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Paper Citation Record · LEDGER

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization

As of 5 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.15809.

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

pith.paper-citation-record.v1
2607.15809 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:20:56.420830Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

35 of 35 outbound references displayed

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  • unresolved35
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External citation measurements

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Outbound references

Observation 236ab453-ccac-4f27-b62c-718e2bd6fb15 · outbound

This paper cites Survey on ai and machine learning techniques for microgrid energy management systems,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Survey on ai and machine learning techniques for microgrid energy management systems,

Reference 1

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Observation 7e838423-c143-44f5-ae73-3e1c7f39f03b · outbound

This paper cites Machine learning accelerated real-time model predictive control for power systems,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Machine learning accelerated real-time model predictive control for power systems,

Reference 2

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Observation c4f266d9-d275-419d-8e4e-4aa5ce52aab5 · outbound

This paper cites Contingency filtering techniques for preventive security-constrained optimal power flow,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Contingency filtering techniques for preventive security-constrained optimal power flow,

Reference 3

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Observation f0832390-9f77-4323-b461-b03b0ae538f9 · outbound

This paper cites Decomposed scopf for improving efficiency,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Decomposed scopf for improving efficiency,

Reference 4

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Observation a06d6e97-1f7a-4e23-8b87-435ac4af4795 · outbound

This paper cites Fast security-constrained optimal power flow through low-impact and redundancy screening,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Fast security-constrained optimal power flow through low-impact and redundancy screening,

Reference 5

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Observation 45b9bd41-29de-4241-a18e-24df55174882 · outbound

This paper cites Security constrained unit commitment using line outage distribution factors,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Security constrained unit commitment using line outage distribution factors,

Reference 6

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Observation 8dedc151-719a-4f42-b06c-a6eab0c8dc08 · outbound

This paper cites Deepopf: A deep neural network approach for security-constrained dc optimal power flow,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Deepopf: A deep neural network approach for security-constrained dc optimal power flow,

Reference 7

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Observation a5f797c0-bfca-40b9-b1ff-d64a19a15c6a · outbound

This paper cites Data-driven optimal power flow: A physics-informed machine learning approach,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Data-driven optimal power flow: A physics-informed machine learning approach,

Reference 8

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Observation ea475db4-2285-4454-93f6-e6441445bcfb · outbound

This paper cites Data-driven screen- ing of network constraints for unit commitment,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Data-driven screen- ing of network constraints for unit commitment,

Reference 9

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Observation 373a9eff-822d-4790-812b-29e72b9c7174 · outbound

This paper cites Applications of physics-informed neural net- works in power systems-a review,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Applications of physics-informed neural net- works in power systems-a review,

Reference 10

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Observation 029e34de-4563-42c2-91d3-8f43f66800f7 · outbound

This paper cites Two- timescale voltage control in distribution grids using deep reinforcement learning,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Two- timescale voltage control in distribution grids using deep reinforcement learning,

Reference 11

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Observation 91b5e388-a94f-47c3-aaa3-415d73c1ea71 · outbound

This paper cites Deep reinforcement learning based volt-var optimization in smart distribution systems,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Deep reinforcement learning based volt-var optimization in smart distribution systems,

Reference 12

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Observation 863a8cbd-27c2-431f-a326-315a69b7bede · outbound

This paper cites Multi-agent deep reinforcement learning for voltage control with coordinated active and reactive power optimization,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Multi-agent deep reinforcement learning for voltage control with coordinated active and reactive power optimization,

Reference 13

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Observation 973d310a-e31d-42cc-93a5-8394bf35017c · outbound

This paper cites Robust deep reinforcement learning for volt-var optimization in active distribution system under uncertainty,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Robust deep reinforcement learning for volt-var optimization in active distribution system under uncertainty,

Reference 14

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Observation 05d01c44-cc71-4f58-b5b8-e9e4412e30c5 · outbound

This paper cites Varying condition scopf based on deep learning and knowledge graph,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Varying condition scopf based on deep learning and knowledge graph,

Reference 15

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Observation eb560d9e-dca5-43a4-afca-2f029336a4dd · outbound

