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Paper Citation Record · LEDGER

Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.02167.

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

pith.paper-citation-record.v1
2503.02167 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:22:03.061417Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T05:16:40.014061Z

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 bd708fab-fc82-4ee0-ad00-a234525cff3d · inbound

From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins cites this paper.

From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

Reference 135

Resolution
unresolved
no resolver link, observed 2026-08-07T11:22:03.061417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:22:03.061417Z digest=sha256:2b9e82cd62ed468cc9ceede3dbc11526a5d0f886db19b220f3582fc978892eeb

Observation 1fa34a55-2b0e-4c39-b47f-66b55a3489b1 · inbound

Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation cites this paper.

Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T20:33:45.647082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:33:45.647082Z digest=sha256:7ce21146891a7d6a24e49e52544baae0a9bc5125d69b09103b91315b985ff641

Observation 10fc0961-3932-48eb-887d-318caa3b9063 · inbound

Enabling Cyber Security Education through Digital Twins and Generative AI cites this paper.

Enabling Cyber Security Education through Digital Twins and Generative AI Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T14:49:10.342734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:49:10.342734Z digest=sha256:f860d3a506fa39f5da47301b040d038fab7d1709a67f624561a87d58d9f57b05

Observation ae783f94-3add-45ed-af96-bfd9d7212bf7 · inbound

Multi-Agent Framework Leveraging Knowledge Graphs for Virtual Commissioning Models cites this paper.

Multi-Agent Framework Leveraging Knowledge Graphs for Virtual Commissioning Models Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-02T05:16:40.015669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T08:15:45.965989Z digest=sha256:90ae200b178d603968b1fc18593a1c3451c163014303a219552a165cd1e93be5