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Paper Citation Record · LEDGER

Revisiting Deep AC-OPF

As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2509.00655.

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

pith.paper-citation-record.v1
2509.00655 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:25:26.829995Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

31 of 31 outbound references displayed

  • verified exact8
  • verified fuzzy11
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ac325bdf-5c0f-44ba-8e8b-192f1c3d7569 · outbound

This paper cites A rewriting system for convex optimization problems.

Revisiting Deep AC-OPF A rewriting system for convex optimization problems

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.263805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.710249Z digest=sha256:9d23538201d7e61044748d8f99cb5afbfef1b022180151d79b2769e172afaeb1

Observation 301c7b30-e3bb-428d-b8b6-e5a47261c3a8 · outbound

This paper cites Under what conditions does e[f(x)] f(e[x]) ? Mathematics Stack Exchange, 2019.

Revisiting Deep AC-OPF Under what conditions does e[f(x)] f(e[x]) ? Mathematics Stack Exchange, 2019

Reference 2

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raw_fallback, observed 2026-08-05T13:25:28.079529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.714737Z digest=sha256:634641b01efdc531fab49991cf13dcfa261fd94d6878b7aaed13d2f85f5ab0b5

Observation feac078b-01c9-4bbd-90e1-80b6ed974c08 · outbound

This paper cites Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions.

Revisiting Deep AC-OPF Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions

Reference 3

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local_arxiv, observed 2026-08-05T13:25:27.996600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.718837Z digest=sha256:7eae2424b8379a5e83372e5edc130b8fbd1eea4ef68e94b0dcfb865e2c76ced7

Observation fa4040bf-64a1-46a2-b535-6c4462f41514 · outbound

This paper cites CVXPY : A P ython-embedded modeling language for convex optimization.

Revisiting Deep AC-OPF CVXPY : A P ython-embedded modeling language for convex optimization

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.222911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.723803Z digest=sha256:d5227da1140d68ccad82317ac945a83b49a10ec67278209e77a027f5b5eb991a

Observation 5f05bd07-e9e5-4ccc-b8b4-779dd9587f3d · outbound

This paper cites Neural networks for power flow: Graph neural solver.

Revisiting Deep AC-OPF Neural networks for power flow: Graph neural solver

Reference 5

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raw_fallback, observed 2026-08-05T13:25:28.198893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.727808Z digest=sha256:53db4c494a2f2a5bbc7cfd914fe5f9f7b6ccd553d00447942703533096b9291b

Observation 4fc608d3-7e5b-4e41-8eaf-944e1aa22151 · outbound

This paper cites Machine learning for sustainable energy systems.

Revisiting Deep AC-OPF Machine learning for sustainable energy systems

Reference 6

Resolution
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raw_fallback, observed 2026-08-05T13:25:28.184467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.731793Z digest=sha256:ba47351ccfae38a059fba60607f96b9d2f60bbe8f81c644e90ce49c9f481c66d

Observation 980e11e0-60f3-41ce-810e-9f6f411f0692 · outbound

This paper cites Enforcing robust control guarantees within neural network policies.

Revisiting Deep AC-OPF Enforcing robust control guarantees within neural network policies

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:25:27.978386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.736217Z digest=sha256:5f763f307389514ea1c93524503ce59ac953bf40146f0c0ba447c40aacc42f6b

Observation d81c80c6-bd42-4604-8021-3fc4ef9b5e5d · outbound

This paper cites Donti, David Rolnick, and J.

Revisiting Deep AC-OPF Donti, David Rolnick, and J

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.171845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.740325Z digest=sha256:073c7259f02b7e33ea3508ce89deada28c5738bc31f049b10b77250d751855ef

Observation 8acc4205-d3b4-44e3-936f-5b6ba1be7d1d · outbound

This paper cites Deep learning architectures for inference of AC-OPF solutions.

Revisiting Deep AC-OPF Deep learning architectures for inference of AC-OPF solutions

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:25:27.959643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.743925Z digest=sha256:8ecbe45a0f0c564feb4350ae6ced53054b730d994705849ce043f32aaa40d50f

Observation 7b72a1c2-65a8-4906-a603-76aa8c5132bb · outbound

This paper cites Leveraging power grid topology in machine learning assisted optimal power flow.

Revisiting Deep AC-OPF Leveraging power grid topology in machine learning assisted optimal power flow

Reference 10

Resolution
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raw_fallback, observed 2026-08-05T13:25:28.159454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.748039Z digest=sha256:dd8bf911ed01cd101c7529370b9213a45ae0b4649cb5db3f71f8509c43305e53

Observation e11faade-9ae2-42bb-bdb2-baeac996ef6f · outbound

This paper cites Power flow balancing with decentralized graph neural networks.

