{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SEHHGERD2EKH4GSTVKCUHXSXNV","short_pith_number":"pith:SEHHGERD","schema_version":"1.0","canonical_sha256":"910e731223d1147e1a53aa8543de576d4c9bfd24c49d950c79d8d44bc3e27e0e","source":{"kind":"arxiv","id":"2506.12189","version":2},"attestation_state":"computed","paper":{"title":"Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ioana Ciuc\\u{a}, Pranav Agarwal","submitted_at":"2025-06-13T19:31:52Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and complex challenge that requires reasoning over long-range context and modeling causal chains. We evalu"},"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":"2506.12189","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-13T19:31:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3c3b3910d94d34038cda50373b955a9c5b7310079af61ed72853b660efac65c4","abstract_canon_sha256":"118b89c4fbbecad4af0c8f479de50a32e61295b5fb1e291a133f0e68185c32ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:12.563142Z","signature_b64":"fQ1Ua8w2Hj+evH5zRfBWv2bhsTMlN36MW6HmBjTNOprG/yaAtp0ovQyHBzsgMm+mnzBqce5KBH8BhD3z1NZoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"910e731223d1147e1a53aa8543de576d4c9bfd24c49d950c79d8d44bc3e27e0e","last_reissued_at":"2026-07-05T11:25:12.562335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:12.562335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ioana Ciuc\\u{a}, Pranav Agarwal","submitted_at":"2025-06-13T19:31:52Z","abstract_excerpt":"Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and complex challenge that requires reasoning over long-range context and modeling causal chains. We evalu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12189","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/2506.12189/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":"2506.12189","created_at":"2026-07-05T11:25:12.562640+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12189v2","created_at":"2026-07-05T11:25:12.562640+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12189","created_at":"2026-07-05T11:25:12.562640+00:00"},{"alias_kind":"pith_short_12","alias_value":"SEHHGERD2EKH","created_at":"2026-07-05T11:25:12.562640+00:00"},{"alias_kind":"pith_short_16","alias_value":"SEHHGERD2EKH4GST","created_at":"2026-07-05T11:25:12.562640+00:00"},{"alias_kind":"pith_short_8","alias_value":"SEHHGERD","created_at":"2026-07-05T11:25:12.562640+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.07966","citing_title":"Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV","json":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV.json","graph_json":"https://pith.science/api/pith-number/SEHHGERD2EKH4GSTVKCUHXSXNV/graph.json","events_json":"https://pith.science/api/pith-number/SEHHGERD2EKH4GSTVKCUHXSXNV/events.json","paper":"https://pith.science/paper/SEHHGERD"},"agent_actions":{"view_html":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV","download_json":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV.json","view_paper":"https://pith.science/paper/SEHHGERD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12189&json=true","fetch_graph":"https://pith.science/api/pith-number/SEHHGERD2EKH4GSTVKCUHXSXNV/graph.json","fetch_events":"https://pith.science/api/pith-number/SEHHGERD2EKH4GSTVKCUHXSXNV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV/action/storage_attestation","attest_author":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV/action/author_attestation","sign_citation":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV/action/citation_signature","submit_replication":"https://pith.science/pith/SEHHGERD2EKH4GSTVKCUHXSXNV/action/replication_record"}},"created_at":"2026-07-05T11:25:12.562640+00:00","updated_at":"2026-07-05T11:25:12.562640+00:00"}