{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W6IQLIMNUE6ZYRL5H4VRZHLWAL","short_pith_number":"pith:W6IQLIMN","schema_version":"1.0","canonical_sha256":"b79105a18da13d9c457d3f2b1c9d7602dbb4f7764125813a5f3d779913010539","source":{"kind":"arxiv","id":"2407.02461","version":5},"attestation_state":"computed","paper":{"title":"Decentralized Intelligence Network (DIN)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","cs.DC","cs.ET","cs.LG"],"primary_cat":"cs.CR","authors_text":"Abraham Nash","submitted_at":"2024-07-02T17:40:06Z","abstract_excerpt":"Decentralized Intelligence Network (DIN) is a theoretical framework designed to address challenges in AI development, particularly focusing on data fragmentation and siloing issues. It facilitates effective AI training within sovereign data networks by overcoming barriers to accessing diverse data sources, leveraging: 1) personal data stores to ensure data sovereignty, where data remains securely within Participants' control; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where only model parameter updates are shared, keeping data wi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.02461","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-07-02T17:40:06Z","cross_cats_sorted":["cs.CY","cs.DC","cs.ET","cs.LG"],"title_canon_sha256":"5e6f257a2c9dcdd781b8af4f8d1a50532b9991bb52b5f2aba9e953effe04ccbc","abstract_canon_sha256":"6ce8571be65fdba65f18a22792ee34532ba0da3ff04e6321fdc2ce277c86599e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:01.278567Z","signature_b64":"VIvwlQcDMLtbkdQolUtzdBNXsGrA+wgpNYAmR+RM7xJMH6hyt6Kp87CmPM+vDvjoRP33pn6bWeWDXdkEGPaQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b79105a18da13d9c457d3f2b1c9d7602dbb4f7764125813a5f3d779913010539","last_reissued_at":"2026-07-05T09:03:01.278147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:01.278147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decentralized Intelligence Network (DIN)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY","cs.DC","cs.ET","cs.LG"],"primary_cat":"cs.CR","authors_text":"Abraham Nash","submitted_at":"2024-07-02T17:40:06Z","abstract_excerpt":"Decentralized Intelligence Network (DIN) is a theoretical framework designed to address challenges in AI development, particularly focusing on data fragmentation and siloing issues. It facilitates effective AI training within sovereign data networks by overcoming barriers to accessing diverse data sources, leveraging: 1) personal data stores to ensure data sovereignty, where data remains securely within Participants' control; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where only model parameter updates are shared, keeping data wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02461","kind":"arxiv","version":5},"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/2407.02461/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.02461","created_at":"2026-07-05T09:03:01.278209+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.02461v5","created_at":"2026-07-05T09:03:01.278209+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02461","created_at":"2026-07-05T09:03:01.278209+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6IQLIMNUE6Z","created_at":"2026-07-05T09:03:01.278209+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6IQLIMNUE6ZYRL5","created_at":"2026-07-05T09:03:01.278209+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6IQLIMN","created_at":"2026-07-05T09:03:01.278209+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09335","citing_title":"Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL","json":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL.json","graph_json":"https://pith.science/api/pith-number/W6IQLIMNUE6ZYRL5H4VRZHLWAL/graph.json","events_json":"https://pith.science/api/pith-number/W6IQLIMNUE6ZYRL5H4VRZHLWAL/events.json","paper":"https://pith.science/paper/W6IQLIMN"},"agent_actions":{"view_html":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL","download_json":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL.json","view_paper":"https://pith.science/paper/W6IQLIMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.02461&json=true","fetch_graph":"https://pith.science/api/pith-number/W6IQLIMNUE6ZYRL5H4VRZHLWAL/graph.json","fetch_events":"https://pith.science/api/pith-number/W6IQLIMNUE6ZYRL5H4VRZHLWAL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL/action/storage_attestation","attest_author":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL/action/author_attestation","sign_citation":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL/action/citation_signature","submit_replication":"https://pith.science/pith/W6IQLIMNUE6ZYRL5H4VRZHLWAL/action/replication_record"}},"created_at":"2026-07-05T09:03:01.278209+00:00","updated_at":"2026-07-05T09:03:01.278209+00:00"}