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Paper Citation Record · LEDGER

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2601.09179.

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

pith.paper-citation-record.v1
2601.09179 v3

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:44:50.262014Z

measured 41 of 41 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T00:11:57.999438Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T14:47:03.892054Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved38
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 49f695d5-b971-4f8b-b928-45de3e154b96 · outbound

This paper cites Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Toward Edge General Intelligence With Agentic AI and Agentification: Concepts, Technologies, and Future Directions,

Reference 1

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source=pdf_text observed=2026-08-03T10:44:46.977192Z digest=sha256:c4bbefeec986b24a6a1925c14fdc1695880d543bbea8f199e61c5c6294c3a63f

Observation 7133796c-d268-445c-8518-394019a91552 · outbound

This paper cites Embodied AI-Enhanced Vehicular Networks: An Inte- grated Vision Language Models and Reinforcement Learning Method,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Embodied AI-Enhanced Vehicular Networks: An Inte- grated Vision Language Models and Reinforcement Learning Method,

Reference 2

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Observation faf44c1a-1a17-4898-ab72-a95dcca8163c · outbound

This paper cites Embodied Intelligent Wireless (EIW): Synesthesia of Machines Empowered Wireless Communications,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Embodied Intelligent Wireless (EIW): Synesthesia of Machines Empowered Wireless Communications,

Reference 3

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Observation d7b9c12e-fd83-492f-aa55-b5f9146c23fa · outbound

This paper cites Intelligent multi-modal sensing-communication inte- gration: Synesthesia of machines,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Intelligent multi-modal sensing-communication inte- gration: Synesthesia of machines,

Reference 4

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source=pdf_text observed=2026-08-03T10:44:47.217382Z digest=sha256:42bd182352efcde6e74e09fc243abf36a27f323159ce68066e690603c44d300a

Observation 86ebfc75-a7e5-480a-a0b5-318292aa65d8 · outbound

This paper cites Multi-Modal Sensing-Aided Channel Prediction for 6G mmWave Massive Antenna Systems,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Multi-Modal Sensing-Aided Channel Prediction for 6G mmWave Massive Antenna Systems,

Reference 5

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source=pdf_text observed=2026-08-03T10:44:47.267859Z digest=sha256:16b1ea82a5d8ff979719efd76f12a2e893ed01242d1fa1b24737acf9cc06b486

Observation 673954bc-642f-4551-be6c-ced47df24987 · outbound

This paper cites Vision-Assisted Near- Field Channel Estimation for XL-MIMO Systems,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Vision-Assisted Near- Field Channel Estimation for XL-MIMO Systems,

Reference 6

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source=pdf_text observed=2026-08-03T10:44:47.341183Z digest=sha256:548b409049305996386c567a9d29718ba27652511d346142d811b36464e779e7

Observation 026c7bab-58a7-4b1c-b7a2-83a9c1b042ed · outbound

This paper cites Synesthesia of machines (SoM)-enhanced wideband multi-user CSI learning with LiDAR sens- ing,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Synesthesia of machines (SoM)-enhanced wideband multi-user CSI learning with LiDAR sens- ing,

Reference 7

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source=pdf_text observed=2026-08-03T10:44:47.485082Z digest=sha256:2bdf9d57d8cdcd4ef90cdfbbda0645557a98a58e8a485a0f767179de71397441

Observation 74e2a345-17ec-4e0a-98b1-0c206a86b2bf · outbound

This paper cites Integrated sensing and communications toward proactive beamforming in mmWave V2I via multi-modal feature fusion (MMFF),.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Integrated sensing and communications toward proactive beamforming in mmWave V2I via multi-modal feature fusion (MMFF),

Reference 8

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source=pdf_text observed=2026-08-03T10:44:47.595888Z digest=sha256:6fe95a94d1e893a2c2ffe08cd30e36433fb24e44da73114572cfc58f2e2a9f25

Observation 5caf607b-96d0-4463-b97b-2b1f4f9cfd26 · outbound

This paper cites Multi-modality sensing in mmWave beamforming for connected vehicles using deep learning,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Multi-modality sensing in mmWave beamforming for connected vehicles using deep learning,

