Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T00:34:16.882059Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.26201.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T00:34:16.882059Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
35 of 35 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 36076810-204d-411a-b297-3a19ea9e2295 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Scientific Reports15(1), 1726 (2025)
Reference 1
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Observation 70114883-cfd8-4228-9a17-6139c0b94cb6 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations frontiers in computer science, 6 (2024)
Reference 2
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Observation bdd57197-11b0-4211-9200-eca1a59b46c4 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)
Reference 3
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Observation 41b0b726-31bc-4ed4-b46d-657d16900a39 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Information fusion58, 82–115 (2020)
Reference 4
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Observation 3c4730da-1b34-4f90-bee9-b750bb43d336 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations AnomalyExplainer Explainable AI for LLM-based anomaly detection using BERTViz and Captum
Reference 5
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Observation 432be145-f11d-478c-8374-f3a5cb81cbb3 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Thinking Machines: A Survey of LLM based Reasoning Strategies
Reference 6
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Observation c65506d2-325d-40fd-9cff-48d4a61d83f7 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations In: 2024 IEEE 10th World Forum on Internet of Things (WF-IoT)
Reference 7
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Observation c1e2dcad-5f28-49e5-bc35-aff205eabdb9 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Journal of Cybersecurity and Privacy4(4), 777– 793 (2024)
Reference 8
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Observation 8c66491f-1d4a-49b4-8fd2-c5e09696c347 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs
Reference 9
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Observation 57cdcdd2-95b5-47de-aa1a-816b2859b8cc · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations NIST Special Publication800(61), 1–147 (2012)
Reference 10
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Observation 73c833e5-8f27-4535-a4ad-a93089a40c0e · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey
Reference 11
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Observation b02a031f-8530-4294-886e-2dcbb7c5f6c0 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Computers and Electrical Engineering129, 110826 (2026)
Reference 12
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Observation 47d0e22f-37f3-44be-b122-e780c0638e76 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Towards Explainable Network Intrusion Detection using Large Language Models
Reference 13
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Observation ca1e6318-4e29-4c40-9257-c84e2783a3ad · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Unresolved cited work
Reference 14
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Observation 7ea067b1-c198-412a-a77d-ef38e60b9ca3 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Unresolved cited work
Reference 15
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Observation c4984bb6-36df-4503-8ef3-34e1f2deb448 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Computers & Security95, 101851 (2020)
Reference 16
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Observation 5f6df651-9dc5-4998-b1e9-4f5cb2166c14 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability
Reference 17
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Observation 19b109a3-1516-4ec5-8ab7-b38c731ab29a · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Advances in neural information processing systems30(2017)
Reference 18
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Observation f7c81fd1-865f-4f04-8a07-ca4507920a48 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations arXiv preprint arXiv:2508.21323 (2025)
Reference 19
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Observation 0af3e86d-f969-450a-9ed7-15ed257afdb4 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations In: 2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)
Reference 20
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Observation e3c31517-1c46-48ac-8d39-4e8ef141f721 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Journal of Cybersecurity and Privacy5(3), 55 (2025)
Reference 21
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Observation 35d2ea85-babf-4954-8c52-4e1cac8a4eb9 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Unresolved cited work
Reference 22
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Observation e0df9f39-98fe-46fd-8aeb-701ff9517751 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Unresolved cited work
Reference 23
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Observation ef644fc4-0caa-436d-bf0a-5ed57b3d65ae · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Cognitive Systems Research86, 101243 (2024)
Reference 24
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Observation e62fab2a-0dec-4ad6-b759-6f584a4580e2 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations why should i trust you?
Reference 25
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Observation b17b3704-1b43-4759-9604-f80c18c9d099 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations ICT express10(4), 935–958 (2024)
Reference 26
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Observation ac5e943a-8d01-465a-a615-d8c0b64b24cd · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations ICISSp1(2018), 108–116 (2018)
Reference 27
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Observation db6333e2-cadf-44c1-aa2e-616cd1af0d2c · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Journal of Cybersecurity and Privacy5(4), 95 (2025)
Reference 28
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Observation 6e4d9b70-66df-4b1b-829f-72c2ff77d2d8 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations ACM Computing Surveys 57(9), 1–38 (2025)
Reference 29
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Observation 43d544cc-b658-4903-84ee-08a7355cf652 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Gemini: A Family of Highly Capable Multimodal Models
Reference 30
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Observation b5d5548f-d2ff-4a2a-9a06-8011943e9e7e · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Ieee Access8, 227756–227779 (2020)
Reference 31
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Observation 131d4296-ba76-4c0a-a6d5-7a0c697e5d25 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations In: 2019 IEEE International Conference on Electro Informa- tion Technology (EIT)
Reference 32
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Observation ad6d91a4-4de0-42a8-b08d-3e69abec742d · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations In: 2024 12th International Symposium on Digital Forensics and Security (ISDFS)
Reference 33
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Observation 81b3795a-4633-468c-abf4-5afb831d0834 · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations Unresolved cited work
Reference 34
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Observation fee6fc73-1bab-4191-ba13-ab7435ecb35f · outbound
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations 4 DLdeep learning
Reference 35
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No inbound Pith citation observations are available.