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Paper Citation Record · LEDGER

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

As of 22 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 10 inbound Pith citation observations for arXiv:2502.06877.

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

pith.paper-citation-record.v1
2502.06877 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:56:47.031768Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:59:29.378563Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 6f52fa0e-72c7-4d2b-a589-98707a1f93ef · outbound

This paper cites Large Wireless Model (LWM): A Foundation Model for Wireless Channels.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Reference 1

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source=pdf_text observed=2026-08-08T18:56:46.984260Z digest=sha256:f9f9e5a59d81a6e0f44056917e8b70275ed9e1e27fbb1c68b6a6d129451a5e9d

Observation 643a9bc9-af7b-4a75-8160-01a06980d7fa · outbound

This paper cites LLM4CP: Adapting large language models for channel prediction,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication LLM4CP: Adapting large language models for channel prediction,

Reference 2

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raw_fallback, observed 2026-08-08T18:56:47.179059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:46.988653Z digest=sha256:d55c0a2e19797f14257c7eff6d3bfa95dc6ab41011fbbf8e0718fd80a19e8bf3

Observation 84d05f6b-0b5f-4a19-b95b-2e099146373b · outbound

This paper cites Radio foundation models: Pre-training Transformers for 5G-based indoor localization,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Radio foundation models: Pre-training Transformers for 5G-based indoor localization,

Reference 3

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raw_fallback, observed 2026-08-08T18:56:47.170251Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:46.991975Z digest=sha256:f84ed96f23fa30851c60096c2f114a48c5964c55b5fe08313b39019ee22e47b3

Observation 5f7ce621-c185-4e19-ad2f-12da7d400444 · outbound

This paper cites ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence

Reference 4

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source=pdf_text observed=2026-08-08T18:56:46.995081Z digest=sha256:48aedcf05d6babb2f25b7319518e97fec9ce8dbf16b833fafba722014c55bb24

Observation de49a0c5-238f-4b87-b747-4fd3379084eb · outbound

This paper cites Accurate channel prediction based on Transformer: Making mobility negligible,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Accurate channel prediction based on Transformer: Making mobility negligible,

Reference 5

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raw_fallback, observed 2026-08-08T18:56:47.161806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:46.998336Z digest=sha256:68c00742877f4e6faf7e763fa40a0fb6ab95d137a651ca974abe10426ce9871c

Observation 6f100d7d-b027-40fa-848f-eb3a20a8a537 · outbound

This paper cites CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels

Reference 6

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source=pdf_text observed=2026-08-08T18:56:47.001598Z digest=sha256:f729b64352e07168735896d175ec83127bfb733987bc5efb7e2ec980810e392d

Observation 8da95653-0ed1-43ec-a866-30ed4d0ae097 · outbound

This paper cites 6G-oriented CSI-based multi-modal pre- training and downstream task adaptation paradigm,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication 6G-oriented CSI-based multi-modal pre- training and downstream task adaptation paradigm,

Reference 7

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raw_fallback, observed 2026-08-08T18:56:47.153234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.004946Z digest=sha256:1aeed61cacf3828b890b6a0b3865e99bf02b89584c27f82f898716cfa06d1407

Observation 3e0898e7-682e-4906-b358-dd7bbb549e4b · outbound

This paper cites Integrating pre-trained language model with physical layer communications,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Integrating pre-trained language model with physical layer communications,

Reference 8

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raw_fallback, observed 2026-08-08T18:56:47.144547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.007767Z digest=sha256:6f8527e418504b1dbf76cfe5110b81061d71740f38ba70ac4954f0236ac4ae80

Observation 7a2a55d3-658d-453e-8b70-85cd189e3b4f · outbound

This paper cites Building 6G Radio Foundation Models with Transformer Architectures.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Building 6G Radio Foundation Models with Transformer Architectures

Reference 9

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source=pdf_text observed=2026-08-08T18:56:47.010616Z digest=sha256:928d16dedd008217b2e3b0a0dcac4e2e93856ec1165535549e1eb5953bfd5648

Observation f8506a30-4748-415a-99da-1f93f4f1dfc2 · outbound

This paper cites Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction

Reference 10

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source=pdf_text observed=2026-08-08T18:56:47.013689Z digest=sha256:b4b38ece67c1c6f6e3cbc0532df60ea2d2298a06c548842e5d65d6544d1f5e12

Observation ca8bd4cb-dc5b-4fe4-b7d7-1421a2909382 · outbound

This paper cites Transformer masked autoencoders for next-generation wireless communications: Ar- chitecture and opportunities,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Transformer masked autoencoders for next-generation wireless communications: Ar- chitecture and opportunities,

Reference 11

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raw_fallback, observed 2026-08-08T18:56:47.135708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.016898Z digest=sha256:5a5ce870ce38b7bdd4cb951332a5de928fa85c95a72be5b763f52deb67998117

Observation 12ff2bfe-ae07-46a3-b65f-d8d6a93293ab · outbound

This paper cites Sionna: An open-source library for next-generation physical layer research,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Sionna: An open-source library for next-generation physical layer research,

Reference 12

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source=pdf_text observed=2026-08-08T18:56:47.019712Z digest=sha256:427a9e369fa1817e8042e0c316cf2a99fd9cce756e6bc2773c8e8e1d578d188a

Observation b92c60f7-fd24-4aba-a9ad-3eba9f744f6e · outbound

This paper cites DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

Reference 13

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source=pdf_text observed=2026-08-08T18:56:47.022463Z digest=sha256:cbc2841a0a2fc447dbceb04421ab76699ccb5cba4678399815b522c95491d817