Reference 9

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source=pdf_text observed=2026-08-03T10:44:47.718246Z digest=sha256:56d24a9fa7f58df9e7c839a2a94052c23ca917a63d6c1c5a168a948025988526

Observation c5d515a1-a636-414b-bbfc-05b85554a035 · outbound

This paper cites Camera based mmWave beam prediction: Towards multi-candidate real-world scenarios,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Camera based mmWave beam prediction: Towards multi-candidate real-world scenarios,

Reference 10

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source=pdf_text observed=2026-08-03T10:44:47.784430Z digest=sha256:0c744897f4a7df0297d08c911c97dc2cc377187c2b977865931ab8d958663110

Observation b6edaafe-9f88-4ad5-abb3-162f25dce293 · outbound

This paper cites Vision Image-Aided Near-Field Beam Training for Internet of Vehicles Communication Systems: From Daytime to Nighttime,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Vision Image-Aided Near-Field Beam Training for Internet of Vehicles Communication Systems: From Daytime to Nighttime,

Reference 11

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source=pdf_text observed=2026-08-03T10:44:47.816967Z digest=sha256:75b0d66b3819f32e0ad407f8f7e5a1feb14af2be37fb1e0792f1d1de1a39edf3

Observation 2a4c9b04-abad-4038-8c93-664c8dfc84b4 · outbound

This paper cites Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach,

Reference 12

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source=pdf_text observed=2026-08-03T10:44:47.875980Z digest=sha256:b96ea2fa26fc463ed46b89548fcf1ce18d81c2e381c94a8e0c934386b025793e

Observation 17c82a04-2945-4bba-a9eb-3b7a9ebac71e · outbound

This paper cites Learning transferable visual models from natural language supervision,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Learning transferable visual models from natural language supervision,

Reference 13

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Observation ad2a6719-aa19-482d-99d6-b4381f118ffe · outbound

This paper cites DiffCL: A Diffusion- Based Contrastive Learning Framework With Semantic Alignment for Multimodal Recommendations,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model DiffCL: A Diffusion- Based Contrastive Learning Framework With Semantic Alignment for Multimodal Recommendations,

Reference 14

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source=pdf_text observed=2026-08-03T10:44:48.098280Z digest=sha256:82b672ab79d2db34b48e71389ade72d36e0242c6d05acd7fc1fa4f1757c73b30

Observation f498a039-49be-4c77-a242-0aced72fec69 · outbound

This paper cites Contrastive Reg- istration for Unsupervised Medical Image Segmentation,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Contrastive Reg- istration for Unsupervised Medical Image Segmentation,

Reference 15

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source=pdf_text observed=2026-08-03T10:44:48.213495Z digest=sha256:ac3e04d5ef2c0dda5423bcace8ae3ea5f802b6f2a9cb002e22fec46062f6c635

Observation 01e9e9d9-a0c9-4267-ac94-f6703ce6f988 · outbound

This paper cites Multi-modal graph contrastive learning for micro-videorecommendation,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Multi-modal graph contrastive learning for micro-videorecommendation,

Reference 16

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Observation 5af51586-8858-4ac8-a199-15503403c84a · outbound

This paper cites A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency

Reference 17

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Observation 9a2d3225-13d6-47da-b7f8-a7a88aa61fbe · outbound

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

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks

Reference 18

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Observation a612cdff-fd0e-403b-9c87-108f90996f64 · outbound

This paper cites When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model When Vision-Language Model (VLM) Meets Beam Prediction: A Multimodal Contrastive Learning Framework

Reference 19

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Observation ec3d51f2-58f8-44e7-b75b-fba3dc0e0723 · outbound

This paper cites Wireless Multimodal Foundation Model (WMFM): Integrat- ing Vision and Communication Modalities for 6G ISAC Systems.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Wireless Multimodal Foundation Model (WMFM): Integrat- ing Vision and Communication Modalities for 6G ISAC Systems

Reference 20

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source=pdf_text observed=2026-08-03T10:44:48.573724Z digest=sha256:1e823ba8c643624b16f7dac1a409b7651b192bee4c0abbc7de425d89986edb4a

Observation b59d0e08-f3f2-49bd-bed1-641b63501d2a · outbound

This paper cites Deep residual learning for image recognition,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Deep residual learning for image recognition,

