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Paper Citation Record · LEDGER

Faster Fuzzing: Reinitialization with Deep Neural Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1711.02807.

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

pith.paper-citation-record.v1
1711.02807 v1

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:01:15.202533Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T15:21:57.510073Z

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 f87bdb39-7cb4-4a56-a8f6-34163b61bc7e · inbound

A systematic review of fuzzing based on machine learning techniques cites this paper.

A systematic review of fuzzing based on machine learning techniques Faster Fuzzing: Reinitialization with Deep Neural Models

Reference 2015

Resolution
verified exact
local_arxiv, observed 2026-08-14T15:21:57.517137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:21:57.406450Z digest=sha256:1349488dda3277c6993de8b47226660becadcb7c0d5a695eabba040dfed75d89

Observation 64fee870-3cbd-4e25-a23c-ad332e4e229f · inbound

Using quantum annealing to generate test cases for cyber-physical systems cites this paper.

Using quantum annealing to generate test cases for cyber-physical systems Faster Fuzzing: Reinitialization with Deep Neural Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T05:01:15.202533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:01:15.202533Z digest=sha256:5ea1fdc891e765be7796ed91cd6d014107cff368f2f50613175430af2500713a