{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6QRGGECITBDWPMSZ3SJ76XXO4R","short_pith_number":"pith:6QRGGECI","schema_version":"1.0","canonical_sha256":"f422631048984767b259dc93ff5eeee47ed910670fd5b5563d7012cda944b160","source":{"kind":"arxiv","id":"2601.08679","version":3},"attestation_state":"computed","paper":{"title":"PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Liang Wang, Qiexiang Wang, Shengbin Yue, Tianrui Qin, Xiaoyou Liu, Xinyi Mou, Yuqing Wang, Zhongyu Wei","submitted_at":"2026-01-13T16:02:35Z","abstract_excerpt":"As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can improve interaction but may compromise objectivity and factual correctness, especially when it is misaligned with the question. To alleviate this problem, we propose PersonaDual, a framework that supports both general-purpose objective reasoning and personalized reasoning in a single model, and adaptively switches modes based on context. PersonaDual is first trained with SFT to learn two reasoning patterns, and then "},"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":"2601.08679","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-13T16:02:35Z","cross_cats_sorted":[],"title_canon_sha256":"27eb243bb57dcb133aba3ce138467dde3130df35c589e57e65e3fe1511292aec","abstract_canon_sha256":"cf80bdb61a0422176679529236aeb5c88ac337e1e3a205670f538c5e3a92dc85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:40.370619Z","signature_b64":"mhv3fm4Wj6itgGhFb/clVvgdqCxKM8gX2RLPNzodhekgDRQgkkSPNNSKZGPVovwxoAC7aFN9J3hEwofhmv0fAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f422631048984767b259dc93ff5eeee47ed910670fd5b5563d7012cda944b160","last_reissued_at":"2026-05-20T00:05:40.369998Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:40.369998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Liang Wang, Qiexiang Wang, Shengbin Yue, Tianrui Qin, Xiaoyou Liu, Xinyi Mou, Yuqing Wang, Zhongyu Wei","submitted_at":"2026-01-13T16:02:35Z","abstract_excerpt":"As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can improve interaction but may compromise objectivity and factual correctness, especially when it is misaligned with the question. To alleviate this problem, we propose PersonaDual, a framework that supports both general-purpose objective reasoning and personalized reasoning in a single model, and adaptively switches modes based on context. PersonaDual is first trained with SFT to learn two reasoning patterns, and then "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.08679","kind":"arxiv","version":3},"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/2601.08679/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":"2601.08679","created_at":"2026-05-20T00:05:40.370094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.08679v3","created_at":"2026-05-20T00:05:40.370094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.08679","created_at":"2026-05-20T00:05:40.370094+00:00"},{"alias_kind":"pith_short_12","alias_value":"6QRGGECITBDW","created_at":"2026-05-20T00:05:40.370094+00:00"},{"alias_kind":"pith_short_16","alias_value":"6QRGGECITBDWPMSZ","created_at":"2026-05-20T00:05:40.370094+00:00"},{"alias_kind":"pith_short_8","alias_value":"6QRGGECI","created_at":"2026-05-20T00:05:40.370094+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/6QRGGECITBDWPMSZ3SJ76XXO4R","json":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R.json","graph_json":"https://pith.science/api/pith-number/6QRGGECITBDWPMSZ3SJ76XXO4R/graph.json","events_json":"https://pith.science/api/pith-number/6QRGGECITBDWPMSZ3SJ76XXO4R/events.json","paper":"https://pith.science/paper/6QRGGECI"},"agent_actions":{"view_html":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R","download_json":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R.json","view_paper":"https://pith.science/paper/6QRGGECI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.08679&json=true","fetch_graph":"https://pith.science/api/pith-number/6QRGGECITBDWPMSZ3SJ76XXO4R/graph.json","fetch_events":"https://pith.science/api/pith-number/6QRGGECITBDWPMSZ3SJ76XXO4R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R/action/storage_attestation","attest_author":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R/action/author_attestation","sign_citation":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R/action/citation_signature","submit_replication":"https://pith.science/pith/6QRGGECITBDWPMSZ3SJ76XXO4R/action/replication_record"}},"created_at":"2026-05-20T00:05:40.370094+00:00","updated_at":"2026-05-20T00:05:40.370094+00:00"}