Reference 21

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source=pdf_text observed=2026-08-03T10:44:48.704609Z digest=sha256:c978ea942f12476d0353d7d8dce0284da8ca66b1c8f4416d4f8c8dab7c3c4e20

Observation f6e53c24-ed25-4d9c-a702-6e782850966c · outbound

This paper cites PointNet: Deep learning on point sets for 3D classification and segmentation,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model PointNet: Deep learning on point sets for 3D classification and segmentation,

Reference 22

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source=pdf_text observed=2026-08-03T10:44:48.862459Z digest=sha256:2214ede76729a395f2ba46878e49bbc56f4b0e02e64cbbcb9ce2bf147708cd9a

Observation 92801751-f82a-4980-b88f-682feb94e9cc · outbound

This paper cites WiFo: Wireless Foundation Model for Channel Prediction,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model WiFo: Wireless Foundation Model for Channel Prediction,

Reference 23

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Observation 80742ab1-b78f-4bc2-8eec-62c4e56e2a7e · outbound

This paper cites Denoising diffusion probabilistic models,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Denoising diffusion probabilistic models,

Reference 24

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source=pdf_text observed=2026-08-03T10:44:49.031622Z digest=sha256:5fef2eb8b9731d9d1a0ac23d53d96c8839e232eb7128ad20bf6114c32644b121

Observation da1e7159-f9ac-4a8d-a627-df53c4978b8c · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Momentum contrast for unsupervised visual representation learning,

Reference 25

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source=pdf_text observed=2026-08-03T10:44:49.116910Z digest=sha256:0b8bb904898093ed835ba294482fcaa594026da69e43d67b18271017c76f8c3b

Observation f5dd86af-2406-4408-a85d-a0b7a6723742 · outbound

This paper cites M 3SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model M 3SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration,

Reference 26

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source=pdf_text observed=2026-08-03T10:44:49.183404Z digest=sha256:6f37b19b4de74e0f51bd5281e68f97428c8896110131cca1090c4c8c0787b2f6

Observation 97900f23-3b2d-4076-88d5-bf25a81641ad · outbound

This paper cites SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM).

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)

Reference 27

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source=pdf_text observed=2026-08-03T10:44:49.253688Z digest=sha256:68aa9ba84b867a6e84c02b4a6ba6e4e08508e2003a5f26b33bb8d61882980a95

Observation 83a1dd0b-fdd3-40dc-adba-4c7f7c5a7b05 · outbound

This paper cites Multimodal deep learning empowered millimeter-wave beam prediction,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Multimodal deep learning empowered millimeter-wave beam prediction,

Reference 28

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Observation 4807192c-07f2-49e8-bd8e-0e56e294548f · outbound

This paper cites SynthSoM- Twin: A Multi-Modal Sensing-Communication Digital-Twin Dataset for Sim2Real Transfer via Synesthesia of Machines.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model SynthSoM- Twin: A Multi-Modal Sensing-Communication Digital-Twin Dataset for Sim2Real Transfer via Synesthesia of Machines

Reference 29

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Observation 58fd50e1-c9f8-4a0c-89e9-433b4eb089b5 · outbound

This paper cites ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communica- tions,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communica- tions,

Reference 30

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source=pdf_text observed=2026-08-03T10:44:49.591106Z digest=sha256:bc972acc5c84e916cc12d4633b26d21d263326ab174421041794be2c48e43c6e

Observation 741ed448-c7df-4431-9b02-ff1a916c9819 · outbound

This paper cites DeepSense 6G: A large-scale real-world multi- modal sensing and communication dataset,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model DeepSense 6G: A large-scale real-world multi- modal sensing and communication dataset,

Reference 31

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source=pdf_text observed=2026-08-03T10:44:49.655184Z digest=sha256:02fb32309f23fb0dd28be79cc99f0adb129d4b3ceb400301e6abdc76a5d3128c

Observation a4f66434-01bb-40e7-a18f-e2670c170612 · outbound

This paper cites Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO,

Reference 32

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source=pdf_text observed=2026-08-03T10:44:49.761084Z digest=sha256:13c399620f78d31fc85488e1bd362f50ebc3744020feb6a12fffed582b8b623d

