{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TZFVQCDHNBSPDC3SEUCP4YMHPS","short_pith_number":"pith:TZFVQCDH","schema_version":"1.0","canonical_sha256":"9e4b5808676864f18b722504fe61877ca059ade4ea81a02b3b2753602b7901f1","source":{"kind":"arxiv","id":"2306.15774","version":2},"attestation_state":"computed","paper":{"title":"Next Steps for Human-Centered Generative AI: A Technical Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.HC","authors_text":"Bolei Zhou, Chien-Sheng Wu, Dingzeyu Li, Jeff Burke, Jennifer Jacobs, Karl D. D. Willis, Matthew K. Hong, Nanyun Peng, Philippe Laban, Ruofei Du, Xiang 'Anthony' Chen","submitted_at":"2023-06-27T19:54:30Z","abstract_excerpt":"Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topics while maintaining a coherent big picture of the future work landscape."},"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":"2306.15774","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-06-27T19:54:30Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"3ac3c02f364d3e2fe5f1a0ad3e8d712073f98f0a0294cbd706f32657b3c87093","abstract_canon_sha256":"2d70bb37bc1e89e219f56abab32bb89651e0d89f746fdca95ca99444b8e317ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:07.008876Z","signature_b64":"nIzmsqLUokPHyKLATNB8TKePzmDJOV8Fl+1bO07t4YvCZCEw/P57/WLRl1HFXImK3B+5McM6RE1z/qkkdI4TCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e4b5808676864f18b722504fe61877ca059ade4ea81a02b3b2753602b7901f1","last_reissued_at":"2026-07-05T07:27:07.008376Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:07.008376Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Next Steps for Human-Centered Generative AI: A Technical Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"cs.HC","authors_text":"Bolei Zhou, Chien-Sheng Wu, Dingzeyu Li, Jeff Burke, Jennifer Jacobs, Karl D. D. Willis, Matthew K. Hong, Nanyun Peng, Philippe Laban, Ruofei Du, Xiang 'Anthony' Chen","submitted_at":"2023-06-27T19:54:30Z","abstract_excerpt":"Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topics while maintaining a coherent big picture of the future work landscape."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15774","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/2306.15774/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":"2306.15774","created_at":"2026-07-05T07:27:07.008441+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.15774v2","created_at":"2026-07-05T07:27:07.008441+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15774","created_at":"2026-07-05T07:27:07.008441+00:00"},{"alias_kind":"pith_short_12","alias_value":"TZFVQCDHNBSP","created_at":"2026-07-05T07:27:07.008441+00:00"},{"alias_kind":"pith_short_16","alias_value":"TZFVQCDHNBSPDC3S","created_at":"2026-07-05T07:27:07.008441+00:00"},{"alias_kind":"pith_short_8","alias_value":"TZFVQCDH","created_at":"2026-07-05T07:27:07.008441+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.15227","citing_title":"GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS","json":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS.json","graph_json":"https://pith.science/api/pith-number/TZFVQCDHNBSPDC3SEUCP4YMHPS/graph.json","events_json":"https://pith.science/api/pith-number/TZFVQCDHNBSPDC3SEUCP4YMHPS/events.json","paper":"https://pith.science/paper/TZFVQCDH"},"agent_actions":{"view_html":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS","download_json":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS.json","view_paper":"https://pith.science/paper/TZFVQCDH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.15774&json=true","fetch_graph":"https://pith.science/api/pith-number/TZFVQCDHNBSPDC3SEUCP4YMHPS/graph.json","fetch_events":"https://pith.science/api/pith-number/TZFVQCDHNBSPDC3SEUCP4YMHPS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS/action/storage_attestation","attest_author":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS/action/author_attestation","sign_citation":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS/action/citation_signature","submit_replication":"https://pith.science/pith/TZFVQCDHNBSPDC3SEUCP4YMHPS/action/replication_record"}},"created_at":"2026-07-05T07:27:07.008441+00:00","updated_at":"2026-07-05T07:27:07.008441+00:00"}