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Paper Citation Record · LEDGER

Adversarial Text Generation Without Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.06640.

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

pith.paper-citation-record.v1
1810.06640 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:38:07.604283Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T21:58:32.378983Z

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 4b5a5f9d-b7f8-4e33-86a5-411951543e79 · inbound

AutoML: A Survey of the State-of-the-Art cites this paper.

AutoML: A Survey of the State-of-the-Art Adversarial Text Generation Without Reinforcement Learning

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-14T15:38:07.604283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:38:07.604283Z digest=sha256:2355390d043681bde68d0abb2ca46857a3386699eef6ebfdfa9afac59689c571

Observation 8824bf89-8c31-45cf-8403-73cb5f55fa42 · inbound

VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity cites this paper.

VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity Adversarial Text Generation Without Reinforcement Learning

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-10T21:58:32.384487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-10T21:58:31.078443Z digest=sha256:fe54b876fd9cdd7bf3bed640e71d41af52c115e06124f436a11ec11b5dd2a43b