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Paper Citation Record · LEDGER

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems

As of 3 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.04374.

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

pith.paper-citation-record.v1
2607.04374 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:39:07.667815Z

measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

23 of 23 outbound references displayed

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  • verified fuzzy0
  • unresolved19
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  • malformed identifier1
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External citation measurements

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Outbound references

Observation 7f19847d-1877-4ad0-ba11-54d250ab2992 · outbound

This paper cites Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Reference 1

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Observation 2a42a667-21cc-41f2-8ec2-06acd44192d9 · outbound

This paper cites Peeking Inside the Black -Box: A Survey on Explainable Artificial Intelligence (XAI).

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Peeking Inside the Black -Box: A Survey on Explainable Artificial Intelligence (XAI)

Reference 2

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Observation b765e6c8-e7ab-44f1-bc85-a3ba8d6cb1e1 · outbound

This paper cites and Aha, D.W.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems and Aha, D.W

Reference 3

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Observation 733018a6-f484-4b57-b70e-4b4ad2626179 · outbound

This paper cites an unresolved cited work.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Unresolved cited work

Reference 4

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Observation 19d39b5d-57f9-49e6-9099-2b3d040ce9f4 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 5

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Observation 48e395d9-31ea-4650-898b-74d6d20da61e · outbound

This paper cites Explaining Explanations: An Overview of Interpretability of Machine Learning,.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Explaining Explanations: An Overview of Interpretability of Machine Learning,

Reference 6

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Observation d3538433-e0ef-4c9d-99d0-c3f014f1f686 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Towards A Rigorous Science of Interpretable Machine Learning

Reference 7

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Observation ec294aed-21da-41fb-bfd4-1a938ceb2f72 · outbound

This paper cites Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed.; Leanpub: Victoria, BC, Canada, 2022.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed.; Leanpub: Victoria, BC, Canada, 2022

Reference 8

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Observation 05e31d62-a2f6-40db-90a3-41304dafed77 · outbound

This paper cites Why Should I Trust You?.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Why Should I Trust You?

Reference 9

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Observation e70f265a-0549-48fc-b11e-8d7d5fa7a86e · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems A Unified Approach to Interpreting Model Predictions

Reference 10

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Observation 5364e6f6-b0e8-47ea-97cf-339a0160b9df · outbound

This paper cites From Local Explanations to Global Understanding with Explainable AI for Trees.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems From Local Explanations to Global Understanding with Explainable AI for Trees

Reference 11

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Observation 6c6320e4-4dbf-4fe0-8caf-db4c992d984a · outbound

This paper cites Anchors: High -Precision Model-Agnostic Explanations.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Anchors: High -Precision Model-Agnostic Explanations

Reference 12

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Observation 4ed66baf-8e52-4410-943f-4106329af189 · outbound

This paper cites & sayres, R.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems & sayres, R

Reference 13

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Observation 3f28b3c9-c739-4ef3-8ccb-a878e028778a · outbound

This paper cites On the Robustness of Interpretability Methods.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems On the Robustness of Interpretability Methods

Reference 14

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Observation e06e576a-ec82-466d-bd4f-3d5c59282af4 · outbound

This paper cites On the (In)fidelity and Sensitivity of Explanations.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems On the (In)fidelity and Sensitivity of Explanations

Reference 15

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Observation 0dd038e7-963e-4016-87a5-75284c784817 · outbound

This paper cites Look at the Variance! Efficient Black-box Explanations with Sobol -based Sensitivity Analysis.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Look at the Variance! Efficient Black-box Explanations with Sobol -based Sensitivity Analysis

Reference 16

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Observation bb8c0f6b-8c46-404a-bda1-4b7f52060cef · outbound

This paper cites Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel

Reference 17

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Observation 7ea0080b-00a0-4b00-bb46-71661f674d9a · outbound

This paper cites & Zhou, D.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems & Zhou, D

Reference 18

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Observation c7e72930-2b7d-4bae-be98-2c829252ee05 · outbound

This paper cites Training language models to follow instructions with human feedback.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Training language models to follow instructions with human feedback

Reference 19

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Observation 865b80b4-d7cd-469d-bafc-3aabe699fdb5 · outbound

This paper cites Fault Detection and Classification in Power Systems Using Machine Learning Algorithms.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Fault Detection and Classification in Power Systems Using Machine Learning Algorithms

Reference 20

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An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Unresolved cited work

Reference 21

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Observation ce6d47da-194f-4de7-829e-5bdfb02f1f11 · outbound

This paper cites Explainable approaches for forecasting building electricity consumption.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems Explainable approaches for forecasting building electricity consumption

Reference 22

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Observation b13f1820-fb76-46f6-9399-f2fb81be72de · outbound

This paper cites K., Wu, M., Chen, J., & Zhang, L.

An End-to-End Explainable AI Framework with Automated LLM-Based Natural Language Explanation Generation for Energy Systems K., Wu, M., Chen, J., & Zhang, L

Reference 23

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arxiv_id, observed 2026-07-11T19:48:13.428449Z

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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.