{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:E7UYYBI25ULP76C7E5BMSPTQAU","short_pith_number":"pith:E7UYYBI2","schema_version":"1.0","canonical_sha256":"27e98c051aed16fff85f2742c93e70053c0cd9f5b01718e68d27c0a0aa0e2273","source":{"kind":"arxiv","id":"2505.06907","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Foundation Models for Personalized Federated Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.AI","authors_text":"Apurba Adhikary, Avi Deb Raha, Choong Seon Hong, Dusit Niyato, Eui-Nam Huh, Huy Q. Le, Loc X. Nguyen, Mengchun Zhang, Phuong-Nam Tran, Yu Qiao","submitted_at":"2025-05-11T08:57:53Z","abstract_excerpt":"The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remarkable capabilities in generating human-like content, pushing the boundaries towards artificial general intelligence (AGI). However, their large-scale nature, privacy sensitivity, and substantial computational demands pose significant challenges for personalized customization for end users. To bridge this gap, we present the vision of artificial personalized intelligence (API), which focuses on adapting FMs to individu"},"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":"2505.06907","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-05-11T08:57:53Z","cross_cats_sorted":["cs.CV","cs.NE"],"title_canon_sha256":"45b711bf997b98e03a3cbb418f10b48f7d4c830e4faabf76e3a85d28f37bc609","abstract_canon_sha256":"aa955d4c9d3c7747a4e043729773a925166159d82172474fb2c379af4e6ce249"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:09.352343Z","signature_b64":"Oe4Xe11JPp2M2gySH3jmoY1i7D24ECKEO4Q4Uv5iG/BQ8sbBjmih+I18Ogj5tUGor9QJSkBe03IqegQKq/ezAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27e98c051aed16fff85f2742c93e70053c0cd9f5b01718e68d27c0a0aa0e2273","last_reissued_at":"2026-05-20T00:04:09.351513Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:09.351513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Foundation Models for Personalized Federated Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.NE"],"primary_cat":"cs.AI","authors_text":"Apurba Adhikary, Avi Deb Raha, Choong Seon Hong, Dusit Niyato, Eui-Nam Huh, Huy Q. Le, Loc X. Nguyen, Mengchun Zhang, Phuong-Nam Tran, Yu Qiao","submitted_at":"2025-05-11T08:57:53Z","abstract_excerpt":"The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remarkable capabilities in generating human-like content, pushing the boundaries towards artificial general intelligence (AGI). However, their large-scale nature, privacy sensitivity, and substantial computational demands pose significant challenges for personalized customization for end users. To bridge this gap, we present the vision of artificial personalized intelligence (API), which focuses on adapting FMs to individu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06907","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/2505.06907/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":"2505.06907","created_at":"2026-05-20T00:04:09.351641+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.06907v2","created_at":"2026-05-20T00:04:09.351641+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06907","created_at":"2026-05-20T00:04:09.351641+00:00"},{"alias_kind":"pith_short_12","alias_value":"E7UYYBI25ULP","created_at":"2026-05-20T00:04:09.351641+00:00"},{"alias_kind":"pith_short_16","alias_value":"E7UYYBI25ULP76C7","created_at":"2026-05-20T00:04:09.351641+00:00"},{"alias_kind":"pith_short_8","alias_value":"E7UYYBI2","created_at":"2026-05-20T00:04:09.351641+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU","json":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU.json","graph_json":"https://pith.science/api/pith-number/E7UYYBI25ULP76C7E5BMSPTQAU/graph.json","events_json":"https://pith.science/api/pith-number/E7UYYBI25ULP76C7E5BMSPTQAU/events.json","paper":"https://pith.science/paper/E7UYYBI2"},"agent_actions":{"view_html":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU","download_json":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU.json","view_paper":"https://pith.science/paper/E7UYYBI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.06907&json=true","fetch_graph":"https://pith.science/api/pith-number/E7UYYBI25ULP76C7E5BMSPTQAU/graph.json","fetch_events":"https://pith.science/api/pith-number/E7UYYBI25ULP76C7E5BMSPTQAU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU/action/storage_attestation","attest_author":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU/action/author_attestation","sign_citation":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU/action/citation_signature","submit_replication":"https://pith.science/pith/E7UYYBI25ULP76C7E5BMSPTQAU/action/replication_record"}},"created_at":"2026-05-20T00:04:09.351641+00:00","updated_at":"2026-05-20T00:04:09.351641+00:00"}