{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EZ4IHMBWSZDBQSG67I7PCVZUAZ","short_pith_number":"pith:EZ4IHMBW","schema_version":"1.0","canonical_sha256":"267883b03696461848defa3ef15734064925c037606efdfce8380a37cb8725cd","source":{"kind":"arxiv","id":"2601.22396","version":2},"attestation_state":"computed","paper":{"title":"Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.HC","physics.soc-ph"],"primary_cat":"cs.CL","authors_text":"Andrea Tagarelli, Candida M. Greco, Lucio La Cava","submitted_at":"2026-01-29T23:07:58Z","abstract_excerpt":"Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the alignment of synthetic, culturally-grounded personas with established frameworks, specifically the World Values Survey (WVS), the Inglehart-Welzel Cultural Map, and Moral Foundations Theory. We conceptualize and produce LLM-generated personas based on a set of interpretable WVS-derived variables, and we examine the generated p"},"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":"2601.22396","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-01-29T23:07:58Z","cross_cats_sorted":["cs.AI","cs.CY","cs.HC","physics.soc-ph"],"title_canon_sha256":"c045546c9b65d76cc034be3cbefbdb5d642a12c843121dc05ca94958261be310","abstract_canon_sha256":"dae82ba836aeed4698917d868da09a449b2c64465633dfc6c7af06feeef890e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:41.341900Z","signature_b64":"7WvzqOdElyXCchHypzAKiQH1p5HOIDYbJg8UCE6htIbLIdLuEqmQCQELb+toAG/EXQF/PjYEb4AxRB56vhRpAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"267883b03696461848defa3ef15734064925c037606efdfce8380a37cb8725cd","last_reissued_at":"2026-06-04T01:09:41.341213Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:41.341213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.HC","physics.soc-ph"],"primary_cat":"cs.CL","authors_text":"Andrea Tagarelli, Candida M. Greco, Lucio La Cava","submitted_at":"2026-01-29T23:07:58Z","abstract_excerpt":"Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain. This paper investigates the alignment of synthetic, culturally-grounded personas with established frameworks, specifically the World Values Survey (WVS), the Inglehart-Welzel Cultural Map, and Moral Foundations Theory. We conceptualize and produce LLM-generated personas based on a set of interpretable WVS-derived variables, and we examine the generated p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.22396","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/2601.22396/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":"2601.22396","created_at":"2026-06-04T01:09:41.341302+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.22396v2","created_at":"2026-06-04T01:09:41.341302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.22396","created_at":"2026-06-04T01:09:41.341302+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZ4IHMBWSZDB","created_at":"2026-06-04T01:09:41.341302+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZ4IHMBWSZDBQSG6","created_at":"2026-06-04T01:09:41.341302+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZ4IHMBW","created_at":"2026-06-04T01:09:41.341302+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.10843","citing_title":"Training-Free Cultural Alignment of Large Language Models via Persona Disagreement","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10843","citing_title":"Training-Free Cultural Alignment of Large Language Models via Persona Disagreement","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ","json":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ.json","graph_json":"https://pith.science/api/pith-number/EZ4IHMBWSZDBQSG67I7PCVZUAZ/graph.json","events_json":"https://pith.science/api/pith-number/EZ4IHMBWSZDBQSG67I7PCVZUAZ/events.json","paper":"https://pith.science/paper/EZ4IHMBW"},"agent_actions":{"view_html":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ","download_json":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ.json","view_paper":"https://pith.science/paper/EZ4IHMBW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.22396&json=true","fetch_graph":"https://pith.science/api/pith-number/EZ4IHMBWSZDBQSG67I7PCVZUAZ/graph.json","fetch_events":"https://pith.science/api/pith-number/EZ4IHMBWSZDBQSG67I7PCVZUAZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ/action/storage_attestation","attest_author":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ/action/author_attestation","sign_citation":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ/action/citation_signature","submit_replication":"https://pith.science/pith/EZ4IHMBWSZDBQSG67I7PCVZUAZ/action/replication_record"}},"created_at":"2026-06-04T01:09:41.341302+00:00","updated_at":"2026-06-04T01:09:41.341302+00:00"}