Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1910.00727.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T13:12:23.874820Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:27:29.537092Z
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 1ad5cbad-ba99-49fe-ba3c-31b09de72b65 · inbound
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios cites this paper.
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T13:12:23.874820Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64c3219e-938a-4c7e-8956-1d6e305046bf · inbound
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:27:29.538544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3575299f-bf68-4248-b16f-ae313fe05c8d · inbound
Data Provenance for Image Auto-Regressive Generation cites this paper.
Data Provenance for Image Auto-Regressive Generation Analyzing and Improving Neural Networks by Generating Semantic Counterexamples through Differentiable Rendering
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T10:34:36.603800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.