{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PPMBUTRRQXVWHMAIVDMHFJNOSZ","short_pith_number":"pith:PPMBUTRR","canonical_record":{"source":{"id":"2604.26197","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-04-29T00:53:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c5d9d2e3b8f601d8c88eccf1b704453dedc27189d4afbe2778015c9686e652ba","abstract_canon_sha256":"a48c99cadd423f56bef2122474501a2d36a47fbeff05a60f7b2665dd9823e8d1"},"schema_version":"1.0"},"canonical_sha256":"7bd81a4e3185eb63b008a8d872a5ae965cb427d5db58613061b21926e33c34fe","source":{"kind":"arxiv","id":"2604.26197","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.26197","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"arxiv_version","alias_value":"2604.26197v2","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.26197","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_12","alias_value":"PPMBUTRRQXVW","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_16","alias_value":"PPMBUTRRQXVWHMAI","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_8","alias_value":"PPMBUTRR","created_at":"2026-05-27T01:04:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PPMBUTRRQXVWHMAIVDMHFJNOSZ","target":"record","payload":{"canonical_record":{"source":{"id":"2604.26197","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-04-29T00:53:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c5d9d2e3b8f601d8c88eccf1b704453dedc27189d4afbe2778015c9686e652ba","abstract_canon_sha256":"a48c99cadd423f56bef2122474501a2d36a47fbeff05a60f7b2665dd9823e8d1"},"schema_version":"1.0"},"canonical_sha256":"7bd81a4e3185eb63b008a8d872a5ae965cb427d5db58613061b21926e33c34fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:04:58.455401Z","signature_b64":"dI8m9rbPadnNdUAN4+94Tw6Eaku7CQ6U2d+swbBJsEbHwdAAcLXoAgz3tnIcHvSanr+4HaoWVbG+8ddFNFYiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bd81a4e3185eb63b008a8d872a5ae965cb427d5db58613061b21926e33c34fe","last_reissued_at":"2026-05-27T01:04:58.454743Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:04:58.454743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.26197","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":"2026-05-27T01:04:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kcGwFHsQ9DiOZd1K3+kCh2+WWsYQtMaWst03UZjVEbt7xv2pM92FFL4Xmiut/whG9fTkMW5Tgur7QvyosQLHCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T19:06:51.559072Z"},"content_sha256":"7d690d77221e365057ec98c533f711267748879e85f0cdb05b0acf7abff1bfe1","schema_version":"1.0","event_id":"sha256:7d690d77221e365057ec98c533f711267748879e85f0cdb05b0acf7abff1bfe1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PPMBUTRRQXVWHMAIVDMHFJNOSZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality.","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Emir Poyraz, Karthik Ramgopal, Praveen Kumar Bodigutla, Shangjin Zhang, Xiaofeng Wang, Xiaoyang Gu, Xie Lu, Ye Jin, Yvonne Li, Zhentao Xu","submitted_at":"2026-04-29T00:53:52Z","abstract_excerpt":"Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential. A central enabler of such capabilities is the agent's long-term semantic memory system, which extracts implicit and explicit signals from noisy longitudinal behavioral data, stores them in a structured form, and supports low-latency retrieval. Building industrial-grade long-term memory for LLM agents raises five challenges: scalability, low-latency retrieval, privacy constraints, adaptability, and observability. We introduce the Hierarchical Long"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"HLTM improves answer correctness and retrieval F1 significantly by more than 10%, while significantly advancing the Pareto frontier between query and indexing latency. HLTM has been deployed in LinkedIn's Hiring Assistant to power core personalization features in production hiring workflows.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the schema-aligned memory tree and adaptation mechanism generalize across diverse use cases and that the claimed >10% gains on LinkedIn's internal Hiring Assistant data reflect real-world improvements without undisclosed data selection or baseline choices.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"HLTM builds a hierarchical memory tree from longitudinal data to enable scalable, private, low-latency retrieval, delivering over 10% gains in answer correctness and retrieval F1 for LinkedIn's Hiring Assistant while improving the query-indexing latency tradeoff.