"sha256:01e9b35d119d54a84d4438009ef6f9ada3293ecfdb5b404460794d6a5ba89a18"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:S33A4WWHO6G4N5N4TOBLKVVUQW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanxing Chen, Jiayu Qian, Kay Chen Tan, Shijun Li, Shiqi Wang, Songpan Gao, Xiaowei Zhu, Yajie Zhang, Yao Hu, Yu-An Huang, Zhenzhen Liu, Zhi-An Huang","submitted_at":"2026-08-06T09:42:13Z","abstract_excerpt":"Deep learning models applied to medical image analysis suffer from severe catastrophic forgetting when continually adapting to new clinical tasks in dynamic environments. Mainstream incremental learning methods typically mitigate this by rehearsing raw historical images. However, this pixel-level rehearsal incurs significant storage overhead, raises privacy concerns, and fails to adequately capture the true data distribution with sparse exemplars. Inspired by human cognitive mechanisms, we propose a novel framework termed Semantic Text-Anchored Incremental Learning (STAIL) for sequential clini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05808","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/2608.05808/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":"2026-08-07T00:52:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c2bpLlo+vYVIt6VNXsaCUypYFTuoCjy9MKILDKcmy9/VN6OdzoDHjYbHckJIJm/xZxiLs4gdc21oz+p9+QFVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:53:23.828664Z"},"content_sha256":"531cdd425c9c1263d475614ed70a0dffdd17e93cb9ad2d7eff108aa4a2fed338","schema_version":"1.0","event_id":"sha256:531cdd425c9c1263d475614ed70a0dffdd17e93cb9ad2d7eff108aa4a2fed338"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:S33A4WWHO6G4N5N4TOBLKVVUQW","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1016/j.media.2025.103581 resolves to 'CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis'. A reader following the printed text alone cannot reach it.","snippet":"Zhang, Y., Huang, Y.A., Hu, Y., Liu, R., Wu, J., Huang, Z.A., Tan, K.C.,2025b. CausalMixNet:Amixed-attentionframeworkforcausal intervention in robust medical image diagnosis. Medical Image Analysis 103, 103581. doi:https://doi.org/10.1016/j","arxiv_id":"2608.05808","detector":"doi_compliance","evidence":{"ref_index":53,"verdict_class":"incontrovertible","resolved_title":"CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis","printed_excerpt":"10.1016/j.media.2025","reconstructed_doi":"10.1016/j.media.2025.103581"},"severity":"advisory","ref_index":53,"audited_at":"2026-08-07T23:18:10.360673Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.media.2025.103581","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"2de210723b550cd1f9603f008d5265210d8e1a5aee79fc45791bff27ebce5425","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"CausalMixNet: A mixed-attention framework for causal intervention in robust medical image diagnosis","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":18565,"payload_sha256":"cbb110c12723d6324864240310a7db5805c0bcf9fa9a786f6b874c19697d13f6","signature_b64":"zW5GuO7NgAtGvRjX22S+HBRtxWeKr4Cyl2gkUnKjj1snKas8cwcPxG4sTh71zA1FTyQFdvlxeBIy+f+UzmzsDA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-08-07T23:18:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CfypYQnKkawLzlG/WvD2Do9ci6dIwapiu06DjManR7FggzFDXP0+gzgHOnGx02WGdFvY+gm+4w7iPH5cnnEQBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T22:53:23.833092Z"},"content_sha256":"5f82b7392e76ca89fe72c23ddaca5264e2e325a7553f85f41288d9db32c24551","schema_version":"1.0","event_id":"sha256:5f82b7392e76ca89fe72c23ddaca5264e2e325a7553f85f41288d9db32c24551"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/bundle.json","state_url":"https://pith.science/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/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":"2026-08-09T22:53:23Z","links":{"resolver":"https://pith.science/pith/S33A4WWHO6G4N5N4TOBLKVVUQW","bundle":"https://pith.science/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/bundle.json","state