{"as_of":"2026-08-09T22:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0929a40ecc0ddf91d32003c089f51f92f15e7617fdeb50632790c50e8d701c21","coverage":[{"denominator":3,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T08:02:15.569763Z","state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T09:44:51.019544Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.18511","last_updated":"2026-06-17T05:04:40Z","snapshot_observed_at":"2026-08-03T08:02:02.807087Z","submitted_at":"2026-01-26T14:17:23Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B","version":2},"cited_work":{"arxiv_id":"2601.18511","doi":"10.48550/arxiv.2601.18511","metadata_source":"pith","pith_arxiv_id":"2601.18511","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B","venue":"cs.CR","work_id":"d44eac47-2a41-4d1c-a182-19ced44797ec","year":2026},"citing_paper":{"arxiv_id":"2606.11541","last_updated":"2026-06-10T01:04:58Z","snapshot_observed_at":"2026-08-06T20:16:24.743832Z","submitted_at":"2026-06-10T01:04:58Z","title":"WHET: Welding Homomorphic Encryption to Accelerator Architectures","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-06-27T09:44:51.019544Z"},"links":{"cited_paper":"/paper/2601.18511","citing_paper":"/paper/2606.11541"},"observation_digest":"sha256:23644a323b6d47444c53de3d0ffe03a59198de4926c59c112923d4ed252cdc01","observation_id":"20fe42f3-5902-427a-9d4b-acc23b7ad8a3","resolution":{"observed_at":"2026-06-27T09:50:48.879974Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2601.18511/citation-record","integrity":"/paper/2601.18511/integrity","json":"/paper/2601.18511/citation-record.json","paper":"/paper/2601.18511"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.00025","last_updated":"2024-05-08T02:20:09Z","snapshot_observed_at":"2026-08-02T08:43:09.148463Z","submitted_at":"2023-11-14T14:37:23Z","title":"Secure Transformer Inference Protocol","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00025","snapshot_observed_at":"2026-08-03T08:02:15.569763Z","title":"[ZWS+25] L","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.18511","last_updated":"2026-06-17T05:04:40Z","snapshot_observed_at":"2026-08-03T08:02:02.807087Z","submitted_at":"2026-01-26T14:17:23Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T08:02:15.569763Z"},"links":{"cited_paper":"/paper/2312.00025","citing_paper":"/paper/2601.18511"},"observation_digest":"sha256:5555660faa42f73fa85805377c43cf17b1a086c4bb3a2a7cb45cbcf16e383d91","observation_id":"b410f381-7cda-4e32-a430-0cb6e068cad9","resolution":{"observed_at":"2026-08-03T08:02:15.569763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-03T08:02:15.507573Z","title":"[TMS+23] H","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.18511","last_updated":"2026-06-17T05:04:40Z","snapshot_observed_at":"2026-08-03T08:02:02.807087Z","submitted_at":"2026-01-26T14:17:23Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T08:02:15.507573Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2601.18511"},"observation_digest":"sha256:9a580e35b782fe96123012fd2d112152d36f24936a88d285db9b3c7bd9d8e034","observation_id":"72f3a4a6-8e58-4f26-bc6c-3f05d4f8626f","resolution":{"observed_at":"2026-08-03T08:02:15.507573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T08:02:15.438640Z","title":"[HLL+23] X","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.18511","last_updated":"2026-06-17T05:04:40Z","snapshot_observed_at":"2026-08-03T08:02:02.807087Z","submitted_at":"2026-01-26T14:17:23Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T08:02:15.438640Z"},"links":{"citing_paper":"/paper/2601.18511"},"observation_digest":"sha256:a65e57e707016aca32584232ca1844d91e189680bbe2a718779bb3a8b1d9b0a9","observation_id":"65ce466a-5673-422e-8c8c-b7930b99c03d","resolution":{"observed_at":"2026-08-03T08:02:15.438640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.18511","last_updated":"2026-06-17T05:04:40Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-03T08:02:02.807087Z","submitted_at":"2026-01-26T14:17:23Z","title":"Scaling up FHE-based Privacy-Preserving ML: Higher Throughput, Longer Inputs for LLama-3-8B"},"reference_resolution":{"displayed":3,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":3},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 1 inbound Pith citation observation for arXiv:2601.18511."}