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"460d19f0b33f857ea61f3cce5e7a32ad7f632c32ff15b2607210da7f7342729f"},"source":{"id":"2604.26197","kind":"arxiv","version":2},"verdict":{"id":"55b68b54-38f8-430b-a5cd-1540be7b5557","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T13:28:13.899470Z","strongest_claim":"HLTM improves answer correctness and retrieval F1 significantly by more than 10%, while significantly advancing the Pareto frontier between query and indexing latency. HLTM has been deployed in LinkedIn's Hiring Assistant to power core personalization features in production hiring workflows.","one_line_summary":"HLTM builds a hierarchical memory tree from longitudinal data to enable scalable, private, low-latency retrieval, delivering over 10% gains in answer correctness and retrieval F1 for LinkedIn's Hiring Assistant while improving the query-indexing latency tradeoff.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the schema-aligned memory tree and adaptation mechanism generalize across diverse use cases and that the claimed >10% gains on LinkedIn's internal Hiring Assistant data reflect real-world improvements without undisclosed data selection or baseline choices.","pith_extraction_headline":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.26197/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T00:40:45.255151Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:24:47.788660Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"3fc9a170e6d1ee9608880e0960a4e1e247c83b22ee7ade4667f9245ebab3e137"},"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":"55b68b54-38f8-430b-a5cd-1540be7b5557"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-27T01:04:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ohlGklQ/BfCHyWUCNNZXWuznX3tqv9kJ/OdyuExlKBVqTCdTQg3KfehxAvx+JE7ztZ3/mnhpgN3N3X13ZrzoDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T19:06:51.559980Z"},"content_sha256":"2e7661a41fddbf7f27fa27410f05b140aa4501f083d7864ee11157dc0f879dfe","schema_version":"1.0","event_id":"sha256:2e7661a41fddbf7f27fa27410f05b140aa4501f083d7864ee11157dc0f879dfe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/bundle.json","state_url":"https://pith.science/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/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":"2026-06-07T19:06:51Z","links":{"resolver":"https://pith.science/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ","bundle":"https://pith.science/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/bundle.json","state":"https://pith.science/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PPMBUTRRQXVWHMAIVDMHFJNOSZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PPMBUTRRQXVWHMAIVDMHFJNOSZ","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":"a48c99cadd423f56bef2122474501a2d36a47fbeff05a60f7b2665dd9823e8d1","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-04-29T00:53:52Z","title_canon_sha256":"c5d9d2e3b8f601d8c88eccf1b704453dedc27189d4afbe2778015c9686e652ba"},"schema_version":"1.0","source":{"id":"2604.26197","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.26197","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"arxiv_version","alias_value":"2604.26197v2","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.26197","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_12","alias_value":"PPMBUTRRQXVW","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_16","alias_value":"PPMBUTRRQXVWHMAI","created_at":"2026-05-27T01:04:58Z"},{"alias_kind":"pith_short_8","alias_value":"PPMBUTRR","created_at":"2026-05-27T01:04:58Z"}],"graph_snapshots":[{"event_id":"sha256:2e7661a41fddbf7f27fa27410f05b140aa4501f083d7864ee11157dc0f879dfe","target":"graph","created_at":"2026-05-27T01:04:58Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"HLTM improves answer correctness and retrieval F1 significantly by more than 10%, while significantly advancing the Pareto frontier between query and indexing latency. HLTM has been deployed in LinkedIn's Hiring Assistant to power core personalization features in production hiring workflows."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the schema-aligned memory tree and adaptation mechanism generalize across diverse use cases and that the claimed >10% gains on LinkedIn's internal Hiring Assistant data reflect real-world improvements without undisclosed data selection or baseline choices."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"HLTM builds a hierarchical memory tree from longitudinal data to enable scalable, private, low-latency retrieval, delivering over 10% gains in answer correctness and retrieval F1 for LinkedIn's Hiring Assistant while improving the query-indexing latency tradeoff."