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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 12 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 2026-08-12T06:34:41.77262+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

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  • 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:c0e0c09aa80f9a4badefd45a18f2aafcf263f75b8cc219c78aef0e271c8ec9e0

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:b31dd3179dc6fd14f85b1430ab08e10ab88d4a041b4f15172fb335b2b948fc04

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=sha256:7beb3122345aa3aea46eead00fac0c40099018818f63aaf031c9cacfc2190cef

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:a10cf9d2ad9c4cc1d2cd7b514e5481665d89d190fa0cbbbedbba56075f8b8329

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=sha256:36b7a5f50571e7a55cfa9864f2a8be46d64d88457d1c01486a01e84bb6d39804

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=sha256:503116074af830192ff8c2e31ea59302a43ab6a68f50be2e2067247c83af3454

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=sha256:3c9e64f34edc62d30c6430fb32d80cd4dadf714d4b381a198223026d302993c5

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=sha256:1521a3d74b3c96953aec79d24582e9d89bf5dc5ae2c9b83344e3d3f88e2e7b86

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=sha256:0373c05a0cf44f9ce81effe63a067dd8fb95a9f54972187a92ece4ae29ca67c5

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:f014f0d71fe16e04ccb20f003b14aed277f902b6f626e6e558e871d8c0da84eb

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:d80da7c757b55e14b4cdcb2e740c5ac6666ca7fb8d554ff61767e01e7d395040

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:cb724e4adf0af8d6f80083869722d493a1947dbd45e9edfc3cbee6b0ff627a6e

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:a239aff0a2754393c8d2b11880b56f1a81826e289db0fb231a450c884fc1e171

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=sha256:0752459e37c16b579951fcacf17fcea3f5d72ea5285b918cb6ba453c8b286a46

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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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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source=pdf_text observed=2026-08-03T10:44:48.938510Z digest=sha256:beb09435e0ba01f4c8ef45f07eea661b0d9d150b373160d9e1cb205db6f31294

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:ce9d1230d89fa707499e75d0e53bd6c97184403a451cee13cf4123c63fef2b6d

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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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=sha256:1b3bde50ead025a9461eb97e37c142d29d3423c552f04565600d75e96210171f

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=sha256:3a2461c9528991c0dbc3f94030c6484087abf046987fa562abbfa26248fb6c11

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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source=pdf_text observed=2026-08-03T10:44:49.493247Z digest=sha256:7a6bff3a80c6ccec783318332db85a64d90cf68b3cc4f86550b05bed2e9b1c51

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:a22c4b8529252012f3a532eb0e35fcfd69bffe8910d322836f252b75db19e2f4

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:b4c06feedf1496af9572778c0eae1c87828293481c1046cc091d4c6837877c87

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=sha256:11ca212e2377c5212ec1f42b7d691a2b9a095aa2919021ac6a71656bd81808b2

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=sha256:4ab06ec9000e83e0ea69fbdd6995a186509e5e774f8f32455a44743ed00a3cfe

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:ee41a1277f4a8e6a64bec353ee8b795fcaefe5aa1b18c5a366e87a07460cee1e

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=sha256:628a147e92ed3462e8399b790fadcd7662d7567d4cf9c5b432bcd9cf4d04b64d

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:bb63514c0001990d59c432abf178ed9d38245054badfb147386e5b89c5937661

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

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source=pdf_text observed=2026-08-03T10:44:50.194734Z digest=sha256:e1da12d90fc85add933e76da171d835c1829dfa400ece51d53b4c0c14abf10e3

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:44:50.262014Z digest=sha256:5acece376cb5d9f7d5fc460e698b1c471a6dd6159ce3a7b1bad26d1ca87b8c92

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T04:41:14.892503Z digest=sha256:b82de3113f98adc3c8d0c595542f3da0e9512d15c766b90806142403ca5a00e4

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-10T04:04:50.254833Z digest=sha256:c102b02be1f983feec284687a8b043135c169d50301dca31ada19bad4bd51ad0

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 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-06-28T00:11:57.999438Z digest=sha256:928b0f7657314e5a491ab12b2e0017681bf22c28efeb0bb4e82ef48b467d2a31