{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MOSASYVEKLJIAAW4FUVJJOR5IP","short_pith_number":"pith:MOSASYVE","schema_version":"1.0","canonical_sha256":"63a40962a452d28002dc2d2a94ba3d43f5f0c39896871d2b89f1f1689e17701b","source":{"kind":"arxiv","id":"2404.08692","version":1},"attestation_state":"computed","paper":{"title":"Apollonion: Profile-centric Dialog Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Shangyu Chen, Xiang Li, Yuanyuan Zhao, Zibo Zhao","submitted_at":"2024-04-10T03:32:41Z","abstract_excerpt":"The emergence of Large Language Models (LLMs) has innovated the development of dialog agents. Specially, a well-trained LLM, as a central process unit, is capable of providing fluent and reasonable response for user's request. Besides, auxiliary tools such as external knowledge retrieval, personalized character for vivid response, short/long-term memory for ultra long context management are developed, completing the usage experience for LLM-based dialog agents. However, the above-mentioned techniques does not solve the issue of \\textbf{personalization from user perspective}: agents response in"},"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":"2404.08692","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-04-10T03:32:41Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"8c7f4c250da20d7ce716c8378105d88e688d1e6a8d133001ea3eff46ed9537fb","abstract_canon_sha256":"5c78a27eb9806e0c1ac0713473a310ed4e98e38d90b0fdad2be675fc4efbbfa6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:38.292819Z","signature_b64":"2Utpggst9MKPyAYGsSsHBSmHoYyNfnsH4PSk+ovcmtkQidtfZ1Flu+r+uW/7Ji7tt16kn0OCOeV55A9t9PhhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63a40962a452d28002dc2d2a94ba3d43f5f0c39896871d2b89f1f1689e17701b","last_reissued_at":"2026-07-05T08:07:38.292356Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:38.292356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Apollonion: Profile-centric Dialog Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Shangyu Chen, Xiang Li, Yuanyuan Zhao, Zibo Zhao","submitted_at":"2024-04-10T03:32:41Z","abstract_excerpt":"The emergence of Large Language Models (LLMs) has innovated the development of dialog agents. Specially, a well-trained LLM, as a central process unit, is capable of providing fluent and reasonable response for user's request. Besides, auxiliary tools such as external knowledge retrieval, personalized character for vivid response, short/long-term memory for ultra long context management are developed, completing the usage experience for LLM-based dialog agents. However, the above-mentioned techniques does not solve the issue of \\textbf{personalization from user perspective}: agents response in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.08692","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/2404.08692/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":"2404.08692","created_at":"2026-07-05T08:07:38.292415+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.08692v1","created_at":"2026-07-05T08:07:38.292415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.08692","created_at":"2026-07-05T08:07:38.292415+00:00"},{"alias_kind":"pith_short_12","alias_value":"MOSASYVEKLJI","created_at":"2026-07-05T08:07:38.292415+00:00"},{"alias_kind":"pith_short_16","alias_value":"MOSASYVEKLJIAAW4","created_at":"2026-07-05T08:07:38.292415+00:00"},{"alias_kind":"pith_short_8","alias_value":"MOSASYVE","created_at":"2026-07-05T08:07:38.292415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP","json":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP.json","graph_json":"https://pith.science/api/pith-number/MOSASYVEKLJIAAW4FUVJJOR5IP/graph.json","events_json":"https://pith.science/api/pith-number/MOSASYVEKLJIAAW4FUVJJOR5IP/events.json","paper":"https://pith.science/paper/MOSASYVE"},"agent_actions":{"view_html":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP","download_json":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP.json","view_paper":"https://pith.science/paper/MOSASYVE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.08692&json=true","fetch_graph":"https://pith.science/api/pith-number/MOSASYVEKLJIAAW4FUVJJOR5IP/graph.json","fetch_events":"https://pith.science/api/pith-number/MOSASYVEKLJIAAW4FUVJJOR5IP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP/action/storage_attestation","attest_author":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP/action/author_attestation","sign_citation":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP/action/citation_signature","submit_replication":"https://pith.science/pith/MOSASYVEKLJIAAW4FUVJJOR5IP/action/replication_record"}},"created_at":"2026-07-05T08:07:38.292415+00:00","updated_at":"2026-07-05T08:07:38.292415+00:00"}