{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KROU6KEZRAMY6INDUGJVYRMEB6","short_pith_number":"pith:KROU6KEZ","schema_version":"1.0","canonical_sha256":"545d4f289988198f21a3a1935c45840fb4ba07abc3f04765605a740a3d61b403","source":{"kind":"arxiv","id":"2507.22255","version":1},"attestation_state":"computed","paper":{"title":"Agent-centric learning: from external reward maximization to internal knowledge curation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SC"],"primary_cat":"cs.LG","authors_text":"Charley M. Wu, David G. Nagy, Fryderyk Mantiuk, Hanqi Zhou","submitted_at":"2025-07-29T22:09:35Z","abstract_excerpt":"The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures. We posit that the capacity -- to shape one's own understanding -- is an element for achieving bette"},"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":"2507.22255","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-29T22:09:35Z","cross_cats_sorted":["cs.AI","cs.SC"],"title_canon_sha256":"286d996b797b0818c79c9fb8d24f951504b2c1a799fd41413e4110f5a2231ecb","abstract_canon_sha256":"85a1f79ea5e74f3a4e6a338f25d966ee3aadfac35f67f3a33c5c0510253327d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:19.249910Z","signature_b64":"Ql4cbO0d3jnQhPoUw54O9tJYUknATvf7Tg03igAzkst7D7VyEkbu6aZjROYszElR4WDHAXJC1qUdI4hkjhQ0AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"545d4f289988198f21a3a1935c45840fb4ba07abc3f04765605a740a3d61b403","last_reissued_at":"2026-07-05T11:45:19.249398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:19.249398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agent-centric learning: from external reward maximization to internal knowledge curation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SC"],"primary_cat":"cs.LG","authors_text":"Charley M. Wu, David G. Nagy, Fryderyk Mantiuk, Hanqi Zhou","submitted_at":"2025-07-29T22:09:35Z","abstract_excerpt":"The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures. We posit that the capacity -- to shape one's own understanding -- is an element for achieving bette"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.22255","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/2507.22255/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":"2507.22255","created_at":"2026-07-05T11:45:19.249459+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.22255v1","created_at":"2026-07-05T11:45:19.249459+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.22255","created_at":"2026-07-05T11:45:19.249459+00:00"},{"alias_kind":"pith_short_12","alias_value":"KROU6KEZRAMY","created_at":"2026-07-05T11:45:19.249459+00:00"},{"alias_kind":"pith_short_16","alias_value":"KROU6KEZRAMY6IND","created_at":"2026-07-05T11:45:19.249459+00:00"},{"alias_kind":"pith_short_8","alias_value":"KROU6KEZ","created_at":"2026-07-05T11:45:19.249459+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08406","citing_title":"Effective Explanations Support Planning Under Uncertainty","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6","json":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6.json","graph_json":"https://pith.science/api/pith-number/KROU6KEZRAMY6INDUGJVYRMEB6/graph.json","events_json":"https://pith.science/api/pith-number/KROU6KEZRAMY6INDUGJVYRMEB6/events.json","paper":"https://pith.science/paper/KROU6KEZ"},"agent_actions":{"view_html":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6","download_json":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6.json","view_paper":"https://pith.science/paper/KROU6KEZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.22255&json=true","fetch_graph":"https://pith.science/api/pith-number/KROU6KEZRAMY6INDUGJVYRMEB6/graph.json","fetch_events":"https://pith.science/api/pith-number/KROU6KEZRAMY6INDUGJVYRMEB6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6/action/storage_attestation","attest_author":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6/action/author_attestation","sign_citation":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6/action/citation_signature","submit_replication":"https://pith.science/pith/KROU6KEZRAMY6INDUGJVYRMEB6/action/replication_record"}},"created_at":"2026-07-05T11:45:19.249459+00:00","updated_at":"2026-07-05T11:45:19.249459+00:00"}