{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FF2MCA3I3WTUCQHA3P6FLHJRZ5","short_pith_number":"pith:FF2MCA3I","schema_version":"1.0","canonical_sha256":"2974c10368dda74140e0dbfc559d31cf51b8975c38c8b1cf7b5e0bfef0ab67bf","source":{"kind":"arxiv","id":"2505.16610","version":1},"attestation_state":"computed","paper":{"title":"From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Jing Ye, Lu Xiang, Yaping Zhang","submitted_at":"2025-05-22T12:45:12Z","abstract_excerpt":"Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic and one-size-fits-all responses that fail to address users' specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users' implicit preferences concerning user profiles (personalities), emotional states, and specific situations. Our framework consists of two dist"},"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":"2505.16610","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-22T12:45:12Z","cross_cats_sorted":[],"title_canon_sha256":"f6e72effd58535533f6f295191c8ad868d7e9656d82f9c90787177c6134757cc","abstract_canon_sha256":"5f9f2330fcfaad4f616daeb094c794b46e52befc763bf20830ae91af13cb0566"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:37.952239Z","signature_b64":"zmn9Ddku+0fDqUORqm671yt01GG3UTzBNIVl32D9VLeppYuFINf8KpuON6Nic+N5/iLxwf2qGZiQ9NAPFZnaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2974c10368dda74140e0dbfc559d31cf51b8975c38c8b1cf7b5e0bfef0ab67bf","last_reissued_at":"2026-07-05T11:07:37.951762Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:37.951762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Jing Ye, Lu Xiang, Yaping Zhang","submitted_at":"2025-05-22T12:45:12Z","abstract_excerpt":"Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic and one-size-fits-all responses that fail to address users' specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users' implicit preferences concerning user profiles (personalities), emotional states, and specific situations. Our framework consists of two dist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16610","kind":"arxiv","version":1},"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/2505.16610/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":"2505.16610","created_at":"2026-07-05T11:07:37.951818+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16610v1","created_at":"2026-07-05T11:07:37.951818+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16610","created_at":"2026-07-05T11:07:37.951818+00:00"},{"alias_kind":"pith_short_12","alias_value":"FF2MCA3I3WTU","created_at":"2026-07-05T11:07:37.951818+00:00"},{"alias_kind":"pith_short_16","alias_value":"FF2MCA3I3WTUCQHA","created_at":"2026-07-05T11:07:37.951818+00:00"},{"alias_kind":"pith_short_8","alias_value":"FF2MCA3I","created_at":"2026-07-05T11:07:37.951818+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.21930","citing_title":"MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinement","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5","json":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5.json","graph_json":"https://pith.science/api/pith-number/FF2MCA3I3WTUCQHA3P6FLHJRZ5/graph.json","events_json":"https://pith.science/api/pith-number/FF2MCA3I3WTUCQHA3P6FLHJRZ5/events.json","paper":"https://pith.science/paper/FF2MCA3I"},"agent_actions":{"view_html":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5","download_json":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5.json","view_paper":"https://pith.science/paper/FF2MCA3I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16610&json=true","fetch_graph":"https://pith.science/api/pith-number/FF2MCA3I3WTUCQHA3P6FLHJRZ5/graph.json","fetch_events":"https://pith.science/api/pith-number/FF2MCA3I3WTUCQHA3P6FLHJRZ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5/action/storage_attestation","attest_author":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5/action/author_attestation","sign_citation":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5/action/citation_signature","submit_replication":"https://pith.science/pith/FF2MCA3I3WTUCQHA3P6FLHJRZ5/action/replication_record"}},"created_at":"2026-07-05T11:07:37.951818+00:00","updated_at":"2026-07-05T11:07:37.951818+00:00"}