Paper Citation Record · LEDGER

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2608.02348.

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

pith.paper-citation-record.v1
2608.02348 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:55:38.352768Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

33 of 33 outbound references displayed

verified exact0
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation afbb6e2a-e4c5-4104-8b90-2412de94e357 · outbound

This paper cites Classification and online clustering of zero-day malware,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Classification and online clustering of zero-day malware,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:34.771468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bff44b33-1e31-433c-b93d-e86a87011441 · outbound

This paper cites Scalable, behavior-based malware clustering.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Scalable, behavior-based malware clustering

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:34.917248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4632239-56d0-4cf9-8d88-485e4fb3f873 · outbound

This paper cites Clustering malware at scale: A first full-benchmark study,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Clustering malware at scale: A first full-benchmark study,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.064104Z

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Unavailable: canonical work link unavailable.

Observation 4bc0cff8-dde4-41e1-919c-dd7703ec472c · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Bootstrap your own latent-a new approach to self-supervised learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.213537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c697bc22-dfbb-4148-9e8a-b6a6ccf50fbb · outbound

This paper cites Exploring simple siamese representation learn- ing,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Exploring simple siamese representation learn- ing,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.366227Z

Source-reported events for the cited work

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Observation ee7564b7-2b20-49dc-b5c5-d3dabe3a9a68 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understand- ing,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Bert: Pre- training of deep bidirectional transformers for language understand- ing,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.524278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c92f9e55-8f52-40a1-a5de-bdd21236f816 · outbound

This paper cites Vime: Extending the success of self-and semi-supervised learning to tabular domain,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Vime: Extending the success of self-and semi-supervised learning to tabular domain,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.693733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1530ced2-d6f2-4b80-8df6-b3f05326a37c · outbound

This paper cites SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.733278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f147a171-9342-484a-ad7b-454109062903 · outbound

This paper cites Switchtab: Switched autoencoders are effective tabular learners,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Switchtab: Switched autoencoders are effective tabular learners,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:35.890689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2269b473-ef55-4465-a563-c822e68e2f1a · outbound

This paper cites Subtab: Subsetting features of tabular data for self-supervised representation learning,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Subtab: Subsetting features of tabular data for self-supervised representation learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.009741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f6b9831-c076-4898-a630-15972e376afe · outbound

This paper cites Semi-supervised malware clustering based on the weight of bytecode and api,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Semi-supervised malware clustering based on the weight of bytecode and api,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.100344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e755c05d-1022-4e80-9721-937fc66072b7 · outbound

This paper cites Cougar: clustering of unknown malware using genetic algorithm routines,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Cougar: clustering of unknown malware using genetic algorithm routines,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.196175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8b4a68e-bdee-47e2-8c0c-b035912b3ad1 · outbound

This paper cites Online clustering of known and emerging malware families,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Online clustering of known and emerging malware families,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.285506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bca5d67a-4ca9-4cfb-bb64-155a3a6c65a5 · outbound

This paper cites Scalable malware clustering using multi-stage tree parallelization,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Scalable malware clustering using multi-stage tree parallelization,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.378638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92e33646-90ef-411d-8ccb-dbced243c9b6 · outbound

This paper cites Scaling multi- objective optimization for clustering malware,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Scaling multi- objective optimization for clustering malware,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.472795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1f5aa7b-6baa-48d4-9560-8056c5f7ae7e · outbound

This paper cites Cluster analysis and concept drift detection in malware: A. mishra, m. stamp,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Cluster analysis and concept drift detection in malware: A. mishra, m. stamp,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.565486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e070009-2a5e-4014-9e63-4075a111af63 · outbound

This paper cites Malware self-supervised graph contrastive learning with data augmentation,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Malware self-supervised graph contrastive learning with data augmentation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.653246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae50245c-c1f6-41af-80ac-7505eeee26b0 · outbound

This paper cites An graph neural network approach with self- supervised learning for malware detection,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning An graph neural network approach with self- supervised learning for malware detection,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.745225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64dc1de8-d5b7-4005-b7d9-84582cf9c141 · outbound

This paper cites Malssl—self-supervised learning for accurate and label-efficient malware classification,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Malssl—self-supervised learning for accurate and label-efficient malware classification,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.829927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a9d7f8d-7315-4c11-ab95-182d8467a6a3 · outbound

This paper cites Self-supervised contrastive representation learning for classifying internet of things malware,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Self-supervised contrastive representation learning for classifying internet of things malware,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:36.923506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6126728-fa33-479f-93a0-0cdcd83efc9c · outbound

This paper cites Malsort: Lightweight and efficient image-based malware classifica- tion using masked self-supervised framework with swin transformer,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Malsort: Lightweight and efficient image-based malware classifica- tion using masked self-supervised framework with swin transformer,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.018773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c39efd9-8847-4bad-bc70-65cc861c6896 · outbound

This paper cites contrast- bert: Behavioral anomaly detection for malware using contrastive learning,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning contrast- bert: Behavioral anomaly detection for malware using contrastive learning,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.146369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac996248-b2b8-4515-9ce3-460c1506f206 · outbound

This paper cites Bibe: A self- supervised contrastive learning architecture for malware detection,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Bibe: A self- supervised contrastive learning architecture for malware detection,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.308198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c32cef-e77b-4fbe-b5eb-fee391f0676c · outbound

This paper cites Nebula: Self-attention for dynamic malware analysis,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Nebula: Self-attention for dynamic malware analysis,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.388993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b97d8f-8aa5-45c4-89ab-5082afbbae31 · outbound

This paper cites {CADE}: Detecting and explaining concept drift sam- ples for security applications,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning {CADE}: Detecting and explaining concept drift sam- ples for security applications,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.484328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01ea4344-9744-4f20-9682-fc10d633ddbd · outbound

This paper cites Evoliot: A self-supervised contrastive learning framework for detecting and characterizing evolving iot malware variants,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Evoliot: A self-supervised contrastive learning framework for detecting and characterizing evolving iot malware variants,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.570297Z

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Unavailable: canonical work link unavailable.

Observation 3fcfbc57-f9a0-4cb6-88a1-3a1f02ea1869 · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Barlow twins: Self-supervised learning via redundancy reduction,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.660557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6214e521-e5d5-4077-9929-11e395e74773 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.758033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3c556e7-dcdc-49ba-a2fd-88dd4bf53e56 · outbound

This paper cites Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detec- tion,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detec- tion,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:37.941524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f03c8b6-0a69-49b4-9e9b-2d65fea93602 · outbound

This paper cites EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:38.129559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 178d7ae4-2310-4e20-aa06-05be32d08fe5 · outbound

This paper cites Avclass: A tool for massive malware labeling,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Avclass: A tool for massive malware labeling,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:38.205101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6900be2c-b008-43dd-a976-c5bd48d3f573 · outbound

This paper cites Bodmas: An open dataset for learning based temporal analysis of pe malware,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Bodmas: An open dataset for learning based temporal analysis of pe malware,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T08:55:38.289049Z

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Unavailable: canonical work link unavailable.

Observation 4ab8437b-9b79-4d33-ac7c-a8a4b83d1605 · outbound

This paper cites Malmixer: Few-shot mal- ware classification with retrieval-augmented semi-supervised learn- ing,.

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning Malmixer: Few-shot mal- ware classification with retrieval-augmented semi-supervised learn- ing,

Reference 33

Resolution: malformed identifier; no resolver link, observed 2026-08-04T08:55:38.352768Z

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Pith citing papers

No inbound Pith citation observations are available.