Paper Citation Record · LEDGER

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows

As of 10 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2502.00064.

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

pith.paper-citation-record.v1
2502.00064 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:42:57.704434Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T05:17:02.494233Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T09:28:39.219761Z

Reference resolution

26 of 26 outbound references displayed

verified exact1
verified fuzzy5
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0f8f26d6-7447-4147-9d09-dadfdc9fd693 · outbound

This paper cites AMAL: high- ﬁdelity, behavior-based automated malware analysis and cl assiﬁcation,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows AMAL: high- ﬁdelity, behavior-based automated malware analysis and cl assiﬁcation,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.544319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25333149-2c36-4d51-99fc-0460a0c1bf23 · outbound

This paper cites Analyzing and detecting emerging internet of things malware: A graph-based approac h,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Analyzing and detecting emerging internet of things malware: A graph-based approac h,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.550943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 478c2414-a08d-4f80-8cf6-8d564b2645ea · outbound

This paper cites Industry-speciﬁc vulnerability assessm ent,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Industry-speciﬁc vulnerability assessm ent,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.558296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9e37691-bb03-4f4c-8d79-a4ebb54c21ae · outbound

This paper cites Enriching vulnerabilit y reports through automated and augmented description summarization,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Enriching vulnerabilit y reports through automated and augmented description summarization,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.566632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc6e8fc-c165-4c41-a357-7b60d4ed5874 · outbound

This paper cites C leaning the nvd: Comprehensive quality assessment, improvements, and analyses,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows C leaning the nvd: Comprehensive quality assessment, improvements, and analyses,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:42:58.615572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 112afe50-d7b0-4e71-a81a-dfee1a0b11b7 · outbound

This paper cites DL-FHMC: deep learning-based ﬁne-grained hierarchical learning approa ch for robust malware classiﬁcation,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows DL-FHMC: deep learning-based ﬁne-grained hierarchical learning approa ch for robust malware classiﬁcation,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.581279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab206026-570b-4f83-b429-55cb40d2a65e · outbound

This paper cites Transformer-based language models for softw are vulnerability detection,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Transformer-based language models for softw are vulnerability detection,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.587424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59ff3d9f-7b10-4908-8533-61f25a722984 · outbound

This paper cites Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.593130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06a90055-ee09-48ac-b039-2cf6687ad00b · outbound

This paper cites Benchmarking Large Language Models for Log Analysis, Security, and Interpretation.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Benchmarking Large Language Models for Log Analysis, Security, and Interpretation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.599811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0b4db03-9465-434d-8158-44eedeeb9814 · outbound

This paper cites Exploring Context Window of Large Language Models via Decomposed Positional Vectors.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Exploring Context Window of Large Language Models via Decomposed Positional Vectors

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.606582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb0fae0e-9a2c-4f25-ad12-c39ef798ab6d · outbound

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

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows LLaMA: Open and Efficient Foundation Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.612655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81eff1c5-e473-493a-b29f-4cb1c6a601c3 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.618958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0edbb153-cdb5-486c-8bd0-da1560c7ee56 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Code Llama: Open Foundation Models for Code

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.625186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 282f0043-b722-45ea-bb70-c696954dae5b · outbound

This paper cites Introducing meta llama 3: The most capable ope nly available llm to date,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Introducing meta llama 3: The most capable ope nly available llm to date,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:42:58.595824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa87ee0a-24b8-496a-aeeb-c088c3af8861 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Gemma: Open Models Based on Gemini Research and Technology

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.638656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c6a284f-484d-4f9b-9443-45ece3322ddd · outbound

This paper cites Codegemma - an ofﬁcial google release for code llms,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Codegemma - an ofﬁcial google release for code llms,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:42:58.576156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09123ce8-6ff4-4503-a05d-51ba1478c7e5 · outbound

This paper cites Mistral 7B.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Mistral 7B

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.651118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 960982d5-8857-40f1-bc0f-471165898fa0 · outbound

This paper cites Mixtral of Experts.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Mixtral of Experts

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.657506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82154ab6-0f88-4267-a34f-371b95d48912 · outbound

This paper cites Textbooks Are All You Need.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Textbooks Are All You Need

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.663606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83ebfb8-31ac-4a9e-aacd-3b6ad7082ae1 · outbound

This paper cites Phi-2: The surprising power of small language models,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Phi-2: The surprising power of small language models,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:42:58.556211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c884861a-fa3f-4cfc-8c10-f1db6651bc84 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.675194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77f9bac2-08f8-41b0-b658-f9457311b6d3 · outbound

This paper cites Reimagining Self-Adaptation in the Age of Large Language Models.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Reimagining Self-Adaptation in the Age of Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-09T22:42:57.785515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 45ca0e14-b5ad-4321-ad80-ef67c177294b · outbound

This paper cites Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.691516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a7581d6-474f-484c-a345-8769cf2cab25 · outbound

This paper cites Vul4j : A dataset of reproducible java vulnerabilities geared towards the stud y of program repair techniques,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Vul4j : A dataset of reproducible java vulnerabilities geared towards the stud y of program repair techniques,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.698775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a2a884a-c438-4c2c-aae6-b6a9b63e1a59 · outbound

This paper cites A new algorithm for data compression,.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows A new algorithm for data compression,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T22:42:58.534072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ba3bfb1e-bb8c-4143-84a0-d4c1f932b320 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T22:42:57.680521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 3b9af115-2f76-41ac-8fa6-00b6f0eeb5ad · inbound

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development cites this paper.

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows

Reference 39

Resolution: malformed identifier; arxiv_id, observed 2026-05-10T09:28:39.221086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.