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality."}],"snapshot_sha256":"460d19f0b33f857ea61f3cce5e7a32ad7f632c32ff15b2607210da7f7342729f"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T00:40:45.255151Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:24:47.788660Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.26197/integrity.json","findings":[],"snapshot_sha256":"3fc9a170e6d1ee9608880e0960a4e1e247c83b22ee7ade4667f9245ebab3e137","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential. A central enabler of such capabilities is the agent's long-term semantic memory system, which extracts implicit and explicit signals from noisy longitudinal behavioral data, stores them in a structured form, and supports low-latency retrieval. Building industrial-grade long-term memory for LLM agents raises five challenges: scalability, low-latency retrieval, privacy constraints, adaptability, and observability. We introduce the Hierarchical Long","authors_text":"Emir Poyraz, Karthik Ramgopal, Praveen Kumar Bodigutla, Shangjin Zhang, Xiaofeng Wang, Xiaoyang Gu, Xie Lu, Ye Jin, Yvonne Li, Zhentao Xu","cross_cats":["cs.LG"],"headline":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-04-29T00:53:52Z","title":"Hierarchical Long-Term Semantic Memory for LinkedIn's Hiring Agent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.26197","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-07T13:28:13.899470Z","id":"55b68b54-38f8-430b-a5cd-1540be7b5557","model_set":{"reader":"grok-4.3"},"one_line_summary":"HLTM builds a hierarchical memory tree from longitudinal data to enable scalable, private, low-latency retrieval, delivering over 10% gains in answer correctness and retrieval F1 for LinkedIn's Hiring Assistant while improving the query-indexing latency tradeoff.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A schema-aligned hierarchical memory tree lets LLM agents store and retrieve long-term semantic knowledge with over 10% gains in correctness and retrieval quality.","strongest_claim":"HLTM improves answer correctness and retrieval F1 significantly by more than 10%, while significantly advancing the Pareto frontier between query and indexing latency. HLTM has been deployed in LinkedIn's Hiring Assistant to power core personalization features in production hiring workflows.","weakest_assumption":"That the schema-aligned memory tree and adaptation mechanism generalize across diverse use cases and that the claimed >10% gains on LinkedIn's internal Hiring Assistant data reflect real-world improvements without undisclosed data selection or baseline choices."}},"verdict_id":"55b68b54-38f8-430b-a5cd-1540be7b5557"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:7d690d77221e365057ec98c533f711267748879e85f0cdb05b0acf7abff1bfe1","target":"record","created_at":"2026-05-27T01:04:58Z","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":"a48c99cadd423f56bef2122474501a2d36a47fbeff05a60f7b2665dd9823e8d1","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2026-04-29T00:53:52Z","title_canon_sha256":"c5d9d2e3b8f601d8c88eccf1b704453dedc27189d4afbe2778015c9686e652ba"},"schema_version":"1.0","source":{"id":"2604.26197","kind":"arxiv","version":2}},"canonical_sha256":"7bd81a4e3185eb63b008a8d872a5ae965cb427d5db58613061b21926e33c34fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7bd81a4e3185eb63b008a8d872a5ae965cb427d5db58613061b21926e33c34fe","first_computed_at":"2026-05-27T01:04:58.454743Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-27T01:04:58.454743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dI8m9rbPadnNdUAN4+94Tw6Eaku7CQ6U2d+swbBJsEbHwdAAcLXoAgz3tnIcHvSanr+4HaoWVbG+8ddFNFYiDQ==","signature_status":"signed_v1","signed_at":"2026-05-27T01:04:58.455401Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.26197","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7d690d77221e365057ec98c533f711267748879e85f0cdb05b0acf7abff1bfe1","sha256:2e7661a41fddbf7f27fa27410f05b140aa4501f083d7864ee11157dc0f879dfe"],"state_sha256":"a84d2d4a19c147064fe5f43a1df10464c23c60660989897360535ab348ab1b01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FgZ1MozksqOQno/i1fX64/KxI1gtkOEwAZ74x7CvH+WJ0ls5hVumvvUOjQE5uwsLN91EETQGlXDkQ/sPDjptCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T19:06:51.563850Z","bundle_sha256":"a7f378636be8442065dbb3062215b449fa764da259dc13a73aef84ee0533fd9c"}}