Paper Citation Record · LEDGER

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.10277.

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

pith.paper-citation-record.v1
2606.10277 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T13:56:30.837763Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

38 of 38 outbound references displayed

verified exact4
verified fuzzy0
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7aefc947-2162-4743-86c8-7236fe21df18 · outbound

This paper cites MIMO channel estimation using score-based generative models,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models MIMO channel estimation using score-based generative models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation ca0a040a-c2d4-4873-b7e3-1f53b77ab8c7 · outbound

This paper cites Channelformer: Attention based neural solution for wireless channel estimation and effective online training,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Channelformer: Attention based neural solution for wireless channel estimation and effective online training,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation ca489101-4cb9-4e7c-8ba0-8d741f0f9bbf · outbound

This paper cites PARAMOUNT: Toward generalizable deep learning for mmwave beam selection using sub-6 GHz channel measurements,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models PARAMOUNT: Toward generalizable deep learning for mmwave beam selection using sub-6 GHz channel measurements,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation f5d03f6c-20b3-42a7-9fdf-ed0457be321f · outbound

This paper cites Model- driven deep learning-based MIMO-OFDM detector: Design, simulation, and experimental results,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Model- driven deep learning-based MIMO-OFDM detector: Design, simulation, and experimental results,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation bc2a0b51-ded1-4ff7-a8bc-e2a7f73ba6cd · outbound

This paper cites Deep learning-based low complexity MIMO detection via partial MAP,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Deep learning-based low complexity MIMO detection via partial MAP,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation 41fe301c-f093-4f82-88e6-47c1ff5e7281 · outbound

This paper cites Large language models empowered autonomous edge AI for connected intelligence,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Large language models empowered autonomous edge AI for connected intelligence,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 5b9a9121-1aa2-4473-b453-6be6b1d4ab6b · outbound

This paper cites Large language models for wireless communications: From adaptation to autonomy,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Large language models for wireless communications: From adaptation to autonomy,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation b3b77b62-6d3f-422d-8672-cc1de69faf3d · outbound

This paper cites Big AI models for 6G wireless networks: Opportunities, challenges, and research directions,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Big AI models for 6G wireless networks: Opportunities, challenges, and research directions,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation c7e18ca2-901c-4bcf-9422-3273c32218dc · outbound

This paper cites ChannelGPT: A large model toward real-world channel foundation model for 6G environment intelligence communication,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models ChannelGPT: A large model toward real-world channel foundation model for 6G environment intelligence communication,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bd713d0-0192-4cba-b2a5-8d5d4b815b67 · outbound

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

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models LLM4CP: Adapting large language models for channel prediction,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation b7842a1b-c3d0-4385-9d5f-8db63c947b65 · outbound

This paper cites Foundation model empowered synesthesia of machines (SoM): AI-native intelligent multi- modal sensing-communication integration,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Foundation model empowered synesthesia of machines (SoM): AI-native intelligent multi- modal sensing-communication integration,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation a2bcc0d8-4658-40c7-b523-73b4b8e25de9 · outbound

This paper cites LLM4WM: Adapting LLM for wireless multi-tasking,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models LLM4WM: Adapting LLM for wireless multi-tasking,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation bec75fe7-ebff-48f0-b657-9cff2614a731 · outbound

This paper cites Large multimodal model-based environment-aware beam management,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Large multimodal model-based environment-aware beam management,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation f6244794-17f5-4e17-97be-63f4071f9964 · outbound

This paper cites WirelessGPT: A generative pre-trained multi-task learning framework for wireless communication,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models WirelessGPT: A generative pre-trained multi-task learning framework for wireless communication,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 6926a7b9-d5d5-49b2-989b-8604504563d3 · outbound

This paper cites LWM: A pre-trained wireless foundation model for universal feature extraction,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models LWM: A pre-trained wireless foundation model for universal feature extraction,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation 62385040-5619-4ab2-8469-9df7609bd1fa · outbound

This paper cites WiFo: Wireless foundation model for channel prediction,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models WiFo: Wireless foundation model for channel prediction,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation f7f31a91-a8e7-4c7a-bcbe-fdf30fa7c05e · outbound

This paper cites WiFo-2: a generalist foundation model unifies heterogeneous wireless system design.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models WiFo-2: a generalist foundation model unifies heterogeneous wireless system design

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:27:36.523199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe867e0d-7069-4c2e-907d-aa026345c365 · outbound

This paper cites A Wireless Foundation Model for Multi-Task Prediction.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models A Wireless Foundation Model for Multi-Task Prediction

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:27:36.517496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c9dda4a7-2f23-4f77-aaec-c095c73020d2 · outbound

This paper cites A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 56609085-0641-46f6-8109-2410e205ea07 · outbound

This paper cites WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM).

