Paper Citation Record · LEDGER

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment

As of 4 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2604.21891.

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

pith.paper-citation-record.v1
2604.21891 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:51:02.261602Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

23 of 23 outbound references displayed

verified exact1
verified fuzzy21
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c50b26b-6bda-411b-9fcb-e7fbb69bbee2 · outbound

This paper cites an unresolved cited work.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-24T08:06:05.178134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6e15aabd-9952-45ce-b331-641d71627c85 · outbound

This paper cites Monitoring and optimization for power grids: A signal processing perspective.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Monitoring and optimization for power grids: A signal processing perspective

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.164548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e052c3ad-bdd8-48d5-8b46-6b4c27a9bb1e · outbound

This paper cites Unit commitment problem in electrical power system: A literature review.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Unit commitment problem in electrical power system: A literature review

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.172738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a1c2b05d-6333-48e1-8714-ddc6181e552b · outbound

This paper cites On the complexity of the unit commitment problem.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment On the complexity of the unit commitment problem

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.147673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f212ed83-b0f0-4b8b-b5fa-7cd80675b530 · outbound

This paper cites A neural combinatorial optimization algorithm for unit commitment in ac power systems.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment A neural combinatorial optimization algorithm for unit commitment in ac power systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.151912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 84df9898-a3ae-47b6-be5e-8513d6f91b3e · outbound

This paper cites Feasibility layer aided machine learning ap- proach for day-ahead operations.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Feasibility layer aided machine learning ap- proach for day-ahead operations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.158383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3966790a-429b-4bfb-88fc-4c91a9210203 · outbound

This paper cites The role of extended horizon methodology in renewable-dense grids with inter-day long-duration energy storage.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment The role of extended horizon methodology in renewable-dense grids with inter-day long-duration energy storage

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.175435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ca8fa924-f30b-496a-8962-5f6245a69284 · outbound

This paper cites Machine learning approaches to the unit commit- ment problem: Current trends, emerging challenges, and new strategies.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Machine learning approaches to the unit commit- ment problem: Current trends, emerging challenges, and new strategies

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.140837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 87776bb8-ad9a-4a61-ac57-bd9ad00451e5 · outbound

This paper cites Feasibility layer aided machine learning ap- proach for day-ahead operations.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Feasibility layer aided machine learning ap- proach for day-ahead operations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.167077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43bea38d-314c-443d-bf95-7b8e24b1f8d4 · outbound

This paper cites The use of artificial intelligence for the unit commitment problem: State of the art.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment The use of artificial intelligence for the unit commitment problem: State of the art

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.134289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ec03f2ba-2130-4617-817a-f2d7f1d0f90c · outbound

This paper cites A gan-based fully model-free learning method for short- term scheduling of large power system.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment A gan-based fully model-free learning method for short- term scheduling of large power system

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.137455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3b984cf3-2bbe-4a65-85bb-b7793a193c31 · outbound

This paper cites Graph convolutional network-based security-constrained unit commitment leveraging power grid topology in learning.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Graph convolutional network-based security-constrained unit commitment leveraging power grid topology in learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.125151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5c88ef8a-84ef-41f9-b833-74c1597b5c49 · outbound

This paper cites Data-driven decision-making for scuc: An improved deep learning approach based on sample coding and seq2seq technique.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Data-driven decision-making for scuc: An improved deep learning approach based on sample coding and seq2seq technique

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.128163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b033d285-8d59-4f11-a3a7-82296c02f282 · outbound

This paper cites Learning-assisted variables reduc- tion method for large-scale milp unit commitment.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Learning-assisted variables reduc- tion method for large-scale milp unit commitment

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.170018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4628fe81-7952-4602-8b12-b61dcb588bcd · outbound

This paper cites Reinforcement learning and mixed-integer program- ming for power plant scheduling in low carbon systems: Comparison and hybridisation.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Reinforcement learning and mixed-integer program- ming for power plant scheduling in low carbon systems: Comparison and hybridisation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.115765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bcc9ff68-7ad8-4615-a324-cc47f90fee1a · outbound

This paper cites Fsnet: Feasibility-seeking neural network for constrained optimization with guarantees.arXiv preprint arXiv:2506.00362.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Fsnet: Feasibility-seeking neural network for constrained optimization with guarantees.arXiv preprint arXiv:2506.00362

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:56:06.822259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a9bf1a58-2bc9-4e12-8385-7db2e73d57af · outbound

This paper cites Attention is all you need.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Attention is all you need

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.118844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5c4bbcde-ebeb-4c0a-b141-e6898bc16a2d · outbound

This paper cites Ai-ccelerating unit commitment.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Ai-ccelerating unit commitment

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.121667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f76b84bd-4b0b-40d6-b53c-eec6a948f249 · outbound

This paper cites On layer normalization in the transformer architecture.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment On layer normalization in the transformer architecture

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.131028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6799514a-8372-48fb-996f-38f7c1596a29 · outbound

This paper cites Zhou,Machine learning.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Zhou,Machine learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.144546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cfb8069b-d8e4-4b1b-8dc0-4047bc734755 · outbound

This paper cites Decoupled weight decay regularization.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Decoupled weight decay regularization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.155219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ba0b80aa-ad68-4d14-8273-b67eab750964 · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Super-convergence: Very fast training of neural networks using large learning rates

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.160969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 004a51ea-8167-4674-8b70-182696ddebce · outbound

This paper cites Spatio-temporal deep learning-assisted reduced security-constrained unit commitment.

A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment Spatio-temporal deep learning-assisted reduced security-constrained unit commitment

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T08:06:05.112422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

No inbound Pith citation observations are available.