{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SBYRHD3E27JRF35FWGY26RXVPJ","short_pith_number":"pith:SBYRHD3E","schema_version":"1.0","canonical_sha256":"9071138f64d7d312efa5b1b1af46f57a72b425f36551d77ef225ca3c9ed4404e","source":{"kind":"arxiv","id":"2503.17662","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Dai, Jingsheng Gao, Ke Ji, Linxu Li, Weiyuan Li, Yixin Lian","submitted_at":"2025-03-22T06:12:34Z","abstract_excerpt":"In recent years, large language models (LLMs) have achieved breakthrough progress in many dialogue generation tasks. However, their lack of emotion and fine-grained role awareness limits the model's ability to provide personalized and diverse interactions further. Current methods face high costs in collecting high-quality annotated data for scenarios such as role-playing, and traditional human alignment methods are difficult to deploy due to the inherent diversity of model behavior in role-playing scenarios. Inspired by the alignment of models for safety behaviors through RLHF (Reinforcement L"},"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":"2503.17662","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-22T06:12:34Z","cross_cats_sorted":[],"title_canon_sha256":"e21f9f70f98231f739a9648371bc7aa99693d050eaa99247880d65e843202655","abstract_canon_sha256":"b34f3be52704b0bd39afcccec213323d4c1ae2b296c78bacec468a03ca5ae447"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:38:45.623030Z","signature_b64":"wIYcs+b8bCNz7fLeJbiGuOtvqO9Hs3UF8ZloF3pFJ7vCZVSM4UeX2w54+qxM19kWCEeY4sSW+5P/tPA//LQtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9071138f64d7d312efa5b1b1af46f57a72b425f36551d77ef225ca3c9ed4404e","last_reissued_at":"2026-07-05T10:38:45.622562Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:38:45.622562Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Dai, Jingsheng Gao, Ke Ji, Linxu Li, Weiyuan Li, Yixin Lian","submitted_at":"2025-03-22T06:12:34Z","abstract_excerpt":"In recent years, large language models (LLMs) have achieved breakthrough progress in many dialogue generation tasks. However, their lack of emotion and fine-grained role awareness limits the model's ability to provide personalized and diverse interactions further. Current methods face high costs in collecting high-quality annotated data for scenarios such as role-playing, and traditional human alignment methods are difficult to deploy due to the inherent diversity of model behavior in role-playing scenarios. Inspired by the alignment of models for safety behaviors through RLHF (Reinforcement L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17662","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/2503.17662/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":"2503.17662","created_at":"2026-07-05T10:38:45.622617+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17662v2","created_at":"2026-07-05T10:38:45.622617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17662","created_at":"2026-07-05T10:38:45.622617+00:00"},{"alias_kind":"pith_short_12","alias_value":"SBYRHD3E27JR","created_at":"2026-07-05T10:38:45.622617+00:00"},{"alias_kind":"pith_short_16","alias_value":"SBYRHD3E27JRF35F","created_at":"2026-07-05T10:38:45.622617+00:00"},{"alias_kind":"pith_short_8","alias_value":"SBYRHD3E","created_at":"2026-07-05T10:38:45.622617+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14717","citing_title":"Layered Mutability: Continuity and Governance in Persistent Self-Modifying Agents","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27228","citing_title":"When Roles Fail: Epistemic Constraints on Advocate Role Fidelity in LLM-Based Political Statement Analysis","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14717","citing_title":"Layered Mutability: Continuity and Governance in Persistent Self-Modifying Agents","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ","json":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ.json","graph_json":"https://pith.science/api/pith-number/SBYRHD3E27JRF35FWGY26RXVPJ/graph.json","events_json":"https://pith.science/api/pith-number/SBYRHD3E27JRF35FWGY26RXVPJ/events.json","paper":"https://pith.science/paper/SBYRHD3E"},"agent_actions":{"view_html":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ","download_json":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ.json","view_paper":"https://pith.science/paper/SBYRHD3E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17662&json=true","fetch_graph":"https://pith.science/api/pith-number/SBYRHD3E27JRF35FWGY26RXVPJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SBYRHD3E27JRF35FWGY26RXVPJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ/action/storage_attestation","attest_author":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ/action/author_attestation","sign_citation":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ/action/citation_signature","submit_replication":"https://pith.science/pith/SBYRHD3E27JRF35FWGY26RXVPJ/action/replication_record"}},"created_at":"2026-07-05T10:38:45.622617+00:00","updated_at":"2026-07-05T10:38:45.622617+00:00"}