{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VOVGTRYIXGVTV7SJYXXBYGG4VU","short_pith_number":"pith:VOVGTRYI","schema_version":"1.0","canonical_sha256":"abaa69c708b9ab3afe49c5ee1c18dcad347ffff0df2c0f5cc02876a5a9682d9d","source":{"kind":"arxiv","id":"2505.14106","version":2},"attestation_state":"computed","paper":{"title":"A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bo Ni, Branislav Kveton, Franck Dernoncourt, Junda Wu, Li Li, Linxin Song, Nesreen K. Ahmed, Peilin Cai, Philip S. Yu, Ruiyi Zhang, Ryan A. Rossi, Samyadeep Basu, Subhojyoti Mukherjee, Tiankai Yang, Tong Yu, Xiangliang Zhang, Xiyang Hu, Yuehan Qin, Yue Huang, Yue Zhao, Yu Wang, Yuxiao Zhou, Zhengmian Hu, Zichao Wang","submitted_at":"2025-05-20T09:13:22Z","abstract_excerpt":"We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike existing work that focuses on either personalization or conversational structure in isolation, PersonaConvBench integrates both, offering three core tasks: sentence classification, impact regression, and user-centric text generation across ten diverse Reddit-based domains. This design enables systematic analysis of how personalized conversational context shapes LLM outputs in realistic multi-user scenarios. We benchmark "},"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":"2505.14106","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-20T09:13:22Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f1236fcf7bff786bb4351041788367458c3d44226cdb513b53bc019e7b20f90d","abstract_canon_sha256":"9f62a1dd83dccd8dfd9d7d5fd180101167e1ad08fd0c9d4c2260833a3fbd550b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:14.299887Z","signature_b64":"JSZt3mqAsA9BQUNbsH5OuufRxePGxjAuKnBjpLqUNDDgiX7r3kMmBYJ1tphndeCaAivpVh/rsRr56bdPnDYPAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abaa69c708b9ab3afe49c5ee1c18dcad347ffff0df2c0f5cc02876a5a9682d9d","last_reissued_at":"2026-07-05T11:09:14.299408Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:14.299408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bo Ni, Branislav Kveton, Franck Dernoncourt, Junda Wu, Li Li, Linxin Song, Nesreen K. Ahmed, Peilin Cai, Philip S. Yu, Ruiyi Zhang, Ryan A. Rossi, Samyadeep Basu, Subhojyoti Mukherjee, Tiankai Yang, Tong Yu, Xiangliang Zhang, Xiyang Hu, Yuehan Qin, Yue Huang, Yue Zhao, Yu Wang, Yuxiao Zhou, Zhengmian Hu, Zichao Wang","submitted_at":"2025-05-20T09:13:22Z","abstract_excerpt":"We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike existing work that focuses on either personalization or conversational structure in isolation, PersonaConvBench integrates both, offering three core tasks: sentence classification, impact regression, and user-centric text generation across ten diverse Reddit-based domains. This design enables systematic analysis of how personalized conversational context shapes LLM outputs in realistic multi-user scenarios. We benchmark "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14106","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/2505.14106/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":"2505.14106","created_at":"2026-07-05T11:09:14.299466+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.14106v2","created_at":"2026-07-05T11:09:14.299466+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14106","created_at":"2026-07-05T11:09:14.299466+00:00"},{"alias_kind":"pith_short_12","alias_value":"VOVGTRYIXGVT","created_at":"2026-07-05T11:09:14.299466+00:00"},{"alias_kind":"pith_short_16","alias_value":"VOVGTRYIXGVTV7SJ","created_at":"2026-07-05T11:09:14.299466+00:00"},{"alias_kind":"pith_short_8","alias_value":"VOVGTRYI","created_at":"2026-07-05T11:09:14.299466+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28739","citing_title":"Agent Safety Is Action Alignment","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12995","citing_title":"F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26247","citing_title":"TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24977","citing_title":"A Survey on LLM-based Conversational User Simulation","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17299","citing_title":"Cat-DPO: Category-Adaptive Safety Alignment","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU","json":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU.json","graph_json":"https://pith.science/api/pith-number/VOVGTRYIXGVTV7SJYXXBYGG4VU/graph.json","events_json":"https://pith.science/api/pith-number/VOVGTRYIXGVTV7SJYXXBYGG4VU/events.json","paper":"https://pith.science/paper/VOVGTRYI"},"agent_actions":{"view_html":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU","download_json":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU.json","view_paper":"https://pith.science/paper/VOVGTRYI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.14106&json=true","fetch_graph":"https://pith.science/api/pith-number/VOVGTRYIXGVTV7SJYXXBYGG4VU/graph.json","fetch_events":"https://pith.science/api/pith-number/VOVGTRYIXGVTV7SJYXXBYGG4VU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU/action/storage_attestation","attest_author":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU/action/author_attestation","sign_citation":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU/action/citation_signature","submit_replication":"https://pith.science/pith/VOVGTRYIXGVTV7SJYXXBYGG4VU/action/replication_record"}},"created_at":"2026-07-05T11:09:14.299466+00:00","updated_at":"2026-07-05T11:09:14.299466+00:00"}