Observation 7e5d93ed-bbda-424e-8026-faa5cc8103a3 · outbound

This paper cites Kyösti, J.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Kyösti, J

Reference 14

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raw_fallback, observed 2026-08-08T18:56:47.122650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.026078Z digest=sha256:5676b4d4ba9a97910733009d0b15cbb7ee39543ca1830a6108c6fb572b093a03

Observation c1c07639-0121-494a-8efc-2866c7831d8b · outbound

This paper cites Study on channel model for frequencies from 0.5 to 100 ghz (release 15),.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Study on channel model for frequencies from 0.5 to 100 ghz (release 15),

Reference 15

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raw_fallback, observed 2026-08-08T18:56:47.113641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.028927Z digest=sha256:06b445f69c4eaf51c7867c15bfc871f0d2762dc4ee14ab853d6856446113009b

Observation 3e683a0c-5c08-486c-b1e7-6fb255ce7a64 · outbound

This paper cites EfficientFi: Toward large-scale lightweight wifi sensing via CSI compression,.

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication EfficientFi: Toward large-scale lightweight wifi sensing via CSI compression,

Reference 16

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T18:56:47.031768Z digest=sha256:97bbad749cdf910d95d2e28b7669f8e1e2776ea90427b3ad7281638a9858fcde

Pith citing papers

Observation 1a323e50-7f23-478b-ae44-971c5fe9b568 · inbound

6G WavesFM: A Foundation Model for Sensing, Communication, and Localization cites this paper.

6G WavesFM: A Foundation Model for Sensing, Communication, and Localization WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 17

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source=pdf_text observed=2026-08-16T11:59:29.378563Z digest=sha256:37a9d148dce4fe8576af6a6d371889412ac1562861224823aea6ad8b2ead7c72

Observation 632e5d7f-6606-49e8-acd5-714a606f08e6 · inbound

A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges cites this paper.

A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 75

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source=pdf_text observed=2026-08-15T23:50:24.351227Z digest=sha256:86e24e7d54548065607f655389e523248f548c9a20243d872c1b61c0f5e09e67

Observation c1e86089-f336-46eb-a2f4-b2f70ccaf8f8 · inbound

A Short Overview of Multi-Modal Wi-Fi Sensing cites this paper.

A Short Overview of Multi-Modal Wi-Fi Sensing WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 60

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source=pdf_text observed=2026-08-15T22:39:44.021336Z digest=sha256:bcbfa1b45391f476d571fa0e1f16f82d91b4471c9941879854cb8f4bbf629476

Observation 467d655b-f10f-41fb-ac53-cd838440b444 · inbound

Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks cites this paper.

Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 30

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source=pdf_text observed=2026-08-15T21:20:15.110200Z digest=sha256:3b90ab0baa3cb1f268cc6982a7889a554895d931dd15f75b4977a9448623b7e0

Observation b624c8f1-f9e1-47e3-ad4a-4c06892ae5d8 · inbound

IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G cites this paper.

IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 16

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source=pdf_text observed=2026-08-07T05:56:03.108601Z digest=sha256:61fc458b30934910963d287bcbaf220f040465fbc74d440b349795c692f3daba

Observation 075b1e7b-eb9c-426d-a888-a4f228ff005f · inbound

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities cites this paper.

Towards channel foundation models (CFMs): Motivations, methodologies and opportunities WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 56

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source=pdf_text observed=2026-08-06T16:24:46.717191Z digest=sha256:17a534d888700c6196f60ce033d8c1269e4035d1fac839eb825abaf0b7e407b9

Observation 0fd471b5-94e9-4ed8-a492-90d288e3761f · inbound

Robust Model Reconstruction Based on the Topological Understanding of Point Clouds Using Persistent Homology cites this paper.

Robust Model Reconstruction Based on the Topological Understanding of Point Clouds Using Persistent Homology WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 4

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local_arxiv, observed 2026-08-06T10:19:14.959476Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T10:19:11.968622Z digest=sha256:f55ed2de4ff970ce5214ed9ba587256a7cf5681b16cc81dfd58cb4cbbb6c4cc4

Observation 092f1678-0221-4b8d-aea3-a70abaa950cc · inbound

LLM-Enhanced Space-Air-Ground-Sea Integrated Networks cites this paper.

LLM-Enhanced Space-Air-Ground-Sea Integrated Networks WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 5

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source=pdf_text observed=2026-08-15T16:40:57.569872Z digest=sha256:1ba732b0c64222ff386642c9e946ad5f6a8418ee27cd06b7ef8e7703c912f77a

Observation 89d1d864-5436-4479-bb74-b55d4496fd01 · inbound

Modular PE-Structured Learning for Cross-Task Wireless Communications cites this paper.

Modular PE-Structured Learning for Cross-Task Wireless Communications WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 4

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source=pdf_text observed=2026-08-04T20:27:46.523883Z digest=sha256:45cc7b92e7ea2fee714385ed7c46e075c35ef46a747ebf0204a7ff06f3e78a99

Observation 003a417f-ad6f-47d2-ae06-1d26ecdd67e3 · inbound

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications cites this paper.

Radio-FM: A Foundation Model for Radio Signal Representation Learning and Its Applications WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

Reference 10

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source=pdf_text observed=2026-08-07T23:29:03.896033Z digest=sha256:c5c762ad489e4b5d0913596db99befcb1655c5147ffd04a8d84e363a224331e7