{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XOB7X6IEZUZRZLVIYOT5L2XAZG","short_pith_number":"pith:XOB7X6IE","canonical_record":{"source":{"id":"2408.07107","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-13T10:28:54Z","cross_cats_sorted":[],"title_canon_sha256":"6efdcae068ad5a14669adb3e942354c4f4092c2ad7141b856b689e67dc4705e7","abstract_canon_sha256":"845feb239173e63ec3a8e3f0a05f168e42a949605f32442acb8a7c6a2ad4563b"},"schema_version":"1.0"},"canonical_sha256":"bb83fbf904cd331caea8c3a7d5eae0c9bb4e40cb401e9136717e4d17f143fa41","source":{"kind":"arxiv","id":"2408.07107","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.07107","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"arxiv_version","alias_value":"2408.07107v4","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.07107","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_12","alias_value":"XOB7X6IEZUZR","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_16","alias_value":"XOB7X6IEZUZRZLVI","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_8","alias_value":"XOB7X6IE","created_at":"2026-07-05T10:45:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XOB7X6IEZUZRZLVIYOT5L2XAZG","target":"record","payload":{"canonical_record":{"source":{"id":"2408.07107","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-13T10:28:54Z","cross_cats_sorted":[],"title_canon_sha256":"6efdcae068ad5a14669adb3e942354c4f4092c2ad7141b856b689e67dc4705e7","abstract_canon_sha256":"845feb239173e63ec3a8e3f0a05f168e42a949605f32442acb8a7c6a2ad4563b"},"schema_version":"1.0"},"canonical_sha256":"bb83fbf904cd331caea8c3a7d5eae0c9bb4e40cb401e9136717e4d17f143fa41","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:10.753715Z","signature_b64":"1T2H0zgxXEo9juV0/RPB3+5aTj6mPCcKq8+xLIipgHLLeJEGFL8nRbbCcTVBCFux1Z2hv/grgup05oQjr6C+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb83fbf904cd331caea8c3a7d5eae0c9bb4e40cb401e9136717e4d17f143fa41","last_reissued_at":"2026-07-05T10:45:10.753277Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:10.753277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.07107","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:45:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kf1cq0u+FM1f9vMKnjeYxq0xzLeJQtSl0/9xN+CgscYfGz6zo9iemKGL/0DwTOyWWXF07khMbNd+JCaAVlYRCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:44:06.211060Z"},"content_sha256":"cb76d492b20a7eddfe52bb77294b475e7b29d9021390926f5c365cfea9a30dc3","schema_version":"1.0","event_id":"sha256:cb76d492b20a7eddfe52bb77294b475e7b29d9021390926f5c365cfea9a30dc3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XOB7X6IEZUZRZLVIYOT5L2XAZG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Self-Supervised Paradigm for Data-Efficient Medical Foundation Model Pre-training: V-information Optimization Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Yan, Hanyu Zhang, Weimin Tan, Wenxuan Yang, Yuqi Sun","submitted_at":"2024-08-13T10:28:54Z","abstract_excerpt":"Self-supervised pre-training medical foundation models on large-scale datasets demonstrate exceptional performance. Recent research challenges this common paradigm by introducing data-effective learning approaches, demonstrating that merely increasing pre-training data volume does not necessarily improve model performance. However, current methods still have unclear standards and the underlying theoretical foundation remains unknown. In this paper, as the first attempt to address this limitation, we introduce V-information into self-supervised pre-training of foundation models to provide a the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.07107","kind":"arxiv","version":4},"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/2408.07107/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":"2026-07-05T10:45:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NLGrl8eQKh09SbUyw5Sf+W7dZBOcAN5ZyS7E5rxXB5edGZ7wIuSTdbQQHiA5i/iZc8dcg8QCdqtgKGnne7PJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:44:06.211667Z"},"content_sha256":"27e289daf9bcf26e5ca363c57897009a3d45433cb82c88edcf0c990a6572c063","schema_version":"1.0","event_id":"sha256:27e289daf9bcf26e5ca363c57897009a3d45433cb82c88edcf0c990a6572c063"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/bundle.json","state_url":"https://pith