{"as_of":"2026-08-17T20:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2ecbff69d7438f8c99271d235223b16ae761549d107c76cd1e860f9fe8a1fbf5","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:24:06.581455Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T01:57:32.613339Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-16T12:24:06.581455Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.12914","last_updated":"2025-04-17T13:03:56Z","snapshot_observed_at":"2026-08-16T12:17:34.841696Z","submitted_at":"2025-04-17T13:03:56Z","title":"In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate?","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-16T12:24:06.581455Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2504.12914"},"observation_digest":"sha256:31676eff44459d99628d2007b30aa5f4eec8b2b0b7682ba43995cf4b3f09a14c","observation_id":"7117ce14-b77a-45cc-85cb-927f73be15b6","resolution":{"observed_at":"2026-08-16T12:24:06.581455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-16T06:06:57.514128Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19274","last_updated":"2025-05-26T14:20:07Z","snapshot_observed_at":"2026-08-16T14:47:43.362850Z","submitted_at":"2025-04-27T15:14:09Z","title":"TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-16T06:06:57.514128Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2504.19274"},"observation_digest":"sha256:2e074b2241999a8c21cd948dc0394389154acf4c0256315499a183d32435e02c","observation_id":"7beb0e87-c7d3-4681-84dd-c72ffa70d061","resolution":{"observed_at":"2026-08-16T06:06:57.514128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-06T21:45:03.990514Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23706","last_updated":"2025-06-30T10:29:42Z","snapshot_observed_at":"2026-08-14T11:00:26.968683Z","submitted_at":"2025-06-30T10:29:42Z","title":"Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-06T21:45:03.990514Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2506.23706"},"observation_digest":"sha256:30f3af3c8b397f693a261b3781d3203899d7c89b9f8a9915c8ded2492fa9632c","observation_id":"4fadef4e-f29e-419e-9860-d263fec3f7bb","resolution":{"observed_at":"2026-08-06T21:45:03.990514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-05T15:43:59.498751Z","title":"Verifiable evaluations of machine learning models using zksnarks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00085","last_updated":"2025-08-27T07:08:44Z","snapshot_observed_at":"2026-08-13T09:09:43.124306Z","submitted_at":"2025-08-27T07:08:44Z","title":"Private, Verifiable, and Auditable AI Systems","version":1},"reference_index":274,"source":"pdf_text","source_observed_at":"2026-08-05T15:43:59.498751Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2509.00085"},"observation_digest":"sha256:ae4f8f60b373e46884adc62a421e5289f65142333d9be24be0290187e6f48269","observation_id":"4ef317c6-0d77-448d-895a-325d29630261","resolution":{"observed_at":"2026-08-05T15:43:59.498751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":"2402.02675","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-07-03T01:57:32.613339Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","venue":null,"work_id":"60542d9e-9bc4-4598-98c8-f4498f5edba8","year":2024},"citing_paper":{"arxiv_id":"2604.04712","last_updated":"2026-04-06T14:26:14Z","snapshot_observed_at":"2026-08-17T05:18:37.931098Z","submitted_at":"2026-04-06T14:26:14Z","title":"Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T19:46:23.836745Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2604.04712"},"observation_digest":"sha256:fd828374a622da1a7fb3e9bf093c39fe202628b726a8defaae813a32fdd48504","observation_id":"03d477e1-acf5-44eb-8171-0f9d812f8fc8","resolution":{"observed_at":"2026-05-10T22:30:52.199340Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":"2402.02675","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-07-03T01:57:32.613339Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","venue":null,"work_id":"60542d9e-9bc4-4598-98c8-f4498f5edba8","year":2024},"citing_paper":{"arxiv_id":"2606.05433","last_updated":"2026-06-03T20:57:28Z","snapshot_observed_at":"2026-08-13T01:56:43.681028Z","submitted_at":"2026-06-03T20:57:28Z","title":"Zero knowledge verification for frontier AI training is possible","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T06:06:59.462289Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2606.05433"},"observation_digest":"sha256:64360bdb3b75042bceb724327d3c1acdd8f46ee645282064608dd46ac8b99c15","observation_id":"2f61b76b-1dd9-4c00-aed0-6ccb1687b21a","resolution":{"observed_at":"2026-07-02T08:26:47.806295Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":"2402.02675","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-07-03T01:57:32.613339Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","venue":null,"work_id":"60542d9e-9bc4-4598-98c8-f4498f5edba8","year":2024},"citing_paper":{"arxiv_id":"2606.09809","last_updated":"2026-06-08T17:55:02Z","snapshot_observed_at":"2026-08-13T06:18:06.017895Z","submitted_at":"2026-06-08T17:55:02Z","title":"Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-06-27T16:11:36.483820Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2606.09809"},"observation_digest":"sha256:d7a213a84c5ba0ccaa19771d3680ae66f8711188d746fb617f9a87b4061ac97b","observation_id":"83d0a000-3cbf-4c26-9c86-6e7225b07dc3","resolution":{"observed_at":"2026-07-03T01:57:32.614700Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-15T15:09:07.307257Z","title":"South, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02774","last_updated":"2026-08-03T18:18:11Z","snapshot_observed_at":"2026-08-17T16:19:18.046945Z","submitted_at":"2026-08-03T18:18:11Z","title":"Privacy-Preserving AI Verification via Minimal Information Disclosure","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T15:09:07.307257Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2608.02774"},"observation_digest":"sha256:1b934aeec02c5ecaf66ae173b24ff59ec94a137bf5c1a14032088a5cb5358160","observation_id":"9d3a6239-da5e-4ee1-b564-dd9ab937f56a","resolution":{"observed_at":"2026-08-15T15:09:07.307257Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-10T13:45:43.974198Z","title":"Verifiable evaluations of machine learning models using zkSNARKs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.07167","last_updated":"2026-08-07T12:36:52Z","snapshot_observed_at":"2026-08-17T05:56:27.013924Z","submitted_at":"2026-08-07T12:36:52Z","title":"NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T13:45:43.974198Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2608.07167"},"observation_digest":"sha256:593616500b6c353b98d19a29aa5371e6ebd4c56b6443c4d3dc3ce2a98c7df9ec","observation_id":"52060627-6a10-41fb-830e-8cbd19c26165","resolution":{"observed_at":"2026-08-10T13:45:43.974198Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02675","snapshot_observed_at":"2026-08-14T04:25:21.588537Z","title":"arXiv preprint arXiv:2402.02675 (2024),https://arxiv.org/abs/2402.02675","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.09055","last_updated":"2026-08-10T03:03:56Z","snapshot_observed_at":"2026-08-17T19:36:22.292576Z","submitted_at":"2026-08-10T03:03:56Z","title":"Repeated-Game Security for Restaking-Based Verifiable Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T04:25:21.588537Z"},"links":{"cited_paper":"/paper/2402.02675","citing_paper":"/paper/2608.09055"},"observation_digest":"sha256:3ecdd53ec9263da8ee5eae890d7d1c05d2397a03d18dbdad42d5e1594429030a","observation_id":"e4f5c83d-0f1f-4f57-9301-014ec67d760e","resolution":{"observed_at":"2026-08-14T04:25:21.588537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02675/citation-record","integrity":"/paper/2402.02675/integrity","json":"/paper/2402.02675/citation-record.json","paper":"/paper/2402.02675"},"outbound":[],"paper":{"arxiv_id":"2402.02675","last_updated":"2024-05-22T17:36:47Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T14:21:36.245621Z","submitted_at":"2024-02-05T02:21:11Z","title":"Verifiable evaluations of machine learning models using zkSNARKs"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.02675."}