{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:UXEQMP44UA76ZLHX35CS6BA2CS","short_pith_number":"pith:UXEQMP44","canonical_record":{"source":{"id":"2502.12988","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T16:11:54Z","cross_cats_sorted":[],"title_canon_sha256":"91ffb89f932f4f0193a4516be3a39ec55e1ed79f8ca224b3f794ee2f6a5961e1","abstract_canon_sha256":"f8dced43d2cf9a033404820e0e86aa570f2d0953d1a6363894c07233476389d9"},"schema_version":"1.0"},"canonical_sha256":"a5c9063f9ca03fecacf7df452f041a14b241760174596620a30140f68df816df","source":{"kind":"arxiv","id":"2502.12988","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12988","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12988v3","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12988","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_12","alias_value":"UXEQMP44UA76","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_16","alias_value":"UXEQMP44UA76ZLHX","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_8","alias_value":"UXEQMP44","created_at":"2026-07-05T11:42:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:UXEQMP44UA76ZLHX35CS6BA2CS","target":"record","payload":{"canonical_record":{"source":{"id":"2502.12988","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T16:11:54Z","cross_cats_sorted":[],"title_canon_sha256":"91ffb89f932f4f0193a4516be3a39ec55e1ed79f8ca224b3f794ee2f6a5961e1","abstract_canon_sha256":"f8dced43d2cf9a033404820e0e86aa570f2d0953d1a6363894c07233476389d9"},"schema_version":"1.0"},"canonical_sha256":"a5c9063f9ca03fecacf7df452f041a14b241760174596620a30140f68df816df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:26.063492Z","signature_b64":"t1F9m2RFUY49X8+7JyDfYu1R4CuytGDKPFSCom3ry5wqOVYKJzLNdbRZKBckpdSl36GNDsvExEhUd9EtvaNhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5c9063f9ca03fecacf7df452f041a14b241760174596620a30140f68df816df","last_reissued_at":"2026-07-05T11:42:26.063043Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:26.063043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.12988","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:42:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tjXCusvK/kqm70cW1RAOtoH/hnyLeJRr1KQuebX68KA6dHtvyMowRq3N1+LVCcTPvEyt79IS1r08ansb4Gc0Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:24:54.578359Z"},"content_sha256":"5c0ea8875a0ec07a1fda00d0b50633144b6f315f1f04ca7b7553fcfbffbeef25","schema_version":"1.0","event_id":"sha256:5c0ea8875a0ec07a1fda00d0b50633144b6f315f1f04ca7b7553fcfbffbeef25"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:UXEQMP44UA76ZLHX35CS6BA2CS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Duzhen Zhang, Ishita Agrawal, Le Song, Shen Gao, Xiuying Chen, Zixiao Wang","submitted_at":"2025-02-18T16:11:54Z","abstract_excerpt":"Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue datasets to capture a character's responses. However, a holistic representation of an individual goes beyond surface-level facts or conversations to deeper thoughts and thinking. In this work, we introduce CharacterBot, a model designed to replicate both the linguistic patterns and distinctive thought patterns as manifested in the textual works of a character. Using Lu Xun, a renowned Chinese writer as a case study, we prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12988","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/2502.12988/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:42:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wj0Zw44kXHDfBBegcJ3y2JVtXeHQShdlA9yXXXHivp05T/X4ZbQoaufiGTwzPH54q57RmbD840qRkmiPoGoGBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:24:54.578733Z"},"content_sha256":"7538de7e3492446961be9969b7df99e5a3d07e9a2d0ec8b951a4e7ef3cb45237","schema_version":"1.0","event_id":"sha256:7538de7e3492446961be9969b7df99e5a3d07e9a2d0ec8b951a4e7ef3cb45237"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UXEQMP44UA76ZLHX35CS6BA2CS/bundle.json","state_url":"https://pith.science/pith/UXEQMP44UA76ZLHX35CS6BA2CS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UXEQMP44UA