Revisiting Deep AC-OPF Power flow balancing with decentralized graph neural networks

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.752081Z digest=sha256:7e4f9a21876a8a00fd118e3d18d3ea984ccff2cfea1e765e01866f8d8762e67b

Observation 21438032-668f-485f-8176-c88859fbff95 · outbound

This paper cites Deepopf-v: Solving ac-opf problems efficiently.

Revisiting Deep AC-OPF Deepopf-v: Solving ac-opf problems efficiently

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.146509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.756048Z digest=sha256:cf6385c11f6461f3f83e15bc2f58746bff1e670cfde21f1eac73638854a8b9f8

Observation 7f23c781-b138-4c9f-a61c-b2625101f1a9 · outbound

This paper cites OPFLearnData: Dataset for Learning AC Optimal Power Flow.

Revisiting Deep AC-OPF OPFLearnData: Dataset for Learning AC Optimal Power Flow

Reference 13

Resolution
verified exact
doi, observed 2026-08-05T13:25:26.866093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.759785Z digest=sha256:501df6830ce1fedf4726cc9eb677b4fe154ff0b8e2227a6f0e71670967e3e6ea

Observation e28daa5c-2ab0-47b1-a4c1-bf5444cbafc6 · outbound

This paper cites an unresolved cited work.

Revisiting Deep AC-OPF Unresolved cited work

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T13:25:26.763513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.763513Z digest=sha256:ce1641ac605051f788dc8a26306898911671370ba01f4e0217dbe1098024afae

Observation 8d269469-533e-49ff-ab0b-9d80ed211c92 · outbound

This paper cites Numerical comparisons of linear power flow approximations: Optimality, feasibility, and computation time.

Revisiting Deep AC-OPF Numerical comparisons of linear power flow approximations: Optimality, feasibility, and computation time

Reference 15

Resolution
verified exact
raw_fallback, observed 2026-08-05T13:25:27.803014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.767238Z digest=sha256:c4961f3a0836a48d06c2c2bafe49906d9af537640cc8d3dd6a45337642ddb168

Observation a032ab1f-bd0b-4e67-a616-2e465babfe40 · outbound

This paper cites an unresolved cited work.

Revisiting Deep AC-OPF Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T13:25:26.770817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.770817Z digest=sha256:0d3979974d0ddde8195dc5c5508d5594f41dd7aa49d833a2c45facd0a4bb1226

Observation c4bae55e-e2ff-4b5a-9926-72d63283c0b1 · outbound

This paper cites DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks.

Revisiting Deep AC-OPF DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T13:25:26.774689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.774689Z digest=sha256:a4c96c1e981a89498319be21e58f39d47367f57eaf4b06e4ba889533387c9083

Observation 27c0333d-a370-47b4-aac6-7e69d62e0e05 · outbound

This paper cites Topology-aware graph neural networks for learning feasible and adaptive ac-opf solutions.

Revisiting Deep AC-OPF Topology-aware graph neural networks for learning feasible and adaptive ac-opf solutions

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.133688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.778750Z digest=sha256:917eae8d20604bcbcb3712f1f355201bfa4be9e5c330277f559c72adebce7efe

Observation 079b1612-178e-42af-9690-0a60aa9c6002 · outbound

This paper cites Optimal power flow using graph neural networks.

Revisiting Deep AC-OPF Optimal power flow using graph neural networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T13:25:26.782845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.782845Z digest=sha256:7896562790685c160024260ea025c66bd6c2c464f2ad901931b34614392adcdc

Observation 32975b81-5dfb-4c51-8714-73676e5b435a · outbound

This paper cites Unsupervised Optimal Power Flow Using Graph Neural Networks.

Revisiting Deep AC-OPF Unsupervised Optimal Power Flow Using Graph Neural Networks

Reference 20

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unresolved
no resolver link, observed 2026-08-05T13:25:26.786914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.786914Z digest=sha256:ae373b1871f0e2df9e3b531be761679b3c1571de2e072002542b35fc122b3fa9

Observation ccb2e160-acab-440f-b599-28398cd43aec · outbound

This paper cites Reduced optimal power flow using graph neural network.

Revisiting Deep AC-OPF Reduced optimal power flow using graph neural network

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.120795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.791082Z digest=sha256:7424775ea2bcb820305d17ee7e4e668b2e327e798f1d0ffb25994d55d4952dc7

Observation 0b455a42-602a-4b9b-b053-0a2f17beedc8 · outbound

This paper cites CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations.

