{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:73UL4AXPJ2ZH7FXG5RYAUYWPBG","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":"fa48e72fbf25097809340eab795f5d9ff49ae93f665395d3fadba07c327b5afb","cross_cats_sorted":["cs.AI","cs.CE","cs.DB"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-14T22:08:06Z","title_canon_sha256":"a809e40cbc0a4fbdab72a43ec9b950907a8b01ec33ec4ee3516dfb44e1a064b4"},"schema_version":"1.0","source":{"id":"2508.11090","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.11090","created_at":"2026-07-05T11:54:18Z"},{"alias_kind":"arxiv_version","alias_value":"2508.11090v1","created_at":"2026-07-05T11:54:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.11090","created_at":"2026-07-05T11:54:18Z"},{"alias_kind":"pith_short_12","alias_value":"73UL4AXPJ2ZH","created_at":"2026-07-05T11:54:18Z"},{"alias_kind":"pith_short_16","alias_value":"73UL4AXPJ2ZH7FXG","created_at":"2026-07-05T11:54:18Z"},{"alias_kind":"pith_short_8","alias_value":"73UL4AXP","created_at":"2026-07-05T11:54:18Z"}],"graph_snapshots":[{"event_id":"sha256:af651adb4b31b99a6b58d3ddd8537a81bf47abe91881ebda6c001a5f3b25766b","target":"graph","created_at":"2026-07-05T11:54:18Z","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/2508.11090/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid expansion in the size of new datasets has created a need for fast and efficient parameter-learning techniques. Compressive learning is a framework that enables efficient processing by using random, non-linear features to project large-scale databases onto compact, information-preserving representations whose dimensionality is independent of the number of samples and can be easily stored, transferred, and processed. These database-level summaries are then used to decode parameters of interest from the underlying data distribution without requiring access to the original samples, offer","authors_text":"Alexander G. Ioannidis, Daniel Mas Montserrat, David Bonet, Maria Perera, Xavier Gir\\'o-i-Nieto","cross_cats":["cs.AI","cs.CE","cs.DB"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-14T22:08:06Z","title":"Compressive Meta-Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.11090","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":"sha256:958178aa3ffd44a7574c54d35baa597692787150dc2a51c81470536156c49e5f","target":"record","created_at":"2026-07-05T11:54:18Z","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":"fa48e72fbf25097809340eab795f5d9ff49ae93f665395d3fadba07c327b5afb","cross_cats_sorted":["cs.AI","cs.CE","cs.DB"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-14T22:08:06Z","title_canon_sha256":"a809e40cbc0a4fbdab72a43ec9b950907a8b01ec33ec4ee3516dfb44e1a064b4"},"schema_version":"1.0","source":{"id":"2508.11090","kind":"arxiv","version":1}},"canonical_sha256":"fee8be02ef4eb27f96e6ec700a62cf099864c574377e3643371dc9ac491114bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fee8be02ef4eb27f96e6ec700a62cf099864c574377e3643371dc9ac491114bc","first_computed_at":"2026-07-05T11:54:18.305664Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:54:18.305664Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M1jRrSIUucoBSuAyH1frZdbFsPvIt7CuNYMyfk9Bpg/Yy0w3i5p37uThyoVJGtoa9GFPfttcr+if5J5Z/OqtCg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:54:18.306149Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.11090","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:958178aa3ffd44a7574c54d35baa597692787150dc2a51c81470536156c49e5f","sha256:af651adb4b31b99a6b58d3ddd8537a81bf47abe91881ebda6c001a5f3b25766b"],"state_sha256":"05b528f5591b424e0b75259ff8c37c89dabdeed31780d7f3594a0abece8c0f97"}