":"https://pith.science/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S33A4WWHO6G4N5N4TOBLKVVUQW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:S33A4WWHO6G4N5N4TOBLKVVUQW","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e9cf21f2ad7d3d1375be4fc87a69ecbeee5ec893a99f83a8385455da2c32223c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T09:42:13Z","title_canon_sha256":"1e610d5ab126c24a3a0166da027fe53f08c12befde00f6cfc7993fe4c6e11047"},"schema_version":"1.0","source":{"id":"2608.05808","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2608.05808","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"arxiv_version","alias_value":"2608.05808v1","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05808","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_12","alias_value":"S33A4WWHO6G4","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_16","alias_value":"S33A4WWHO6G4N5N4","created_at":"2026-08-07T00:52:47Z"},{"alias_kind":"pith_short_8","alias_value":"S33A4WWH","created_at":"2026-08-07T00:52:47Z"}],"graph_snapshots":[{"event_id":"sha256:531cdd425c9c1263d475614ed70a0dffdd17e93cb9ad2d7eff108aa4a2fed338","target":"graph","created_at":"2026-08-07T00:52:47Z","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/2608.05808/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models applied to medical image analysis suffer from severe catastrophic forgetting when continually adapting to new clinical tasks in dynamic environments. Mainstream incremental learning methods typically mitigate this by rehearsing raw historical images. However, this pixel-level rehearsal incurs significant storage overhead, raises privacy concerns, and fails to adequately capture the true data distribution with sparse exemplars. Inspired by human cognitive mechanisms, we propose a novel framework termed Semantic Text-Anchored Incremental Learning (STAIL) for sequential clini","authors_text":"Guanxing Chen, Jiayu Qian, Kay Chen Tan, Shijun Li, Shiqi Wang, Songpan Gao, Xiaowei Zhu, Yajie Zhang, Yao Hu, Yu-An Huang, Zhenzhen Liu, Zhi-An Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T09:42:13Z","title":"STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05808","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":"sha256:01e9b35d119d54a84d4438009ef6f9ada3293ecfdb5b404460794d6a5ba89a18","target":"record","created_at":"2026-08-07T00:52:47Z","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":"e9cf21f2ad7d3d1375be4fc87a69ecbeee5ec893a99f83a8385455da2c32223c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T09:42:13Z","title_canon_sha256":"1e610d5ab126c24a3a0166da027fe53f08c12befde00f6cfc7993fe4c6e11047"},"schema_version":"1.0","source":{"id":"2608.05808","kind":"arxiv","version":1}},"canonical_sha256":"96f60e5ac7778dc6f5bc9b82b556b485bfcfa0dc8c50c0251194b1d953b57732","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"96f60e5ac7778dc6f5bc9b82b556b485bfcfa0dc8c50c0251194b1d953b57732","first_computed_at":"2026-08-07T00:52:47.868997Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-08-07T00:52:47.868997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ntX8hNkFzICcqMN2qISqRzmCSQN5HWm2W+XMih5JtGySRl6OdSDHB4L0MLHVyuyJ6kfI7cgrOg4k/KVTqN9wBg==","signature_status":"signed_v1","signed_at":"2026-08-07T00:52:47.870404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2608.05808","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01e9b35d119d54a84d4438009ef6f9ada3293ecfdb5b404460794d6a5ba89a18","sha256:531cdd425c9c1263d475614ed70a0dffdd17e93cb9ad2d7eff108aa4a2fed338","sha256:5f82b7392e76ca89fe72c23ddaca5264e2e325a7553f85f41288d9db32c24551"],"state_sha256":"3d8d6b74c90ad9b47b39c698350996a97395e828979c9d88165b39f02dae0ba1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EuZ5qwZkUm1FfKgeHkec4F0vNqdy3+KBJm9ll7dUCLXwZgDkb+f+vvcxPnFSiaCVUMBe7JsNc7fuxdpMbdoyAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T22:53:23.835918Z","bundle_sha256":"cd9590354bd88cb6b7b824ae2143cc07e3729c2c350ef3e3d58c3f5e81c490ba"}}