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Paper Citation Record · LEDGER

Adversarial Training Methods for Semi-Supervised Text Classification

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 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 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:34:13.339981Z

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-18T01:29:27.427361Z digest=sha256:b4ce74dd839d3103d6caa5da690619bcf6603de4cee9e8c72d925f057098caca

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-14T17:11:00.639293Z digest=sha256:db9f9637aae31c1cfe6a42a5b169e065dddc24263e07408634242cc30b09f004

Observation 8e19abb6-7169-41cd-9fc8-a62253e89e96 · inbound

READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data cites this paper.

READ: Reinforcement-based Adversarial Learning for Text Classification with Limited Labeled Data Adversarial Training Methods for Semi-Supervised Text Classification

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T20:34:13.339981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:34:13.339981Z digest=sha256:9b93986aa0afbef4afbacb4c201832a26c4d05b6c7d9734e86643221c78a7238

Observation 9be44a50-7d5c-4c89-b605-17da96294928 · inbound

Enhancing Generalization in Chain of Thought Reasoning for Smaller Models cites this paper.

Enhancing Generalization in Chain of Thought Reasoning for Smaller Models Adversarial Training Methods for Semi-Supervised Text Classification

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T19:42:22.800170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:42:22.800170Z digest=sha256:939d1ebd5b5a7fd454f9ab1c2ecd6c3df7365aa45de1b8da8d2b2e9a6e1c1a4a

Observation f8b15bd7-02b0-48f6-bb1f-e8689998ffe9 · inbound

RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition cites this paper.

RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition Adversarial Training Methods for Semi-Supervised Text Classification

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T17:58:38.317213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T17:58:38.317213Z digest=sha256:907d3686b3e0bf042817b3258113f0d57b03d1d90e9fee55728e5fe0cc6dbc84

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.

source=pdf_text observed=2026-08-06T15:46:44.819791Z digest=sha256:13bffc38c719ba285a2537ff3ec6d27e90b46e91247cba9df9b706ce3a0dc672

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.

source=pdf_text observed=2026-08-05T13:44:08.626668Z digest=sha256:fd20c17e1ffda9cce2f64df861b06290dc38c22609ee7a6d6bbb3c57df935c45

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-15T07:22:24.713032Z digest=sha256:7ac9ebff18fd6d51172d6862ac3649e289c07726c17cc8153b9b11717014c062

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-28T10:09:09.678060Z digest=sha256:4244be2f20f786fbde7a3ab05201ef2b03a482f6230d820b5ba4821872793ad0