{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:D62KHUTIG3XZDRZSLVKAAWK7OA","short_pith_number":"pith:D62KHUTI","canonical_record":{"source":{"id":"2310.20081","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T23:40:41Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"4c6afb8fd602c1aca87a51e3f95c096230a8c7129d0bb0843b5f4675c4a271d6","abstract_canon_sha256":"9d9395ba70077aca844343c30808cdfc4086a1613e38ba44b50e18bef648faf4"},"schema_version":"1.0"},"canonical_sha256":"1fb4a3d26836ef91c7325d5400595f703908c169be7875ee7d89dc189da492be","source":{"kind":"arxiv","id":"2310.20081","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.20081","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"arxiv_version","alias_value":"2310.20081v1","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20081","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_12","alias_value":"D62KHUTIG3XZ","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_16","alias_value":"D62KHUTIG3XZDRZS","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_8","alias_value":"D62KHUTI","created_at":"2026-07-05T07:07:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:D62KHUTIG3XZDRZSLVKAAWK7OA","target":"record","payload":{"canonical_record":{"source":{"id":"2310.20081","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T23:40:41Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"4c6afb8fd602c1aca87a51e3f95c096230a8c7129d0bb0843b5f4675c4a271d6","abstract_canon_sha256":"9d9395ba70077aca844343c30808cdfc4086a1613e38ba44b50e18bef648faf4"},"schema_version":"1.0"},"canonical_sha256":"1fb4a3d26836ef91c7325d5400595f703908c169be7875ee7d89dc189da492be","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:23.717573Z","signature_b64":"v3zCahFrSAU6eoPAEqZj0oIw0DQFTrMnL9J6vD6xwR4uSar4l2OEVjOF4DsdHQXRkaoHEkuzCU0slXF6I08jAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fb4a3d26836ef91c7325d5400595f703908c169be7875ee7d89dc189da492be","last_reissued_at":"2026-07-05T07:07:23.717057Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:23.717057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.20081","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:07:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0eSisomrFqP6s35dv62AFK9VqVcdzldB5sD3ddfbRKo+Etfe96/dj4R9BtQ84Mur7XwF3p6O4dZ2C8v29giBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T16:42:29.000687Z"},"content_sha256":"dd1d5841c20aba148b730a624c74cb03842828b9d5f4dd7d0292e90b7baf556b","schema_version":"1.0","event_id":"sha256:dd1d5841c20aba148b730a624c74cb03842828b9d5f4dd7d0292e90b7baf556b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:D62KHUTIG3XZDRZSLVKAAWK7OA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CL","authors_text":"Abhinav Sethy, Arshdeep Singh, Chris Richardson, Kellen Gillespie, Omar Zia Khan, Sudipta Kar, Yao Zhang, Zeynab Raeesy","submitted_at":"2023-10-30T23:40:41Z","abstract_excerpt":"Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20081","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/2310.20081/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":"2026-07-05T07:07:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WPANXRmL2jviTs8xxJ3u/P6MjQAUgCvHz+eGMhS7rOnOzB0lPaqJtdSg84X+eu5HrYOmRQtgafJgE3VPC6G/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T16:42:29.001108Z"},"content_sha256":"85365d802d297b94b337420b68b04384b1b0b1a811731fa21b58f5be52864d6b","schema_version":"1.0","event_id":"sha256:85365d802d297b94b337420b68b04384b1b0b1a811731fa21b58f5be52864d6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/bundle.json","state_url":"https://pith.science/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/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":"2026-07-27T16:42:29Z","links":{"resolver":"https://pith.science/pith/D62KHUTIG3XZDRZSLVKAAWK7OA","bundle":"https://pith.science/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/bundle.json","state":"https://pith.science/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D62KHUTIG3XZDRZSLVKAAWK7OA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:D62KHUTIG3XZDRZSLVKAAWK7OA","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":"9d9395ba70077aca844343c30808cdfc4086a1613e38ba44b50e18bef648faf4","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T23:40:41Z","title_canon_sha256":"4c6afb8fd602c1aca87a51e3f95c096230a8c7129d0bb0843b5f4675c4a271d6"},"schema_version":"1.0","source":{"id":"2310.20081","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.20081","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"arxiv_version","alias_value":"2310.20081v1","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20081","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_12","alias_value":"D62KHUTIG3XZ","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_16","alias_value":"D62KHUTIG3XZDRZS","created_at":"2026-07-05T07:07:23Z"},{"alias_kind":"pith_short_8","alias_value":"D62KHUTI","created_at":"2026-07-05T07:07:23Z"}],"graph_snapshots":[{"event_id":"sha256:85365d802d297b94b337420b68b04384b1b0b1a811731fa21b58f5be52864d6b","target":"graph","created_at":"2026-07-05T07:07:23Z","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/2310.20081/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges b","authors_text":"Abhinav Sethy, Arshdeep Singh, Chris Richardson, Kellen Gillespie, Omar Zia Khan, Sudipta Kar, Yao Zhang, Zeynab Raeesy","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T23:40:41Z","title":"Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20081","kind":"arxiv","version":1},"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:dd1d5841c20aba148b730a624c74cb03842828b9d5f4dd7d0292e90b7baf556b","target":"record","created_at":"2026-07-05T07:07:23Z","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":"9d9395ba70077aca844343c30808cdfc4086a1613e38ba44b50e18bef648faf4","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-30T23:40:41Z","title_canon_sha256":"4c6afb8fd602c1aca87a51e3f95c096230a8c7129d0bb0843b5f4675c4a271d6"},"schema_version":"1.0","source":{"id":"2310.20081","kind":"arxiv","version":1}},"canonical_sha256":"1fb4a3d26836ef91c7325d5400595f703908c169be7875ee7d89dc189da492be","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1fb4a3d26836ef91c7325d5400595f703908c169be7875ee7d89dc189da492be","first_computed_at":"2026-07-05T07:07:23.717057Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:23.717057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v3zCahFrSAU6eoPAEqZj0oIw0DQFTrMnL9J6vD6xwR4uSar4l2OEVjOF4DsdHQXRkaoHEkuzCU0slXF6I08jAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:23.717573Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.20081","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd1d5841c20aba148b730a624c74cb03842828b9d5f4dd7d0292e90b7baf556b","sha256:85365d802d297b94b337420b68b04384b1b0b1a811731fa21b58f5be52864d6b"],"state_sha256":"d507158b2c306b3322c43acd15b3f84017ab4461bbc4b07f3602f4ba137b9484"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uLLjbbRAOE3HYe8k15RHEBoB2HPz+y0NK48672hddrkAYRzivAbHQDlsjr5lmfcoJtTXdvX/8HnBO1qSFkrvAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-27T16:42:29.003625Z","bundle_sha256":"7cfa870b71d351377ea0a25fe5ccfeba90ee2dc8bbe6afb15536dc91847ca336"}}