.science/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/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":"2026-08-18T21:44:06Z","links":{"resolver":"https://pith.science/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG","bundle":"https://pith.science/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/bundle.json","state":"https://pith.science/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XOB7X6IEZUZRZLVIYOT5L2XAZG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XOB7X6IEZUZRZLVIYOT5L2XAZG","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":"845feb239173e63ec3a8e3f0a05f168e42a949605f32442acb8a7c6a2ad4563b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-13T10:28:54Z","title_canon_sha256":"6efdcae068ad5a14669adb3e942354c4f4092c2ad7141b856b689e67dc4705e7"},"schema_version":"1.0","source":{"id":"2408.07107","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.07107","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"arxiv_version","alias_value":"2408.07107v4","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.07107","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_12","alias_value":"XOB7X6IEZUZR","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_16","alias_value":"XOB7X6IEZUZRZLVI","created_at":"2026-07-05T10:45:10Z"},{"alias_kind":"pith_short_8","alias_value":"XOB7X6IE","created_at":"2026-07-05T10:45:10Z"}],"graph_snapshots":[{"event_id":"sha256:27e289daf9bcf26e5ca363c57897009a3d45433cb82c88edcf0c990a6572c063","target":"graph","created_at":"2026-07-05T10:45:10Z","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/2408.07107/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised pre-training medical foundation models on large-scale datasets demonstrate exceptional performance. Recent research challenges this common paradigm by introducing data-effective learning approaches, demonstrating that merely increasing pre-training data volume does not necessarily improve model performance. However, current methods still have unclear standards and the underlying theoretical foundation remains unknown. In this paper, as the first attempt to address this limitation, we introduce V-information into self-supervised pre-training of foundation models to provide a the","authors_text":"Bo Yan, Hanyu Zhang, Weimin Tan, Wenxuan Yang, Yuqi Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-13T10:28:54Z","title":"A Self-Supervised Paradigm for Data-Efficient Medical Foundation Model Pre-training: V-information Optimization Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.07107","kind":"arxiv","version":4},"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:cb76d492b20a7eddfe52bb77294b475e7b29d9021390926f5c365cfea9a30dc3","target":"record","created_at":"2026-07-05T10:45:10Z","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":"845feb239173e63ec3a8e3f0a05f168e42a949605f32442acb8a7c6a2ad4563b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-13T10:28:54Z","title_canon_sha256":"6efdcae068ad5a14669adb3e942354c4f4092c2ad7141b856b689e67dc4705e7"},"schema_version":"1.0","source":{"id":"2408.07107","kind":"arxiv","version":4}},"canonical_sha256":"bb83fbf904cd331caea8c3a7d5eae0c9bb4e40cb401e9136717e4d17f143fa41","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb83fbf904cd331caea8c3a7d5eae0c9bb4e40cb401e9136717e4d17f143fa41","first_computed_at":"2026-07-05T10:45:10.753277Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:45:10.753277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1T2H0zgxXEo9juV0/RPB3+5aTj6mPCcKq8+xLIipgHLLeJEGFL8nRbbCcTVBCFux1Z2hv/grgup05oQjr6C+Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:45:10.753715Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.07107","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb76d492b20a7eddfe52bb77294b475e7b29d9021390926f5c365cfea9a30dc3","sha256:27e289daf9bcf26e5ca363c57897009a3d45433cb82c88edcf0c990a6572c063"],"state_sha256":"80b620fe6620f345bd5a7044c60e59c2ea537f5267255e8a5fc97d02bd693852"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kb+luvFHGhso93lRuC/odTSbwn2vsmjnkO3pKNHRRJ/CsXCKrxn4Xa1WbPRlBcXov/cWw5f3oWmcBglE9ubdAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:44:06.216719Z","bundle_sha256":"882b9c3d78a10e17d0ee02e4b6415ec356d8ed32678365e5072f55a728322f1b"}}