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Paper Citation Record · LEDGER

Input Validation for Neural Networks via Runtime Local Robustness Verification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2002.03339.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2002.03339 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:38:34.886887Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T00:17:29.229010Z

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 b46951d7-57f8-4713-9744-6740c1e54649 · inbound

A New Perspective On AI Safety Through Control Theory Methodologies cites this paper.

A New Perspective On AI Safety Through Control Theory Methodologies Input Validation for Neural Networks via Runtime Local Robustness Verification

Reference 178

Resolution
unresolved
no resolver link, observed 2026-08-06T21:38:34.886887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:38:34.886887Z digest=sha256:6a7e40d9e4d8885f24c8476bcb23590bb529f1206eecdbdbc0a2d2a912f39f62

Observation 3bac70cd-3bb6-4018-8b8a-4bea4b08ff0a · inbound

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism cites this paper.

Scaling Neural Network Verification with Tensor Parallelism and Fully Sharded Data Parallelism Input Validation for Neural Networks via Runtime Local Robustness Verification

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:17:29.230630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T17:20:04.247977Z digest=sha256:650046c0704e4ffda2e56f29e2713214c71db5ca4ea7019f58dbe58fde893a92