Paper Citation Record · LEDGER

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding

As of 13 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2508.18785.

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

pith.paper-citation-record.v1
2508.18785 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:18:16.643563Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:29:03.910400Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:49:45.036522Z

Reference resolution

45 of 45 outbound references displayed

verified exact1
verified fuzzy33
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2c599ddc-b44a-4f6a-88bc-84796a022ddf · outbound

This paper cites Contrastive self-supervised clustering for specific emitter identification.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Contrastive self-supervised clustering for specific emitter identification

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.830633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8f9ac8e2-f61e-4498-8a8b-bdf159d45111 · outbound

This paper cites A self-supervised learning-based channel estimation for irs-aided communication without ground truth.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A self-supervised learning-based channel estimation for irs-aided communication without ground truth

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.816139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 262c9cb2-0b66-47e3-bdb4-2643788595b3 · outbound

This paper cites Self-supervised visual feature learning with deep neural networks: A survey.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Self-supervised visual feature learning with deep neural networks: A survey

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.795469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1ae49460-1cb5-4e89-b5bc-5a7cc4f5dd69 · outbound

This paper cites A comprehensive survey on pretrained foundation models: A history from bert to chatgpt.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A comprehensive survey on pretrained foundation models: A history from bert to chatgpt

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.777320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4fe0f8c3-91ea-49bb-ab4e-9679334f0510 · outbound

This paper cites Towards a wireless physical-layer foundation model: Challenges and strategies.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Towards a wireless physical-layer foundation model: Challenges and strategies

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.757118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5097d66c-4bef-45c9-8c7b-02c3f4819896 · outbound

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

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A Wireless Foundation Model for Multi-Task Prediction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.454742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 275833a5-2693-4af0-bf7b-1173c60235e0 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Foundation models for time series analysis: A tutorial and survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.460239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e15c0ea-96e4-43f1-a418-db51794be3cb · outbound

This paper cites Deep learning in mobile and wireless networking: A survey.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Deep learning in mobile and wireless networking: A survey

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.719950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d31220c7-cc5b-47d8-834c-3c4fc1598062 · outbound

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

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A multi-task foundation model for wireless channel representation using contrastive and masked autoencoder learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.469291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af79184a-6b39-420e-9e64-1fd30ac44e2a · outbound

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

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Wirelessgpt: A generative pre-trained multi-task learning framework for wireless communication

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.698978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fcd79b84-cd82-4809-b8dd-0d63fb0ca6dc · outbound

This paper cites Sionna: An Open-Source Library for Next-Generation Physical Layer Research.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.480315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a62b643-8f31-4c8a-9202-ed6f13797eca · outbound

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

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.485727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5ac9d57-ebc2-44d9-988b-3aa43048cb9a · outbound

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

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding 6G WavesFM: A Foundation Model for Sensing, Communication, and Localization

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:18:17.073269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bf6dece9-a6c2-4c7c-bb70-83013ff0c08e · outbound

This paper cites Spec- trumfm: A foundation model for intelligent spectrum management.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Spec- trumfm: A foundation model for intelligent spectrum management

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.495962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca220396-f250-4546-a290-6c1c8786b78f · outbound

This paper cites Over- the-air deep learning based radio signal classification.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Over- the-air deep learning based radio signal classification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.679959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dfe24d96-6247-495c-b546-195b1deb8a67 · outbound

This paper cites Towards low- complexity wireless technology classification across multiple environ- ments.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Towards low- complexity wireless technology classification across multiple environ- ments

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.652293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 585c5157-4b9b-4737-adbf-aebbd4b6013a · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Foundation models defining a new era in vision: a survey and outlook

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.634011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5c507fe3-4324-4c07-9c65-242d5a9876f3 · outbound

This paper cites Foun- dation models in electrocardiogram: A review.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Foun- dation models in electrocardiogram: A review

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.516034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b0a258c-d69d-450a-aced-132ee2ea0c86 · outbound

This paper cites SpectralGPT: Spectral Remote Sensing Foundation Model.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding SpectralGPT: Spectral Remote Sensing Foundation Model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.520556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1763686-dd44-45f7-b6f2-c98a134c2d21 · outbound

This paper cites Attention is all you need.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Attention is all you need

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.526039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb225bfa-6b12-43de-ba68-6aad4b5c9aec · outbound

This paper cites Hisarmod: A new challenging modulated signals dataset.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Hisarmod: A new challenging modulated signals dataset

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.602958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b3f88ae6-0527-4010-a9fa-e67759b6c424 · outbound

This paper cites Classification of Radio Signals and HF Transmission Modes with Deep Learning.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Classification of Radio Signals and HF Transmission Modes with Deep Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.535447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 714dc043-c4a2-4520-847f-7301eb36a3e0 · outbound

This paper cites Dataset for modulation classi- fication and signal type classification for multi-task and single task learning.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Dataset for modulation classi- fication and signal type classification for multi-task and single task learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.585653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c66ac0dd-7c70-4c98-b51a-7414d7cd258b · outbound