Revisiting Deep AC-OPF CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations

Reference 22

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unresolved
no resolver link, observed 2026-08-05T13:25:26.794918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:25:26.794918Z digest=sha256:ba4a5bfa7b9ad38854004b72eca35764b92909665c89c91e850f76009e0296fc

Observation 982f7fcf-cd08-4c81-81b2-b81836b3be04 · outbound

This paper cites Learning an Optimally Reduced Formulation of OPF through Meta-optimization.

Revisiting Deep AC-OPF Learning an Optimally Reduced Formulation of OPF through Meta-optimization

Reference 23

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:25:27.478226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.799074Z digest=sha256:6fea6b55d86099983e5b8959a2f445de32c7e2690adfff2ccdc74fdff2818a0f

Observation 338e7b0e-5dac-4be4-b67d-8b57c1ed3a84 · outbound

This paper cites Linear power flow calculation methods for urban network.

Revisiting Deep AC-OPF Linear power flow calculation methods for urban network

Reference 24

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verified exact
raw_fallback, observed 2026-08-05T13:25:27.459244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.803090Z digest=sha256:dd1be0d6a4361d66b9d43ce397078370e94ff6cc598a0bca5b7ff36f93fe922e

Observation 2f4de27a-5300-4772-8900-b27b0a024a47 · outbound

This paper cites A state-independent linear power flow model with accurate estimation of voltage magnitude.

Revisiting Deep AC-OPF A state-independent linear power flow model with accurate estimation of voltage magnitude

Reference 25

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:25:27.366965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.806698Z digest=sha256:37780e361061164923c4a46c9345d4711a1cce04db0fa965a42b4c55d935da7d

Observation baaf79d2-9982-4d6c-99a4-678607f8edea · outbound

This paper cites A novel network model for optimal power flow with reactive power and network losses.

Revisiting Deep AC-OPF A novel network model for optimal power flow with reactive power and network losses

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.108116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.810523Z digest=sha256:710c966b35ef45fa70da88da2be8c24768e869367e3c59498143056c87a8c6e1

Observation a344c91c-2a89-4314-93da-e978c9219ae1 · outbound

This paper cites A linearized opf model with reactive power and voltage magnitude: A pathway to improve the mw-only dc opf.

Revisiting Deep AC-OPF A linearized opf model with reactive power and voltage magnitude: A pathway to improve the mw-only dc opf

Reference 27

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:25:27.278105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.814176Z digest=sha256:e8a9eeb35aac37ccf260daa7e87ce8cb27dcbfcf0392f660567d622b137df361

Observation e48c9cd3-5e2c-4423-a0b0-04d4f42984de · outbound

This paper cites A general formulation of linear power flow models: Basic theory and error analysis.

Revisiting Deep AC-OPF A general formulation of linear power flow models: Basic theory and error analysis

Reference 28

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:25:27.198587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.818234Z digest=sha256:7fc73995822ae027e665ad7ed9744846fb7e48f434227d92d14324baf8a760d6

Observation 33b05fb5-e5c1-405b-a6d8-d11ec6a7be2c · outbound

This paper cites Heydt, Vijay Vittal, and Jaime Quintero.

Revisiting Deep AC-OPF Heydt, Vijay Vittal, and Jaime Quintero

Reference 29

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metadata mismatch
raw_fallback, observed 2026-08-05T13:25:27.090204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.821937Z digest=sha256:6a6b5ce2f7ee1806b933fc66b3b823a89a3e72f34be55ebb8a0a2a1da20cafe1

Observation f178c72c-eff4-4bb5-8202-85a61da45dc7 · outbound

This paper cites an unresolved cited work.

Revisiting Deep AC-OPF Unresolved cited work

Reference 30

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metadata mismatch
raw_fallback, observed 2026-08-05T13:25:27.014117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.825922Z digest=sha256:0790bfed3b5879290ae967d3f935d6a7eee9ddcf2c7d7c45c48627b05e852a19

Observation fedc7550-e3fc-4eac-b991-b0c3ce95e4ff · outbound

This paper cites Matpower.

Revisiting Deep AC-OPF Matpower

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:25:28.094877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-05T13:25:26.829995Z digest=sha256:cee92cb962d94f5da4ee8188c2687b4c8edb53906ba61d8f6f0c41284128d5e3

Pith citing papers

No inbound Pith citation observations are available.