76ZLHX35CS6BA2CS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T13:24:54Z","links":{"resolver":"https://pith.science/pith/UXEQMP44UA76ZLHX35CS6BA2CS","bundle":"https://pith.science/pith/UXEQMP44UA76ZLHX35CS6BA2CS/bundle.json","state":"https://pith.science/pith/UXEQMP44UA76ZLHX35CS6BA2CS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UXEQMP44UA76ZLHX35CS6BA2CS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:UXEQMP44UA76ZLHX35CS6BA2CS","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f8dced43d2cf9a033404820e0e86aa570f2d0953d1a6363894c07233476389d9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T16:11:54Z","title_canon_sha256":"91ffb89f932f4f0193a4516be3a39ec55e1ed79f8ca224b3f794ee2f6a5961e1"},"schema_version":"1.0","source":{"id":"2502.12988","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.12988","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"arxiv_version","alias_value":"2502.12988v3","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.12988","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_12","alias_value":"UXEQMP44UA76","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_16","alias_value":"UXEQMP44UA76ZLHX","created_at":"2026-07-05T11:42:26Z"},{"alias_kind":"pith_short_8","alias_value":"UXEQMP44","created_at":"2026-07-05T11:42:26Z"}],"graph_snapshots":[{"event_id":"sha256:7538de7e3492446961be9969b7df99e5a3d07e9a2d0ec8b951a4e7ef3cb45237","target":"graph","created_at":"2026-07-05T11:42:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.12988/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Previous approaches to persona simulation large language models (LLMs) have typically relied on learning basic biographical information, or using limited role-play dialogue datasets to capture a character's responses. However, a holistic representation of an individual goes beyond surface-level facts or conversations to deeper thoughts and thinking. In this work, we introduce CharacterBot, a model designed to replicate both the linguistic patterns and distinctive thought patterns as manifested in the textual works of a character. Using Lu Xun, a renowned Chinese writer as a case study, we prop","authors_text":"Duzhen Zhang, Ishita Agrawal, Le Song, Shen Gao, Xiuying Chen, Zixiao Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T16:11:54Z","title":"Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.12988","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5c0ea8875a0ec07a1fda00d0b50633144b6f315f1f04ca7b7553fcfbffbeef25","target":"record","created_at":"2026-07-05T11:42:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f8dced43d2cf9a033404820e0e86aa570f2d0953d1a6363894c07233476389d9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-18T16:11:54Z","title_canon_sha256":"91ffb89f932f4f0193a4516be3a39ec55e1ed79f8ca224b3f794ee2f6a5961e1"},"schema_version":"1.0","source":{"id":"2502.12988","kind":"arxiv","version":3}},"canonical_sha256":"a5c9063f9ca03fecacf7df452f041a14b241760174596620a30140f68df816df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5c9063f9ca03fecacf7df452f041a14b241760174596620a30140f68df816df","first_computed_at":"2026-07-05T11:42:26.063043Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:42:26.063043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t1F9m2RFUY49X8+7JyDfYu1R4CuytGDKPFSCom3ry5wqOVYKJzLNdbRZKBckpdSl36GNDsvExEhUd9EtvaNhDA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:42:26.063492Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.12988","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c0ea8875a0ec07a1fda00d0b50633144b6f315f1f04ca7b7553fcfbffbeef25","sha256:7538de7e3492446961be9969b7df99e5a3d07e9a2d0ec8b951a4e7ef3cb45237"],"state_sha256":"7142139252310ccc1577c7afd949af7320212f8afa33bcbfdccbbe0df46a370d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uqluDqiSUP37tJWSPrH8hMMcAWOe2w4Ozj7YjVt+WVD8v3WxFBGYMR+F5HGaW0IrHFhkA2zdk9u/pwCuX1aNBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:24:54.580965Z","bundle_sha256":"07bac01202099b3f875d479aec03a51f330a58c1f454b413e18b0a00c829921f"}}