Observation 370b3953-1f1b-4cc4-af08-992a359aea1b · outbound

This paper cites Deep learning-based beamspace channel estimation in mmWave massive MIMO systems,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Deep learning-based beamspace channel estimation in mmWave massive MIMO systems,

Reference 33

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source=pdf_text observed=2026-08-03T10:44:49.864818Z digest=sha256:034c0b8f8ac784db85c15dc256d8c4558465eccb9eee0b3dbcb53922c9ddd525

Observation f8a6c103-f652-43c1-b3d6-cda7960b9719 · outbound

This paper cites Deep Learning Super- Resolution-Based Channel Completion for Massive MISO Systems,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Deep Learning Super- Resolution-Based Channel Completion for Massive MISO Systems,

Reference 34

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source=pdf_text observed=2026-08-03T10:44:49.969897Z digest=sha256:777ff3ff38437e2088cc93314a69e134570c6169d0907af99ae40f1b6e00e452

Observation 246739e8-3dea-444a-bb51-d00d7f1409d8 · outbound

This paper cites Channel estimation in IRS-enhanced mmWave system with super-resolution network,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Channel estimation in IRS-enhanced mmWave system with super-resolution network,

Reference 35

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unresolved
no resolver link, observed 2026-08-03T10:44:50.068017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:44:50.068017Z digest=sha256:c993a66071b83fd2f82076ad1eff2cbd764519b9d071bb57f7be7f6a0977bf70

Observation 5f91ed0e-04ec-4881-8c78-28b5d962366c · outbound

This paper cites Nerf2: Neural radio-frequency radiance fields,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Nerf2: Neural radio-frequency radiance fields,

Reference 36

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unresolved
no resolver link, observed 2026-08-03T10:44:50.142177Z

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source=pdf_text observed=2026-08-03T10:44:50.142177Z digest=sha256:e9d37956e1b7c07e8f19ff87639aec80ce85747fe88e29a761172a96df97f9f9

Observation 320f118e-028d-45ef-95a8-184711632d9d · outbound

This paper cites Fire: enabling reciprocity for fdd mimo systems,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Fire: enabling reciprocity for fdd mimo systems,

Reference 37

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unresolved
no resolver link, observed 2026-08-03T10:44:50.194734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:44:50.194734Z digest=sha256:756779b8b9934965aa4532d44dfbf0c42618a083c2ed1c0e2b9f4d69e01b493b

Observation d15f391e-8532-48e2-a037-5a882d02c954 · outbound

This paper cites Accurate Channel Prediction Based on Transformer: Making Mobility Negligible,.

WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model Accurate Channel Prediction Based on Transformer: Making Mobility Negligible,

Reference 38

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unresolved
no resolver link, observed 2026-08-03T10:44:50.262014Z

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source=pdf_text observed=2026-08-03T10:44:50.262014Z digest=sha256:024438c3dd00600916e62d84d1c0ad85d0f8823c05c33eec9ab3ce8e79031c31

Pith citing papers

Observation f70d6899-7369-4310-a50d-daffb68b71f0 · inbound

Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface cites this paper.

Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model

Reference 41

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verified exact
arxiv_id, observed 2026-07-28T02:21:36.948638Z

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=2026-05-10T04:41:14.892503Z digest=sha256:27978bf37571916c0e4ea7764225a06612bb1a8fcd855e68117db5e2a41cf53b

Observation ffd98844-8cda-4c41-b73a-9ba0a085c19c · inbound

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

WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM) WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-28T02:21:36.948638Z

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=2026-05-10T04:04:50.254833Z digest=sha256:f32d67c0c18be43f496c973b945a18eef795e20a5a0c4f6c47f145cf426e0b2a

Observation 763aa201-b096-4de2-afd1-f49b48ee39a0 · inbound

Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy cites this paper.

Foundation Models for Wireless Communications: From PHY Intelligence to Network Autonomy WiFo-M$^2$: Empower Wireless Communications With Plug-and-Play Environment Sensing via Foundation Model

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-07-28T02:21:36.948638Z

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=2026-06-28T00:11:57.999438Z digest=sha256:5dfafc2cdd0c323f65a0e5b07b70b838fee3fb7342d34236bc7c659edb6fb229