This paper cites A data-knowledge-hybrid-driven method for modeling reactive power- voltage response characteristics of renewable energy sources,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization A data-knowledge-hybrid-driven method for modeling reactive power- voltage response characteristics of renewable energy sources,

Reference 16

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Observation 5988d892-93a1-483c-8b28-f9fefe34ae33 · outbound

This paper cites A meta-learning based distribution system load forecasting model selection framework,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization A meta-learning based distribution system load forecasting model selection framework,

Reference 17

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Observation c7d055e3-6cc4-4807-97d8-60496ad99618 · outbound

This paper cites Meta-ann–a dynamic artificial neural network refined by meta-learning for short-term load forecasting,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Meta-ann–a dynamic artificial neural network refined by meta-learning for short-term load forecasting,

Reference 18

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Observation 47a718e2-366d-40d2-97ad-acdb0bda489b · outbound

This paper cites Short-term load forecasting of distribution transformer supply zones based on federated model-agnostic meta learning,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Short-term load forecasting of distribution transformer supply zones based on federated model-agnostic meta learning,

Reference 19

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Observation 24486c8b-8304-4cd5-855e-5e4ef828ed02 · outbound

This paper cites Load recognition with few-shot transfer learning based on meta-learning and relational network in non- intrusive load monitoring,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Load recognition with few-shot transfer learning based on meta-learning and relational network in non- intrusive load monitoring,

Reference 20

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Observation 3f539768-f6c8-448d-babd-47d184a7be6a · outbound

This paper cites A generalizable method for practical non-intrusive load monitoring via metric-based meta-learning,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization A generalizable method for practical non-intrusive load monitoring via metric-based meta-learning,

Reference 21

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Observation ba5ec06e-0b75-4287-8818-981edceba549 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Retrieval- augmented generation for knowledge-intensive nlp tasks,

Reference 22

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Observation bc648049-d1c8-4bd3-b0ea-359a4d340d88 · outbound

This paper cites Remodiffuse: Retrieval-augmented motion diffusion model,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Remodiffuse: Retrieval-augmented motion diffusion model,

Reference 23

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Observation 809d2a14-8c37-4195-a245-7dd15e7c0734 · outbound

This paper cites Re-imagen: Retrieval- augmented text-to-image generator,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Re-imagen: Retrieval- augmented text-to-image generator,

Reference 24

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Observation 515d77e1-dea0-409f-9011-d06750c313e1 · outbound

This paper cites An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization

Reference 25

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Observation e8fb164f-4b86-4625-9f4f-21a075973c22 · outbound

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From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Denoising diffusion probabilistic models,

Reference 26

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Observation b3af8084-9bcf-41ce-8ed9-d70c8319ea69 · outbound

This paper cites Diffusion-based generation, optimization, and planning in 3d scenes,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Diffusion-based generation, optimization, and planning in 3d scenes,

Reference 27

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Observation af6f679d-ec74-4f51-8dca-99324b7db77f · outbound

This paper cites A large-scale dataset of distributed renewable energy scenarios on the ieee-33 bus network,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization A large-scale dataset of distributed renewable energy scenarios on the ieee-33 bus network,

Reference 28

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Observation 71f11d01-b93d-4442-9bdf-78c58ac644e9 · outbound

This paper cites An open tool for creating battery-electric vehicle time series from empirical data, emobpy,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization An open tool for creating battery-electric vehicle time series from empirical data, emobpy,

Reference 29

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Observation 6035421b-c2e8-42a6-b327-7efba949fed6 · outbound

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From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model

Reference 30

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Observation dd980b60-09e0-4a41-87af-bf44dd1de663 · outbound

This paper cites Egbad: Ensemble graph- boosted anomaly detection for user-level multi-energy load data,.

From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Egbad: Ensemble graph- boosted anomaly detection for user-level multi-energy load data,

Reference 31

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From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Unresolved cited work

Reference 32

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From Similarity to Feasibility: Diffusion-Refined Retrieval-Augmented Generation for Distribution Network Optimization Unresolved cited work

Reference 33

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Reference 34

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Reference 35

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