{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:HNS63G2YXYAGE5JTG3PLFOYQEJ","short_pith_number":"pith:HNS63G2Y","canonical_record":{"source":{"id":"2502.20616","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-28T00:43:35Z","cross_cats_sorted":[],"title_canon_sha256":"4ae00a42d6d7409d1cdda25e10d1047439692215d17433f5b32450de633dc4b4","abstract_canon_sha256":"4c309b7e7dd44cd09d96619cb8c3f2664b33a29cb2c6dedc35e6bb372e921d03"},"schema_version":"1.0"},"canonical_sha256":"3b65ed9b58be0062753336deb2bb1022415fe46f8822027cef910b447a80473d","source":{"kind":"arxiv","id":"2502.20616","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20616","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20616v2","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20616","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_12","alias_value":"HNS63G2YXYAG","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_16","alias_value":"HNS63G2YXYAGE5JT","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_8","alias_value":"HNS63G2Y","created_at":"2026-07-05T11:56:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:HNS63G2YXYAGE5JTG3PLFOYQEJ","target":"record","payload":{"canonical_record":{"source":{"id":"2502.20616","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-28T00:43:35Z","cross_cats_sorted":[],"title_canon_sha256":"4ae00a42d6d7409d1cdda25e10d1047439692215d17433f5b32450de633dc4b4","abstract_canon_sha256":"4c309b7e7dd44cd09d96619cb8c3f2664b33a29cb2c6dedc35e6bb372e921d03"},"schema_version":"1.0"},"canonical_sha256":"3b65ed9b58be0062753336deb2bb1022415fe46f8822027cef910b447a80473d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:53.372594Z","signature_b64":"8FvtahmN47XhYR2FoISItLosqq14IXQSDI5e98zAK5bsb1wnboV+ZJQAAgeSbY9M/HCAKhcKm4CdwJqj9NjDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b65ed9b58be0062753336deb2bb1022415fe46f8822027cef910b447a80473d","last_reissued_at":"2026-07-05T11:56:53.372059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:53.372059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.20616","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:56:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iCeIvzD1536pewo6BvbajncqUzTFSaje0qeRP6IBtCi3di3r75C316xe0TQjDvwwKt9xHw/Ptf/x/IiX5TEvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:31:59.552825Z"},"content_sha256":"f0e96a1adc340b06b38f50d0a6caef7a9cce7f68c81e74548b6e8a1cb64d752a","schema_version":"1.0","event_id":"sha256:f0e96a1adc340b06b38f50d0a6caef7a9cce7f68c81e74548b6e8a1cb64d752a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:HNS63G2YXYAGE5JTG3PLFOYQEJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Caiming Xiong, Huan Wang, Jianguo Zhang, Juntao Tan, Liangwei Yang, Ming Zhu, Rithesh Murthy, Shelby Heinecke, Shirley Kokane, Silvio Savarese, Tulika Manoj Awalgaonkar, Weiran Yao, Zhiwei Liu, Zuxin Liu","submitted_at":"2025-02-28T00:43:35Z","abstract_excerpt":"Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI models to access and interpret a user's private data (e.g., conversation history, user-AI interactions, app usage) to understand personal details such as biographical information, preferences, and social connections. However, due to the sensitive nature of such data, there are no publicly available datasets that allow us to assess an AI model's ability to understand users through direct access to personal information."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20616","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/2502.20616/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":"2026-07-05T11:56:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lXSDGlsnRJMP3A4OWHhMBnSeoYNcrgDDW62Pupc2bclvG2lJFeagvG20XNaqarxSYTzK//6gTVb03QtSthTVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T12:31:59.553193Z"},"content_sha256":"50a626e362499376cf66388fc17c89e14eac8465c70aeea8e045898374fdeaf9","schema_version":"1.0","event_id":"sha256:50a626e362499376cf66388fc17c89e14eac8465c70aeea8e045898374fdeaf9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/bundle.json","state_url":"https://pith.science/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