{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EBHWO2M2ZLPULG2QYVD6HW7ACN","short_pith_number":"pith:EBHWO2M2","schema_version":"1.0","canonical_sha256":"204f67699acadf459b50c547e3dbe0134fb728797e6238cc7b99e90dba91d59c","source":{"kind":"arxiv","id":"2407.01017","version":2},"attestation_state":"computed","paper":{"title":"Coding for Intelligence from the Perspective of Category","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaying Liu, Lilang Lin, Ling-Yu Duan, Wenhan Yang, Zixuan Hu","submitted_at":"2024-07-01T07:05:44Z","abstract_excerpt":"Coding, which targets compressing and reconstructing data, and intelligence, often regarded at an abstract computational level as being centered around model learning and prediction, interweave recently to give birth to a series of significant progress. The recent trends demonstrate the potential homogeneity of these two fields, especially when deep-learning models aid these two categories for better probability modeling. For better understanding and describing from a unified perspective, inspired by the basic generally recognized principles in cognitive psychology, we formulate a novel proble"},"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":"2407.01017","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-01T07:05:44Z","cross_cats_sorted":[],"title_canon_sha256":"b292798d5cd93ee89748ab81f97bceb692990fcaf556ca043e28d84c8a7e09e0","abstract_canon_sha256":"fbda06ac126ace05ee89be8bc5f21532afe755500138677c95f10cf417da9ead"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:01.405114Z","signature_b64":"yU8dYfcuQ6Non85xara50jJB0OK0LVyfPD1Q5mL8a5Yqq4Kg6tmcFRqzexI/T1QwPSLBD9QvIJwteCortcGPDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"204f67699acadf459b50c547e3dbe0134fb728797e6238cc7b99e90dba91d59c","last_reissued_at":"2026-07-05T08:39:01.404558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:01.404558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Coding for Intelligence from the Perspective of Category","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaying Liu, Lilang Lin, Ling-Yu Duan, Wenhan Yang, Zixuan Hu","submitted_at":"2024-07-01T07:05:44Z","abstract_excerpt":"Coding, which targets compressing and reconstructing data, and intelligence, often regarded at an abstract computational level as being centered around model learning and prediction, interweave recently to give birth to a series of significant progress. The recent trends demonstrate the potential homogeneity of these two fields, especially when deep-learning models aid these two categories for better probability modeling. For better understanding and describing from a unified perspective, inspired by the basic generally recognized principles in cognitive psychology, we formulate a novel proble"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.01017","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/2407.01017/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":"2407.01017","created_at":"2026-07-05T08:39:01.404643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.01017v2","created_at":"2026-07-05T08:39:01.404643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.01017","created_at":"2026-07-05T08:39:01.404643+00:00"},{"alias_kind":"pith_short_12","alias_value":"EBHWO2M2ZLPU","created_at":"2026-07-05T08:39:01.404643+00:00"},{"alias_kind":"pith_short_16","alias_value":"EBHWO2M2ZLPULG2Q","created_at":"2026-07-05T08:39:01.404643+00:00"},{"alias_kind":"pith_short_8","alias_value":"EBHWO2M2","created_at":"2026-07-05T08:39:01.404643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05279","citing_title":"MTL-UE: Learning to Learn Nothing for Multi-Task Learning","ref_index":77,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN","json":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN.json","graph_json":"https://pith.science/api/pith-number/EBHWO2M2ZLPULG2QYVD6HW7ACN/graph.json","events_json":"https://pith.science/api/pith-number/EBHWO2M2ZLPULG2QYVD6HW7ACN/events.json","paper":"https://pith.science/paper/EBHWO2M2"},"agent_actions":{"view_html":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN","download_json":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN.json","view_paper":"https://pith.science/paper/EBHWO2M2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.01017&json=true","fetch_graph":"https://pith.science/api/pith-number/EBHWO2M2ZLPULG2QYVD6HW7ACN/graph.json","fetch_events":"https://pith.science/api/pith-number/EBHWO2M2ZLPULG2QYVD6HW7ACN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN/action/storage_attestation","attest_author":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN/action/author_attestation","sign_citation":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN/action/citation_signature","submit_replication":"https://pith.science/pith/EBHWO2M2ZLPULG2QYVD6HW7ACN/action/replication_record"}},"created_at":"2026-07-05T08:39:01.404643+00:00","updated_at":"2026-07-05T08:39:01.404643+00:00"}