Paper Citation Record · LEDGER

Adversarial Training Methods for Semi-Supervised Text Classification

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1605.07725.

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

pith.paper-citation-record.v1
1605.07725 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:46:44.819791Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:26:28.462824Z

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 0d8ac99c-31a2-4c2d-87e5-f7390d380901 · inbound

XLNet: Generalized Autoregressive Pretraining for Language Understanding cites this paper.

XLNet: Generalized Autoregressive Pretraining for Language Understanding Adversarial Training Methods for Semi-Supervised Text Classification

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:29:27.519948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3b97ccef-05f5-4990-a997-dacfd4fd094a · inbound

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks cites this paper.

SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks Adversarial Training Methods for Semi-Supervised Text Classification

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:11:00.788753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03dd8a4f-ba1a-4827-9836-65fce42644f9 · inbound

ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model cites this paper.

ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model Adversarial Training Methods for Semi-Supervised Text Classification

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T15:46:44.819791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04aff88f-cdd0-4881-bcdc-8996dc36e3c1 · inbound

Unifying Adversarial Perturbation for Graph Neural Networks cites this paper.

Unifying Adversarial Perturbation for Graph Neural Networks Adversarial Training Methods for Semi-Supervised Text Classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T13:44:08.626668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e085fb-40fd-43e0-804a-0d79c7b28c69 · inbound

OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework cites this paper.

OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework Adversarial Training Methods for Semi-Supervised Text Classification

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T07:25:12.609569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 175ab6d8-6d09-414c-9fe7-e8d3fc5c65b2 · inbound

AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification cites this paper.

AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification Adversarial Training Methods for Semi-Supervised Text Classification

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:26:28.464664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.