Paper Citation Record · LEDGER

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.28236.

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

pith.paper-citation-record.v1
2607.28236 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T13:43:42.026890Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact0
verified fuzzy0
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c409b43-e3aa-4b65-923c-77e7f2bd4cec · outbound

This paper cites Peters et al.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Peters et al

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.380974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca6d24bb-dcf2-478c-bb67-9e1a1606891e · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.473552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06391bce-c2e5-41a6-bfe0-cc22b9860628 · outbound

This paper cites LLMs are Also Effective Embedding Models: An In-depth Overview.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising LLMs are Also Effective Embedding Models: An In-depth Overview

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.636911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c105db-5717-496f-846a-b6eac47c51d9 · outbound

This paper cites Sentence-BERT: Sentence embeddings using Siamese BERT-networks.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Sentence-BERT: Sentence embeddings using Siamese BERT-networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.709479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04cb07b5-d140-4f2b-989a-17995ee8a2e1 · outbound

This paper cites Bowman, Gabor Angeli, Christopher Potts, and Christopher D.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Bowman, Gabor Angeli, Christopher Potts, and Christopher D

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.817112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e9d3fc3-0991-4443-93f2-1d8717a01dfa · outbound

This paper cites Llm applications: Current paradigms and the next frontier.arXiv preprint arXiv:2503.04596, 2025.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Llm applications: Current paradigms and the next frontier.arXiv preprint arXiv:2503.04596, 2025

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.905607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82c45797-a14a-4d1f-997f-77a1c0d20726 · outbound

This paper cites Interpreting the robustness of neural NLP models to textual perturbations.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Interpreting the robustness of neural NLP models to textual perturbations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.975293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c6ea075-142b-4a54-bf2e-e8a38b832bec · outbound

This paper cites Multilingual e5 text embeddings: A technical report, 2024.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Multilingual e5 text embeddings: A technical report, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.049423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3b4282b-f95e-4743-b279-b9127130c29f · outbound

This paper cites SimCSE: Simple contrastive learning of sentence embeddings.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising SimCSE: Simple contrastive learning of sentence embeddings

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.114159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6850f805-d2fd-4a42-b949-aa858a58579c · outbound

This paper cites Robustsentembed: Robust sentence embeddings using adversarial self-supervised contrastive learning.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Robustsentembed: Robust sentence embeddings using adversarial self-supervised contrastive learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.191101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76db792f-0b00-425b-b7ce-0f888a7f1ab4 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.254023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38345798-60bb-4f26-84e0-afc6a6892ea2 · outbound

This paper cites Mteb: Massive text embedding benchmark.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Mteb: Massive text embedding benchmark

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.334782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ceee0c2-f54b-4bf2-b04e-a7b8fabc3b36 · outbound

This paper cites Tsdae: Using transformer-based sequential denoising auto- encoderfor unsupervised sentence embedding learning.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Tsdae: Using transformer-based sequential denoising auto- encoderfor unsupervised sentence embedding learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.379621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93835ce5-0e1c-44f9-9560-cac7048687bb · outbound

This paper cites an unresolved cited work.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.461393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a57cdaa8-cba1-481e-adbd-4c992feffcb2 · outbound

This paper cites Representation learning with contrastive predictive coding, 2019.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Representation learning with contrastive predictive coding, 2019

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.578051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ec54083-38d8-4af7-bed7-3aad2e1faed0 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.620456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a41e3bd-ea7b-47c0-b779-bb332bce80b4 · outbound

This paper cites Transformers: State-of-the-art natural language processing.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Transformers: State-of-the-art natural language processing

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.700184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff8cda29-66d6-4ae8-857a-b787dd60fb3c · outbound

This paper cites Decoupled weight decay regularization, 2019.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Decoupled weight decay regularization, 2019

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.734808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38fb55e-d8ce-4c41-beed-b6d5a55ca8c1 · outbound

This paper cites Datasets: A community library for natural language processing.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Datasets: A community library for natural language processing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.820914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a120327d-ccb3-4340-9c4d-7be48718610f · outbound

This paper cites Data Structures for Statistical Computing in Python.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Data Structures for Statistical Computing in Python

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.902567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c453f7b-ab5e-4842-a9a7-9f934695d4d2 · outbound

This paper cites Harris, K.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Harris, K

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:41.947376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fcd9fe9-d4d7-4d33-b7cb-9f5f7417049a · outbound

This paper cites NLTK: The natural language toolkit.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising NLTK: The natural language toolkit

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:42.026890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20f7531c-df14-49c6-b1b0-5055ac79c1dc · outbound

This paper cites an unresolved cited work.

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising Unresolved cited work

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-07-31T13:43:40.566494Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.