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Paper Citation Record · LEDGER

Large Foundation Models for Power Systems

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.07044.

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

pith.paper-citation-record.v1
2312.07044 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:32:57.809879Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T04:50:34.214473Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c921dc13-5736-45a1-887c-3f242277568b · inbound

RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks cites this paper.

RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks Large Foundation Models for Power Systems

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T04:32:57.809879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:32:57.809879Z digest=sha256:84a22bbe4e14abea32d1e8c182947909b5e43158d3c23262911b91ace7428fff

Observation f846178f-dd54-43f5-b4a8-9581ef78a304 · inbound

Large Language Model Interface for Home Energy Management Systems cites this paper.

Large Language Model Interface for Home Energy Management Systems Large Foundation Models for Power Systems

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T20:33:45.482836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:33:45.482836Z digest=sha256:e473acc4814d7fc0cd0d16085f13f4f1f108cb59b1835e28ebac7b858d1c5d58

Observation e8175168-6429-4d10-be1c-6b1054a16073 · inbound

LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration cites this paper.

LLM4DistReconfig: A Fine-tuned Large Language Model for Power Distribution Network Reconfiguration Large Foundation Models for Power Systems

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-10T14:49:59.334031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:49:59.334031Z digest=sha256:06c666920b896461665238ceef2a3d131ef38f7da549fa5d0ce7b289eeb3469b

Observation ab0daa1a-5bc3-47ea-a11e-701b3367bbcf · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Large Foundation Models for Power Systems

Reference 109

Resolution
verified exact
local_arxiv, observed 2026-08-05T04:50:34.219219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T04:50:31.888993Z digest=sha256:0170c5ed37fbd3072542bd74ea9a3575a042dfdf25511c6b67bf551a2eb2b910

Observation 5ddf8562-4888-40e4-bb75-3549689ebf87 · inbound

Agentic Artificial Intelligence for Power Systems: Strategies to Identify and Close Capability Gaps cites this paper.

Agentic Artificial Intelligence for Power Systems: Strategies to Identify and Close Capability Gaps Large Foundation Models for Power Systems

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T00:21:39.169720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:21:39.169720Z digest=sha256:93158440cf4e8c1e6b86e0f95268cf780daffe91820034121945c8bf78922031