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:27:36.517451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fe5f17f-a2cd-4bcf-a190-f58bd388ea0d · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Parameter-efficient transfer learning for nlp,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d532f8f-e55a-4350-ba74-3b20cfa4a541 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models LoRA: Low-rank adaptation of large language models,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation 52dc6d7c-316d-49b3-80f0-216493f8d4ed · outbound

This paper cites Mitigating over- smoothing in transformers via regularized nonlocal functionals,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Mitigating over- smoothing in transformers via regularized nonlocal functionals,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation 44105cc5-d3f8-4852-ad6d-aef2c991f7fe · outbound

This paper cites Anti-oversmoothing in deep vision transformers via the Fourier domain analysis: From theory to practice,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Anti-oversmoothing in deep vision transformers via the Fourier domain analysis: From theory to practice,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 54e4a8c3-8acc-48c4-80c9-e62830275820 · outbound

This paper cites What does BERT learn about the structure of language?.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models What does BERT learn about the structure of language?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation aed977b0-53d9-45f5-87e1-a7c125f39afd · outbound

This paper cites A primer in BERTology: What we know about how BERT works,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models A primer in BERTology: What we know about how BERT works,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation e4719dbc-4785-4a1c-8640-8a1d3a2c060d · outbound

This paper cites BERT rediscovers the classical NLP pipeline,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models BERT rediscovers the classical NLP pipeline,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 4d4cdf86-935f-4f56-af09-e9a1f1e28036 · outbound

This paper cites Deep contextualized word representations,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Deep contextualized word representations,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

Source-reported events for the cited work

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Observation a780ad65-5f52-4156-a150-862f5b97c82a · outbound

This paper cites Exploiting deep representations for neural machine translation,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Exploiting deep representations for neural machine translation,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 2085a618-9364-410b-b6da-4975b6a7143c · outbound

This paper cites Understanding and improving encoder layer fusion in sequence-to- sequence learning,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Understanding and improving encoder layer fusion in sequence-to- sequence learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 149f8771-a017-468b-a0a1-154973869ad2 · outbound

This paper cites Vision transformer adapter for dense predictions,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Vision transformer adapter for dense predictions,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation b795e528-8b7b-42b0-a023-9c8482de64bb · outbound

This paper cites ViT-CoMer: Vision transformer with convolutional multi-scale feature interaction for dense predictions,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models ViT-CoMer: Vision transformer with convolutional multi-scale feature interaction for dense predictions,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation 35d2bd6c-0b3e-49c4-84ab-b509427f127e · outbound

This paper cites Depth-wise attention (DW Att): A layer fusion method for data-efficient classification,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Depth-wise attention (DW Att): A layer fusion method for data-efficient classification,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 21a08c25-1c26-4785-8479-d711241ef58c · outbound

This paper cites AdapterFu- sion: Non-destructive task composition for transfer learning,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models AdapterFu- sion: Non-destructive task composition for transfer learning,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Observation cd1b5666-6afe-47b3-919f-af4017f19380 · outbound

This paper cites Task-customized mixture of adapters for general image fusion,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Task-customized mixture of adapters for general image fusion,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 94d40d0b-361f-4322-936d-b0e3f531a719 · outbound

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

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:27:36.520351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18c4ea73-6fb8-441f-9f68-445d9cf5c1b1 · outbound

This paper cites Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Observation 09f7b900-310c-4237-8e4e-75e6b265d0fa · outbound

This paper cites AI/ML for NR Air Interface,.

A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models AI/ML for NR Air Interface,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T13:56:30.837763Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.