Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Verifiable evaluations of machine learning models using zkSNARKs
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.02675.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:24:06.581455Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T01:57:32.613339Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 7117ce14-b77a-45cc-85cb-927f73be15b6 · inbound
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? cites this paper.
In Which Areas of Technical AI Safety Could Geopolitical Rivals Cooperate? Verifiable evaluations of machine learning models using zkSNARKs
Reference 93
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:24:06.581455Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7beb0e87-c7d3-4681-84dd-c72ffa70d061 · inbound
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks cites this paper.
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks Verifiable evaluations of machine learning models using zkSNARKs
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T06:06:57.514128Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4fadef4e-f29e-419e-9860-d263fec3f7bb · inbound
Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments cites this paper.
Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments Verifiable evaluations of machine learning models using zkSNARKs
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:45:03.990514Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ef317c6-0d77-448d-895a-325d29630261 · inbound
Private, Verifiable, and Auditable AI Systems cites this paper.
Private, Verifiable, and Auditable AI Systems Verifiable evaluations of machine learning models using zkSNARKs
Reference 274
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:43:59.498751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03d477e1-acf5-44eb-8171-0f9d812f8fc8 · inbound
Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification cites this paper.
Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification Verifiable evaluations of machine learning models using zkSNARKs
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:30:52.199340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2f61b76b-1dd9-4c00-aed0-6ccb1687b21a · inbound
Zero knowledge verification for frontier AI training is possible cites this paper.
Zero knowledge verification for frontier AI training is possible Verifiable evaluations of machine learning models using zkSNARKs
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T08:26:47.806295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 83d0a000-3cbf-4c26-9c86-6e7225b07dc3 · inbound
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting cites this paper.
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Verifiable evaluations of machine learning models using zkSNARKs
Reference 100
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T01:57:32.614700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9d3a6239-da5e-4ee1-b564-dd9ab937f56a · inbound
Privacy-Preserving AI Verification via Minimal Information Disclosure cites this paper.
Privacy-Preserving AI Verification via Minimal Information Disclosure Verifiable evaluations of machine learning models using zkSNARKs
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:09:07.307257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 52060627-6a10-41fb-830e-8cbd19c26165 · inbound
NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs cites this paper.
NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs Verifiable evaluations of machine learning models using zkSNARKs
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T13:45:43.974198Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4f5c83d-0f1f-4f57-9301-014ec67d760e · inbound
Repeated-Game Security for Restaking-Based Verifiable Inference cites this paper.
Repeated-Game Security for Restaking-Based Verifiable Inference Verifiable evaluations of machine learning models using zkSNARKs
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T04:25:21.588537Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.