Paper Citation Record · LEDGER

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis

As of 7 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.12100.

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

pith.paper-citation-record.v1
2506.12100 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:11.444452Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy17
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c1aac59d-b64f-46c6-8a34-118c9eb41e70 · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:17.432370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e37ef6a-b929-4fcf-9227-c5e1867d75ba · outbound

This paper cites Improving Language Models by Retrieving from Trillions of Tokens.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Improving Language Models by Retrieving from Trillions of Tokens

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:17.094506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dbc61c80-be9b-4462-a8f2-f3e0fdd7bdff · outbound

This paper cites Evaluation of ChatGPT Model for Vulnerability Detection.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Evaluation of ChatGPT Model for Vulnerability Detection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:09.055205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47a047df-903d-45ac-9a67-669c915781a9 · outbound

This paper cites ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:19:11.910825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c464d64d-2833-439e-a4b8-5877d8644a76 · outbound

This paper cites PentestGPT: Evaluating and Harnessing Large Language Models for Automated Pen- etration Testing.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis PentestGPT: Evaluating and Harnessing Large Language Models for Automated Pen- etration Testing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.638521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 175b029d-4952-4885-8a71-3c57a781ca31 · outbound

This paper cites A Sliding Layer Merging Method for Effi- cient Depth-Wise Pruning in LLMs.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis A Sliding Layer Merging Method for Effi- cient Depth-Wise Pruning in LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:09.397480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4bb763a-a0a8-4c26-b822-015755f801cb · outbound

This paper cites Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:09.495629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb038151-ef34-4102-845c-64006b06d0a2 · outbound

This paper cites Introducing Gemma 3: The Most Ca- pable Model You Can Run on a Single GPU or TPU.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Introducing Gemma 3: The Most Ca- pable Model You Can Run on a Single GPU or TPU

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.416978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c329cf78-7070-4ca4-bea6-13c4ccae29c9 · outbound

This paper cites From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.155391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b374c62c-7837-4db5-a386-86b1d6dd5446 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis LoRA: Low-Rank Adaptation of Large Language Models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.880215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c32f4d49-b9b9-4596-8f69-65fc55324fb1 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:09.845343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88cf17e3-b3a6-4840-b848-643a999185dd · outbound

This paper cites DeepSeek-R1-Distill-Llama-8B.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis DeepSeek-R1-Distill-Llama-8B

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.589915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e8f508e-ad6b-4e8b-bb59-8f2553931c61 · outbound

This paper cites Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:10.037010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d85352a8-7bf2-4f5b-bec6-c701cb2fb5be · outbound

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

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:10.099050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 360285c3-ccb4-444f-a4dd-27ad0c4403ea · outbound

This paper cites SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:10.177621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a16f72d8-0c51-466c-9e3e-cbfdcd3f1b96 · outbound

This paper cites LLM- Pruner: On the Structural Pruning of Large Language Models.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis LLM- Pruner: On the Structural Pruning of Large Language Models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.319963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9035c8ac-ddf2-46a2-9cdd-36968beb8ca6 · outbound

This paper cites Llama 3.2: Revolutionizing edge ai and vi- sion with open, customizable models.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Llama 3.2: Revolutionizing edge ai and vi- sion with open, customizable models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.009956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5692323b-bbc4-4caa-b3a1-02d5f6dc0f04 · outbound

This paper cites Mistral Small 3.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Mistral Small 3

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.683230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bf44201d-a2b2-453b-81c4-83c2dd60ae5d · outbound

This paper cites LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:10.531760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a728d09-ede6-442d-905d-be4fe5940a81 · outbound

This paper cites CVE - Common Vulnerabilities and Expo- sures.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis CVE - Common Vulnerabilities and Expo- sures

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.372881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 41b53623-c34f-4374-873d-bc59c7b94be0 · outbound

This paper cites National Vulnerability Database (NVD).

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis National Vulnerability Database (NVD)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.070591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d77ef160-c97a-4d38-8ff6-77b45800b9bd · outbound

This paper cites Empirical Validation of Automated Vulnerability Curation and Characterization.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Empirical Validation of Automated Vulnerability Curation and Characterization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:13.692872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e83884f-962a-4081-87c0-cb4987ea9294 · outbound

This paper cites AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Gener- ation.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Gener- ation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:13.225834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8f980ab-0f0c-4aab-aedb-114ebc784011 · outbound

This paper cites The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:11.006093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 399edb2e-e258-4a16-90f8-fe608bdaf866 · outbound

This paper cites What is a CVE? https://www.redhat.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis What is a CVE? https://www.redhat

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:12.915559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce05af3b-5b0c-4e72-92f7-d9440f4b8b52 · outbound

This paper cites Attention Is All You Need.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Attention Is All You Need

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:12.615822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa617041-db12-48c2-bc37-b3a04c8b0aea · outbound

This paper cites Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review.

LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Model's Response for Vulnerability Analysis Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:12.311818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.