{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CSV2ZIJV5RAKGN4UI2K6NDCPXY","short_pith_number":"pith:CSV2ZIJV","canonical_record":{"source":{"id":"2411.02572","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T20:09:51Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"697cc9eb8b3c79745a883cdb2d4cf2255b906bfcb06f546afbfda4d6ff7f4510","abstract_canon_sha256":"14fd6656bde399c4210e5cb7538e852ecd8cfb3e769f8b0991fbee51f3f5fedf"},"schema_version":"1.0"},"canonical_sha256":"14abaca135ec40a337944695e68c4fbe256dc1f31d221c7ce73d6e4c04a3558b","source":{"kind":"arxiv","id":"2411.02572","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02572","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02572v2","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02572","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_12","alias_value":"CSV2ZIJV5RAK","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_16","alias_value":"CSV2ZIJV5RAKGN4U","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_8","alias_value":"CSV2ZIJV","created_at":"2026-07-05T11:38:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CSV2ZIJV5RAKGN4UI2K6NDCPXY","target":"record","payload":{"canonical_record":{"source":{"id":"2411.02572","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T20:09:51Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"697cc9eb8b3c79745a883cdb2d4cf2255b906bfcb06f546afbfda4d6ff7f4510","abstract_canon_sha256":"14fd6656bde399c4210e5cb7538e852ecd8cfb3e769f8b0991fbee51f3f5fedf"},"schema_version":"1.0"},"canonical_sha256":"14abaca135ec40a337944695e68c4fbe256dc1f31d221c7ce73d6e4c04a3558b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:01.775186Z","signature_b64":"Ks1HNLcndb1Gp+Q2V6QR4DS+yaBTA1+qx5D/PHYHxG4BGM/9CouOXRGAzSUA73JgHcpgxn+Ek3BuF5AKA9HyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14abaca135ec40a337944695e68c4fbe256dc1f31d221c7ce73d6e4c04a3558b","last_reissued_at":"2026-07-05T11:38:01.774755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:01.774755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.02572","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:38:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pYHRUWV0Z9oduSRx0yyJtQFav8ezzPfoGpiDTAyUTshUrc4VZsPvHkAilavMGzM1o4aOGwColpNmubhsekNyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T17:00:48.557284Z"},"content_sha256":"72e91c4c68676938f1c76fd9529e3921360c8260b3a83e8aa3e8c1dcfa5dec44","schema_version":"1.0","event_id":"sha256:72e91c4c68676938f1c76fd9529e3921360c8260b3a83e8aa3e8c1dcfa5dec44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CSV2ZIJV5RAKGN4UI2K6NDCPXY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Ihab Bendidi, Imran S Haque, Jason Hartford, John Urbanik, Juan Sebastian Rodriguez Vera, Kian Kenyon-Dean, Konstantin Donhauser, Marta Fay, Nil Sahin, Oren Kraus, Saber Saberian, Safiye Celik, Zitong Jerry Wang","submitted_at":"2024-11-04T20:09:51Z","abstract_excerpt":"Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02572","kind":"arxiv","version":2},"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/2411.02572/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":"2026-07-05T11:38:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CS/tjY36WDpcnrViowD4N9cZnm0xyHg4NSTiEwd1K9gokvDMrVsjaL7gnWsHV+9EZfhd48Ai5MjrzGkB+JrbAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T17:00:48.557665Z"},"content_sha256":"cbac582f7ee4c495618bd535c2423c83c92d3387f13b2eaa1d834cc0f10a2c12","schema_version":"1.0","event_id":"sha256:cbac582f7ee4c495618bd535c2423c83c92d3387f13b2eaa1d834cc0f10a2c12"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/bundle.json","state_url":"https://pith.science/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/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":"2026-07-13T17:00:48Z","links":{"resolver":"https://pith.science/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY","bundle":"https://pith.science/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/bundle.json","state":"https://pith.science/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CSV2ZIJV5RAKGN4UI2K6NDCPXY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CSV2ZIJV5RAKGN4UI2K6NDCPXY","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":"14fd6656bde399c4210e5cb7538e852ecd8cfb3e769f8b0991fbee51f3f5fedf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T20:09:51Z","title_canon_sha256":"697cc9eb8b3c79745a883cdb2d4cf2255b906bfcb06f546afbfda4d6ff7f4510"},"schema_version":"1.0","source":{"id":"2411.02572","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.02572","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"arxiv_version","alias_value":"2411.02572v2","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02572","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_12","alias_value":"CSV2ZIJV5RAK","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_16","alias_value":"CSV2ZIJV5RAKGN4U","created_at":"2026-07-05T11:38:01Z"},{"alias_kind":"pith_short_8","alias_value":"CSV2ZIJV","created_at":"2026-07-05T11:38:01Z"}],"graph_snapshots":[{"event_id":"sha256:cbac582f7ee4c495618bd535c2423c83c92d3387f13b2eaa1d834cc0f10a2c12","target":"graph","created_at":"2026-07-05T11:38:01Z","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/2411.02572/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, we need models that can map each image into a feature space that represents diverse biological phenotypes consistently, in the sense that perturbations with similar biological effects have similar representations. In this work, we present the largest foundation model for cell microscopy data to date, a new 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion microscopy im","authors_text":"Ihab Bendidi, Imran S Haque, Jason Hartford, John Urbanik, Juan Sebastian Rodriguez Vera, Kian Kenyon-Dean, Konstantin Donhauser, Marta Fay, Nil Sahin, Oren Kraus, Saber Saberian, Safiye Celik, Zitong Jerry Wang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T20:09:51Z","title":"ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02572","kind":"arxiv","version":2},"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":"sha256:72e91c4c68676938f1c76fd9529e3921360c8260b3a83e8aa3e8c1dcfa5dec44","target":"record","created_at":"2026-07-05T11:38:01Z","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":"14fd6656bde399c4210e5cb7538e852ecd8cfb3e769f8b0991fbee51f3f5fedf","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-04T20:09:51Z","title_canon_sha256":"697cc9eb8b3c79745a883cdb2d4cf2255b906bfcb06f546afbfda4d6ff7f4510"},"schema_version":"1.0","source":{"id":"2411.02572","kind":"arxiv","version":2}},"canonical_sha256":"14abaca135ec40a337944695e68c4fbe256dc1f31d221c7ce73d6e4c04a3558b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14abaca135ec40a337944695e68c4fbe256dc1f31d221c7ce73d6e4c04a3558b","first_computed_at":"2026-07-05T11:38:01.774755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:38:01.774755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ks1HNLcndb1Gp+Q2V6QR4DS+yaBTA1+qx5D/PHYHxG4BGM/9CouOXRGAzSUA73JgHcpgxn+Ek3BuF5AKA9HyBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:38:01.775186Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.02572","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72e91c4c68676938f1c76fd9529e3921360c8260b3a83e8aa3e8c1dcfa5dec44","sha256:cbac582f7ee4c495618bd535c2423c83c92d3387f13b2eaa1d834cc0f10a2c12"],"state_sha256":"0bfbd5ea8262fd82449b1e0c44323203c9b08cc7e3c924abc45ebef813ae3676"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0gMLMsVhbBMM6yyLKz88QZZgDpjtp986jhV2ex/zleWm7pTt5aMR6ytfo/k+8xoJfpN3OBRJhddgEdTi3ieRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T17:00:48.559673Z","bundle_sha256":"29170482b17dd8c00f87be75f902d9c1d1421d34fba3dabb54511ff90ee52987"}}