Paper Citation Record · LEDGER

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

As of 22 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2504.21028.

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

pith.paper-citation-record.v1
2504.21028 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:31:13.705972Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

24 of 24 outbound references displayed

verified exact1
verified fuzzy12
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b968d38-8b96-48a4-a79b-06bde80b9c5c · outbound

This paper cites statista.com/statistics/1491093/new-malware-variants-detected- worldwide, 2025.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings statista.com/statistics/1491093/new-malware-variants-detected- worldwide, 2025

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-16T10:31:13.876346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e122746a-6ca4-4fab-99a2-82da6584ca92 · outbound

This paper cites LLMs Are Few-Shot In-Context Low-Resource Language Learners.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings LLMs Are Few-Shot In-Context Low-Resource Language Learners

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.620616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3c4d478-afb5-4623-846a-02096d5636d3 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Learning transferable visual models from natural language supervision

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.624974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd881d4-05bc-4b63-812f-9810a04bcc65 · outbound

This paper cites unb.ca/cic/datasets/andmal2020.html, 2020.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings unb.ca/cic/datasets/andmal2020.html, 2020

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:14.057552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 36483b44-e5c0-4110-81b7-b94162269b18 · outbound

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

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Bodmas: An open dataset for learning based temporal analysis of pe malware

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:14.044873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c413a956-0a22-4c31-9135-bc3041f84c05 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Model-agnostic meta-learning for fast adaptation of deep networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.636853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e88d6856-e33b-4bf3-b39d-d326b1d5daff · outbound

This paper cites Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.640893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4aa6b37-8404-4545-9440-8ee9d38fd4f2 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Representation Learning with Contrastive Predictive Coding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.644419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc58ae61-7138-4967-84df-0c3c313a47a9 · outbound

This paper cites Super- vised contrastive learning.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Super- vised contrastive learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.647898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2eab5c51-5d0a-4970-8bd6-dd6f72802938 · outbound

This paper cites Contrastive Learning with Hard Negative Samples.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Contrastive Learning with Hard Negative Samples

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.650945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac575d5-dd32-4e4c-8cb4-211487770226 · outbound

This paper cites Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few- shot learning.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Enhancing information maximization with distance-aware contrastive learning for source-free cross-domain few- shot learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:14.016525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 622f71e1-bd3c-4419-8a07-a98db9370dcd · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings LLaMA: Open and Efficient Foundation Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.658762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137346cb-e2cc-4b62-a0d1-37653c43209e · outbound

This paper cites Towards Diverse Temporal Grounding under Single Positive Labels.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Towards Diverse Temporal Grounding under Single Positive Labels

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.662700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e7fa1e8-55b0-406e-8fea-00d9d10219a3 · outbound

This paper cites Multimodal deep learning framework for malware detection.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Multimodal deep learning framework for malware detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:14.004863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5cfd1acb-18d2-40ca-b842-837938638037 · outbound

This paper cites Multimodal malware detection using deep learning.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Multimodal malware detection using deep learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.993580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 779e4dd6-491c-42ee-838b-6ab7f6f3efec · outbound

This paper cites Multimodal detection of hateful memes by applying a vision-language pre-training model.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Multimodal detection of hateful memes by applying a vision-language pre-training model

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.982055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 84767822-3272-4275-a002-0a7cc8868575 · outbound

This paper cites Meta-learning for few-shot intrusion detection.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Meta-learning for few-shot intrusion detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.970297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6ca02db-bcd2-44e6-8bdc-e6f80b23465d · outbound

This paper cites Meta-learning for cybersecurity tasks.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Meta-learning for cybersecurity tasks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.958761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61331b68-1e8a-492a-84c6-4c166ddd9a2c · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Distilling the Knowledge in a Neural Network

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T10:31:13.686517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8040440-d0b7-42c2-b262-809ae7b6e349 · outbound

This paper cites Knowledge distillation: A survey.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Knowledge distillation: A survey

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.944872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 66908eb3-ee97-463b-b762-65d7060b46f6 · outbound

This paper cites Mul- timodal machine learning: A survey and taxonomy.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Mul- timodal machine learning: A survey and taxonomy

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.931697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 352f61d1-0743-4642-aac0-8f4ad521cda3 · outbound

This paper cites A survey on deep learning for multimodal data fusion.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings A survey on deep learning for multimodal data fusion

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.918052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2dd4696a-7e2c-499a-8d35-0bccd0d2bccc · outbound

This paper cites Learning robust representations for multimodal data with knowledge distillation.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Learning robust representations for multimodal data with knowledge distillation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:31:13.904866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aaf2ef34-da99-46bf-983d-cdba858bc4b1 · outbound

This paper cites an unresolved cited work.

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-16T10:31:13.889725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.