Paper Citation Record · LEDGER

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code

As of 12 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2411.16561.

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

pith.paper-citation-record.v1
2411.16561 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:02:51.693819Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

31 of 31 outbound references displayed

verified exact0
verified fuzzy20
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f2867161-e9a9-4b1d-b61b-ee556568dd38 · outbound

This paper cites A systematic literature review on the cyber security,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code A systematic literature review on the cyber security,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.128018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 64e7cfa1-0fe6-43c0-9aad-a971e52630a3 · outbound

This paper cites Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.502323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bedf274-9ae3-4d6c-9c1a-92a8e6347f4b · outbound

This paper cites Predictive analytics on open big data for supporting smart transportation services,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Predictive analytics on open big data for supporting smart transportation services,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.105366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 27bfa6b6-f142-41a7-b8c5-715d8f4bff2b · outbound

This paper cites Malware detection and prevention using artificial intelligence techniques,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Malware detection and prevention using artificial intelligence techniques,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.090512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation acef081c-de55-40c2-a364-7b88731ea5c0 · outbound

This paper cites Bayesian hyperparameter optimization for deep neural network-based network intrusion detection,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Bayesian hyperparameter optimization for deep neural network-based network intrusion detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.076454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cdf1d3af-be45-414c-afc4-4cbadf1b27c2 · outbound

This paper cites Ai-based sensor information fusion for supporting deep supervised learning,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Ai-based sensor information fusion for supporting deep supervised learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.063338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5a2afc70-463b-4582-8397-459b73a8cb00 · outbound

This paper cites Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.050772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e48baf75-d218-4eb6-82e8-4b9f308f75d3 · outbound

This paper cites Predicting facebook-users’ personality based on status and linguistic features via flexible regression analysis techniques,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Predicting facebook-users’ personality based on status and linguistic features via flexible regression analysis techniques,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.036476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 691c8629-4c5e-42be-a49f-e0b834244fd2 · outbound

This paper cites A survey of machine learning for big code and naturalness,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code A survey of machine learning for big code and naturalness,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.557713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00501fda-13b8-4847-9c63-69aecb77ec4f · outbound

This paper cites CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.562550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33923419-e945-424a-9587-8336823a1afb · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.567101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a77f0ca-444e-46bc-989b-b7390d94f81d · outbound

This paper cites UniXcoder: Unified Cross-Modal Pre-training for Code Representation.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code UniXcoder: Unified Cross-Modal Pre-training for Code Representation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.571487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 632799de-5496-47ff-919f-a98be4bb25da · outbound

This paper cites Modeling and discover- ing vulnerabilities with code property graphs,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Modeling and discover- ing vulnerabilities with code property graphs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:52.014233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 49bf6f5c-f18d-43a5-9ac8-5405c947b870 · outbound

This paper cites VulDeePecker: A Deep Learning-Based System for Vulnerability Detection.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.581442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc3f8c7-9d7b-48f5-b0af-f3c83121120f · outbound

This paper cites Sysevr: A framework for using deep learning to detect software vulnerabilities,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Sysevr: A framework for using deep learning to detect software vulnerabilities,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.586366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fecf4e16-a369-40e9-90c2-ab6c31b72cb6 · outbound

This paper cites Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Devign: Effective vul- nerability identification by learning comprehensive program semantics via graph neural networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.590983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6606dc21-1b89-412f-9844-61f238755b6d · outbound

This paper cites Automated vulnerability detection in source code using deep representation learning,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Automated vulnerability detection in source code using deep representation learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.986305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation be62345e-fbc0-4365-8193-584cd5ac02c6 · outbound

This paper cites an unresolved cited work.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.601432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 075c963a-dc01-4443-bc4a-c1aaea2d7c7f · outbound

This paper cites Support vector machine,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Support vector machine,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.966363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e092dea-dfa2-41fe-ba5b-cfb13f7995a5 · outbound

This paper cites Genuer, J.-M.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Genuer, J.-M

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.954155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 762fb303-23c0-4fd1-bab4-802750cdea22 · outbound

This paper cites Xgboost: A scalable tree boosting system,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Xgboost: A scalable tree boosting system,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.620176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce45bb2a-4205-4078-b09e-5a86eb34968b · outbound

This paper cites Fault attacks on secure em- bedded software: Threats, design, and evaluation,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Fault attacks on secure em- bedded software: Threats, design, and evaluation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.933130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c5a9d56d-b194-4618-ab4b-5295e94dd167 · outbound

This paper cites Comparison and analysis of software vulnerability databases,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Comparison and analysis of software vulnerability databases,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.919559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 377847cf-753f-47da-b0bf-185227174cc3 · outbound

This paper cites Auto- mated vulnerability detection in source code using deep representation learning,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Auto- mated vulnerability detection in source code using deep representation learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.906151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 118fbe2e-e454-4887-af26-3a22741c60e3 · outbound

This paper cites Vulnerability detection in c/c++ source code with graph representation learning,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Vulnerability detection in c/c++ source code with graph representation learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.892736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44bf7d52-b7a2-447d-bd07-a12bba305690 · outbound

This paper cites Software vulner- ability detection using deep neural networks: a survey,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Software vulner- ability detection using deep neural networks: a survey,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T13:02:51.658667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58d1ffd1-2b64-4c2f-8be7-01fbdb200bc6 · outbound

This paper cites Vuldeelocator: A deep learning-based system for detecting and locating software vulnerabilities,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Vuldeelocator: A deep learning-based system for detecting and locating software vulnerabilities,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.868096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9296f8c4-b0dd-4700-9b9f-f6b9ef2e4c4f · outbound

This paper cites Vuldebert: A vulnerability detection system using bert,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Vuldebert: A vulnerability detection system using bert,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.855197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6640160-5635-4535-a370-cab00434ce00 · outbound

This paper cites Vulberta: Simplified source code pre-training for vulnerability detection,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Vulberta: Simplified source code pre-training for vulnerability detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.840336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3781773a-942a-44e5-b279-32f9543976af · outbound

This paper cites Chatgpt for vulnerability detection, classification, and repair: How far are we?,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Chatgpt for vulnerability detection, classification, and repair: How far are we?,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.823528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1ff81de2-8760-4f95-8a10-b9809f43a719 · outbound

This paper cites Attention-based lstm for aspect-level sentiment classification,.

EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code Attention-based lstm for aspect-level sentiment classification,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:02:51.808205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.