{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MQXJJX752MQ4JJV4XZ6JBODAOX","short_pith_number":"pith:MQXJJX75","canonical_record":{"source":{"id":"2506.12161","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-11T12:48:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4909fffffb3b4cc10aa9626e014335d1f131bac9e1dc1194828f148e852a2ae1","abstract_canon_sha256":"47c709d2b180f47ba50f08b79dfaadb43213766f50a50d69de8cbabb364f6340"},"schema_version":"1.0"},"canonical_sha256":"642e94dffdd321c4a6bcbe7c90b86075d16c5161748575de23da124fa8740c10","source":{"kind":"arxiv","id":"2506.12161","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12161","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12161v1","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12161","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_12","alias_value":"MQXJJX752MQ4","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_16","alias_value":"MQXJJX752MQ4JJV4","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_8","alias_value":"MQXJJX75","created_at":"2026-07-05T11:21:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MQXJJX752MQ4JJV4XZ6JBODAOX","target":"record","payload":{"canonical_record":{"source":{"id":"2506.12161","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-11T12:48:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4909fffffb3b4cc10aa9626e014335d1f131bac9e1dc1194828f148e852a2ae1","abstract_canon_sha256":"47c709d2b180f47ba50f08b79dfaadb43213766f50a50d69de8cbabb364f6340"},"schema_version":"1.0"},"canonical_sha256":"642e94dffdd321c4a6bcbe7c90b86075d16c5161748575de23da124fa8740c10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:22.298164Z","signature_b64":"+Evp/PpiRPcaYnRFUVvZKf/rHpzX7DLQ+p97KWgBXucQg199jC4A1HNHEfFgbgTc9sq5EvOskj4qJ/R7xG5qBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"642e94dffdd321c4a6bcbe7c90b86075d16c5161748575de23da124fa8740c10","last_reissued_at":"2026-07-05T11:21:22.297739Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:22.297739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.12161","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":"2026-07-05T11:21:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2R2O27GagzUTQVAPl+wIY/4WGYXzYQ7G/QW2qcOOKos1s9IUqE9/nuZS+yHiMSNDwWS6VNr/cE9n0CcU3yJRAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:39:55.711034Z"},"content_sha256":"1365209feadd74b2f12969cea0d2705101cb7f5dbfacf432f3b6f2baa8f88a6e","schema_version":"1.0","event_id":"sha256:1365209feadd74b2f12969cea0d2705101cb7f5dbfacf432f3b6f2baa8f88a6e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MQXJJX752MQ4JJV4XZ6JBODAOX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fabio Ferreira","submitted_at":"2025-06-11T12:48:45Z","abstract_excerpt":"The growing number of pretrained models in Machine Learning (ML) presents significant challenges for practitioners. Given a new dataset, they need to determine the most suitable deep learning (DL) pipeline, consisting of the pretrained model and the hyperparameters for finetuning to it. Moreover, as models grow in scale, the increasing reliance on real-world data poses a bottleneck for training and requires leveraging data more effectively. Addressing the first challenge often involves manual model selection and hyperparameter tuning. At the same time, as models grow larger and more and more o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12161","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/2506.12161/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":"2026-07-05T11:21:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YZUSc3whTcS9tySmMwVSVGEUMT+Iz2hF/33ADEnScc6JRl479hgNnjmYDEp1OCTnvpUr+Vf7VKAARnmSkcayBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:39:55.711572Z"},"content_sha256":"a0b3aee5b91029bf5126803e7110bcff1fb407bb889bc6b0532c919d4fe69f98","schema_version":"1.0","event_id":"sha256:a0b3aee5b91029bf5126803e7110bcff1fb407bb889bc6b0532c919d4fe69f98"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/bundle.json","state_url":"https://pith.science/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/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":"2026-08-10T05:39:55Z","links":{"resolver":"https://pith.science/pith/MQXJJX752MQ4JJV4XZ6JBODAOX","bundle":"https://pith.science/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/bundle.json","state":"https://pith.science/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MQXJJX752MQ4JJV4XZ6JBODAOX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MQXJJX752MQ4JJV4XZ6JBODAOX","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":"47c709d2b180f47ba50f08b79dfaadb43213766f50a50d69de8cbabb364f6340","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-11T12:48:45Z","title_canon_sha256":"4909fffffb3b4cc10aa9626e014335d1f131bac9e1dc1194828f148e852a2ae1"},"schema_version":"1.0","source":{"id":"2506.12161","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12161","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12161v1","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12161","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_12","alias_value":"MQXJJX752MQ4","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_16","alias_value":"MQXJJX752MQ4JJV4","created_at":"2026-07-05T11:21:22Z"},{"alias_kind":"pith_short_8","alias_value":"MQXJJX75","created_at":"2026-07-05T11:21:22Z"}],"graph_snapshots":[{"event_id":"sha256:a0b3aee5b91029bf5126803e7110bcff1fb407bb889bc6b0532c919d4fe69f98","target":"graph","created_at":"2026-07-05T11:21:22Z","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/2506.12161/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The growing number of pretrained models in Machine Learning (ML) presents significant challenges for practitioners. Given a new dataset, they need to determine the most suitable deep learning (DL) pipeline, consisting of the pretrained model and the hyperparameters for finetuning to it. Moreover, as models grow in scale, the increasing reliance on real-world data poses a bottleneck for training and requires leveraging data more effectively. Addressing the first challenge often involves manual model selection and hyperparameter tuning. At the same time, as models grow larger and more and more o","authors_text":"Fabio Ferreira","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-11T12:48:45Z","title":"Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12161","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":"sha256:1365209feadd74b2f12969cea0d2705101cb7f5dbfacf432f3b6f2baa8f88a6e","target":"record","created_at":"2026-07-05T11:21:22Z","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":"47c709d2b180f47ba50f08b79dfaadb43213766f50a50d69de8cbabb364f6340","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-11T12:48:45Z","title_canon_sha256":"4909fffffb3b4cc10aa9626e014335d1f131bac9e1dc1194828f148e852a2ae1"},"schema_version":"1.0","source":{"id":"2506.12161","kind":"arxiv","version":1}},"canonical_sha256":"642e94dffdd321c4a6bcbe7c90b86075d16c5161748575de23da124fa8740c10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"642e94dffdd321c4a6bcbe7c90b86075d16c5161748575de23da124fa8740c10","first_computed_at":"2026-07-05T11:21:22.297739Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:22.297739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Evp/PpiRPcaYnRFUVvZKf/rHpzX7DLQ+p97KWgBXucQg199jC4A1HNHEfFgbgTc9sq5EvOskj4qJ/R7xG5qBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:22.298164Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.12161","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1365209feadd74b2f12969cea0d2705101cb7f5dbfacf432f3b6f2baa8f88a6e","sha256:a0b3aee5b91029bf5126803e7110bcff1fb407bb889bc6b0532c919d4fe69f98"],"state_sha256":"ad96654afac48b37cee536e6e99b54505b6fd05ff0ccfbda4c43b124c1281415"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cAthpvvjObC69VfjQ5TVFz/tzjKZ4+5KkIrr3k7lJzc9fuNaGxogyHIwNzRVqGVUT4P7iYILJPmKcaVGhUFgCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:39:55.715192Z","bundle_sha256":"398986b63a12c0dd009b1d44fd2696decab93341feddc2e0387350f299f22a4a"}}