/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":"2026-07-07T12:31:59Z","links":{"resolver":"https://pith.science/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ","bundle":"https://pith.science/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/bundle.json","state":"https://pith.science/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HNS63G2YXYAGE5JTG3PLFOYQEJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HNS63G2YXYAGE5JTG3PLFOYQEJ","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":"4c309b7e7dd44cd09d96619cb8c3f2664b33a29cb2c6dedc35e6bb372e921d03","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-28T00:43:35Z","title_canon_sha256":"4ae00a42d6d7409d1cdda25e10d1047439692215d17433f5b32450de633dc4b4"},"schema_version":"1.0","source":{"id":"2502.20616","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.20616","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"arxiv_version","alias_value":"2502.20616v2","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20616","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_12","alias_value":"HNS63G2YXYAG","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_16","alias_value":"HNS63G2YXYAGE5JT","created_at":"2026-07-05T11:56:53Z"},{"alias_kind":"pith_short_8","alias_value":"HNS63G2Y","created_at":"2026-07-05T11:56:53Z"}],"graph_snapshots":[{"event_id":"sha256:50a626e362499376cf66388fc17c89e14eac8465c70aeea8e045898374fdeaf9","target":"graph","created_at":"2026-07-05T11:56:53Z","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/2502.20616/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI models to access and interpret a user's private data (e.g., conversation history, user-AI interactions, app usage) to understand personal details such as biographical information, preferences, and social connections. However, due to the sensitive nature of such data, there are no publicly available datasets that allow us to assess an AI model's ability to understand users through direct access to personal information.","authors_text":"Caiming Xiong, Huan Wang, Jianguo Zhang, Juntao Tan, Liangwei Yang, Ming Zhu, Rithesh Murthy, Shelby Heinecke, Shirley Kokane, Silvio Savarese, Tulika Manoj Awalgaonkar, Weiran Yao, Zhiwei Liu, Zuxin Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-28T00:43:35Z","title":"PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20616","kind":"arxiv","version":2},"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:f0e96a1adc340b06b38f50d0a6caef7a9cce7f68c81e74548b6e8a1cb64d752a","target":"record","created_at":"2026-07-05T11:56:53Z","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":"4c309b7e7dd44cd09d96619cb8c3f2664b33a29cb2c6dedc35e6bb372e921d03","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-02-28T00:43:35Z","title_canon_sha256":"4ae00a42d6d7409d1cdda25e10d1047439692215d17433f5b32450de633dc4b4"},"schema_version":"1.0","source":{"id":"2502.20616","kind":"arxiv","version":2}},"canonical_sha256":"3b65ed9b58be0062753336deb2bb1022415fe46f8822027cef910b447a80473d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3b65ed9b58be0062753336deb2bb1022415fe46f8822027cef910b447a80473d","first_computed_at":"2026-07-05T11:56:53.372059Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:56:53.372059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8FvtahmN47XhYR2FoISItLosqq14IXQSDI5e98zAK5bsb1wnboV+ZJQAAgeSbY9M/HCAKhcKm4CdwJqj9NjDDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:56:53.372594Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.20616","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f0e96a1adc340b06b38f50d0a6caef7a9cce7f68c81e74548b6e8a1cb64d752a","sha256:50a626e362499376cf66388fc17c89e14eac8465c70aeea8e045898374fdeaf9"],"state_sha256":"de562573b0abcc21dd7ae1181947f1fa055e00aec576bd77a380141cf13a0cab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EBEZsgxQ7p5pMk4lKiBiQOIC565X6etDwxh9dkMR1tc/RBKELeEFJyFrDGV5f4oRGP8u8sgaqtj+179m0kX/Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T12:31:59.555167Z","bundle_sha256":"ad1add5dc0e8d5dd1b01ffc63f93a8b1a91bc7f70e9e1b0f6dec2e522caada7b"}}