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Paper Citation Record · LEDGER

WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2405.17053.

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

pith.paper-citation-record.v1
2405.17053 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:19:26.997798Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:40:03.531958Z

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 7eb5c347-f304-4ecb-ac0e-2bbc9c2102c3 · inbound

NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research cites this paper.

NextG-GPT: Leveraging GenAI for Advancing Wireless Networks and Communication Research WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T14:19:26.997798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:19:26.997798Z digest=sha256:2d98d32549a79f6adcdc0ef1c898c3d11e73ac8147b6fbd987a72c367ac9a0ba

Observation 50b9b306-641d-4009-a608-bb835087de8d · inbound

ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing cites this paper.

ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T12:06:19.081431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:06:19.081431Z digest=sha256:4065efadbcd6b7d4924fca3348b586c8b501069f6e5c5bf7b6a503e966cbad48

Observation e8b2f937-2eee-4cc0-ab4a-5910214b82e2 · inbound

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration cites this paper.

Foundation Model Empowered Synesthesia of Machines (SoM): AI-native Intelligent Multi-Modal Sensing-Communication Integration WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T05:33:54.116045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:33:54.116045Z digest=sha256:c4d572860e5c19c109a890fe40d86130fdd8c41797d76a10f0c1608fcf6391d3

Observation 693b4d0c-c0a4-43d2-9e69-51c50fb5b3be · inbound

Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges cites this paper.

Large Language Models-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T04:23:15.513591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:23:15.513591Z digest=sha256:ae92d4ff2d256d35c7fe451f52ff49bb0079069e2653ca6efb01c23bcf3f94b4

Observation fb86e781-9772-4328-ae73-771bd5aac030 · inbound

A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication cites this paper.

A New Pathway to Integrated Learning and Communication (ILAC): Large AI Model and Hyperdimensional Computing for Communication WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-06T23:20:57.420487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:20:57.420487Z digest=sha256:65c78251a25ba4beaa8766ead4ba34802bdd3fa9a2c381299d5bbf86f59059a2

Observation b411e0d5-5adb-4944-9b0a-2db8b98c37f5 · 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 WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-05T04:50:31.528220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T04:50:31.528220Z digest=sha256:d0474f8a79be46f744614152b8bf91677562123c66db6807ab7fb1c31ff7920f

Observation c667a53c-e10c-48f5-ab28-41acb7f8d5b9 · inbound

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives cites this paper.

AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 106

Resolution
unresolved
no resolver link, observed 2026-08-04T19:34:31.018521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:34:31.018521Z digest=sha256:d9929e7fa64f57ebdefca9589bf960587457a8edae5e0d501fd36b18b39e19cc

Observation fcad4e6c-4ce9-4511-8d0f-3ee7763721ba · inbound

MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications cites this paper.

MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:10:22.975861Z

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=pdf_text observed=2026-05-17T22:06:30.391838Z digest=sha256:3ebdc8b6dfc3eeeeed89f806abe1f481b7815c7fd8fa430a0324fb3c6ae6f5b4

Observation 8e2ecb67-045b-4c29-b263-50b17bc91190 · inbound

When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling cites this paper.

When Adaptive Rewards Hurt: Causal Probing and the Switching-Stability Dilemma in LLM-Guided LEO Satellite Scheduling WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-13T13:05:01.450957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T13:05:01.450957Z digest=sha256:7cfecd4c09a2f9d70c784d442cbc566a365630f10c32c90c208786b865881e2a

Observation 69e5ecf9-20a7-4e8c-91e9-4f7a98f02d10 · inbound

When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks cites this paper.

When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-13T17:38:02.548916Z

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=pdf_text observed=2026-05-13T17:37:00.586612Z digest=sha256:65692d20027c1f51f5c5b77858b0c438b13743e03a8d77ff3c582b87173a7976

Observation 5885bd90-a310-4ca0-823d-8742bf25de4d · inbound

Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G cites this paper.

Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:30:54.555731Z

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=pdf_text observed=2026-05-10T18:28:45.511385Z digest=sha256:67eb24c811eb15b632a258c8bdc657d7b0ceae096f6842b35d4ef46bfd53a893

Observation f823225a-1f9b-4f82-bea6-9c5b4fb3b793 · inbound

Adversarial Water-Filling: Theory, Algorithms and Foundation Model cites this paper.

Adversarial Water-Filling: Theory, Algorithms and Foundation Model WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-06-29T23:44:03.272236Z

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=pdf_text observed=2026-06-29T23:34:08.173177Z digest=sha256:710c2e48421bb39a8e0eb7aa0670de72ddd01251fa2564909ef49e392d4c244d

Observation 837b0c29-51da-44cd-b080-e78f902700ab · inbound

Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges cites this paper.

Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 269

Resolution
verified exact
arxiv_id, observed 2026-07-04T18:40:03.549619Z

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=pdf_text observed=2026-06-25T22:34:33.698421Z digest=sha256:9c5a8b84af3588ce5c151885b289a17d060a395fabf72ebb5b06b9e32dc9ea9f

Observation 9b08aaf6-d942-4483-8e20-74193acdfc7e · inbound

Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization cites this paper.

Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-01T22:34:46.394854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T22:34:46.394854Z digest=sha256:3e38d1faae20080dcba344a0251dbc670e908303cc1978a2a2df913661c9dccb