This paper cites Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Wisig: A large-scale wifi signal dataset for receiver and channel agnostic rf fingerprinting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.566794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e407a026-727f-45c3-98cf-71d61e5818d1 · outbound

This paper cites Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.550660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1492db1b-c3f7-41b6-b380-e0ebbcd521c8 · outbound

This paper cites Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Trust in 5g open rans through machine learning: Rf fingerprinting on the powder pawr platform

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.534069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 379c7846-ef41-4065-bcf0-867f4c27f67c · outbound

This paper cites Transmitter classification with supervised deep learning.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Transmitter classification with supervised deep learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.517830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b0fcda3-e8d5-420f-aef7-f0bb2b521a1e · outbound

This paper cites Lora device finger- printing in the wild: Disclosing rf data-driven fingerprint sensitivity to deployment variability.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Lora device finger- printing in the wild: Disclosing rf data-driven fingerprint sensitivity to deployment variability

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.501731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 31d069a6-63f4-442c-8307-b81677d3acbc · outbound

This paper cites Zero-bias deep learning for accurate identification of internet-of-things (iot) devices.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Zero-bias deep learning for accurate identification of internet-of-things (iot) devices

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.481790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80b6955b-801f-43dd-80bf-01677024c122 · outbound

This paper cites Dronerfa: A large-scale dataset of drone radio frequency signals for detecting low-altitude drones.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Dronerfa: A large-scale dataset of drone radio frequency signals for detecting low-altitude drones

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.462613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c50c12ef-cbbb-4446-b552-4f1ddaefa3a0 · outbound

This paper cites Rf-diffusion: Radio signal generation via time-frequency diffusion.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Rf-diffusion: Radio signal generation via time-frequency diffusion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.447032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ab5fa3a8-1676-40fa-bbc7-937b6926c485 · outbound

This paper cites A generative self-supervised framework for cognitive radio leveraging time-frequency features and attention-based fusion.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A generative self-supervised framework for cognitive radio leveraging time-frequency features and attention-based fusion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.430416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 70f9c577-72a2-4ae3-8662-eadfb4f57b4a · outbound

This paper cites RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:16.585754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3ea23a-e069-47e9-8a72-bc3c08a72e42 · outbound

This paper cites Radio machine learning dataset generation with gnu radio.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Radio machine learning dataset generation with gnu radio

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.411797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a007a541-3f6d-477a-bc7c-41602136f6c3 · outbound

This paper cites Con- volutional radio modulation recognition networks.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Con- volutional radio modulation recognition networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.393806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 53ff73d9-1b84-408b-9339-e6b0cab56040 · outbound

This paper cites Multi-task learning for radar signal characterisation.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Multi-task learning for radar signal characterisation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.370577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ddeb691f-8241-48b3-8c95-987f77f6b1de · outbound

This paper cites Large-scale real-world radio signal recognition with deep learning.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Large-scale real-world radio signal recognition with deep learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.350911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b35156a7-fbe9-423f-8fd7-9ec816e4f296 · outbound

This paper cites Deep residual learning for image recognition.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Deep residual learning for image recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.334320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6a6e58fb-6d96-43d6-8db4-5e9c74a695a3 · outbound

This paper cites Mcnet: An efficient cnn architecture for robust automatic modulation classification.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Mcnet: An efficient cnn architecture for robust automatic modulation classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.316546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dfa6ba99-4315-44e1-b55b-ff5b0918fe97 · outbound

This paper cites Automatic modulation classification using recurrent neural networks.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Automatic modulation classification using recurrent neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.298858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 34344067-f13b-4e8f-9388-5feba64ec2b6 · outbound

This paper cites Real-time radio technology and modulation classification via an lstm auto-encoder.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Real-time radio technology and modulation classification via an lstm auto-encoder

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.279556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5669bbb6-46fc-4b47-b08a-940fc9eefaa7 · outbound

This paper cites Cgdnet: Efficient hybrid deep learning model for robust automatic modulation recognition.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Cgdnet: Efficient hybrid deep learning model for robust automatic modulation recognition

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.262043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1fbc4871-d875-46d4-812d-3f1a17744467 · outbound

This paper cites A novel automatic modulation classification scheme based on multi- scale networks.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding A novel automatic modulation classification scheme based on multi- scale networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.244635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1e3d0f20-e57a-4f4d-afc9-e65dda431f0e · outbound

This paper cites Amc- net: An effective network for automatic modulation classification.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Amc- net: An effective network for automatic modulation classification

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.222376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 68f5c926-dee3-4620-a892-330d9aa6342c · outbound

This paper cites Performance measurement in blind audio source separation.

EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding Performance measurement in blind audio source separation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:18:17.206479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 460f4471-d1a4-4977-b513-e18ddba02c1d · inbound

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization cites this paper.

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:49:45.038121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5da6ccf4-4fd6-469b-8b10-892250bf2269 · 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 EMind: A Foundation Model for Multi-task Electromagnetic Signals Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T23:29:03.910400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.