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Paper Citation Record · LEDGER

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks

As of 11 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.11466.

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

pith.paper-citation-record.v1
2506.11466 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:07:30.502161Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

24 of 24 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df5715d6-7052-4087-b34d-3c593c457440 · outbound

This paper cites write newline.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:30.409491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:30.409491Z digest=sha256:7f684c2d3c5dc2fd9875986d901ad1c035a979fbafeaf6439ecac8c8b31c598b

Observation cd81afe1-d9dd-43d7-b62a-e3fbddad99de · outbound

This paper cites Technical Report Group Radio Access Network; Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface ( V18.0.0).

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Technical Report Group Radio Access Network; Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface ( V18.0.0)

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.836384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.414866Z digest=sha256:d35445d9f878443dce473015aee64b6173003a5f5809f341b77d828672410dd4

Observation a86ddb80-6f63-4a7c-b933-bbe551e6613b · outbound

This paper cites 5G Positioning Advancements with AI/ML.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks 5G Positioning Advancements with AI/ML

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:30.418949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:30.418949Z digest=sha256:f40cc36af63104f6358fe9f99f1245b262e75f28d1db2fedddd5c0a9223e189c

Observation d12d99ed-7cab-4eb8-b5b5-841a77cd939a · outbound

This paper cites Assuring the machine learning lifecycle: Desiderata, methods, and challenges.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Assuring the machine learning lifecycle: Desiderata, methods, and challenges

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.823275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.423050Z digest=sha256:4598e6b1a131d43251079c7a6102bbf299009246afc607ccc4e6518d3ca1ea74

Observation 92361694-ae35-495d-a94e-f76d7c3d5019 · outbound

This paper cites and Globerson, A.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks and Globerson, A

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.810414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.427294Z digest=sha256:443b5b91b105b6bf7651d226d3bd0f01a4e1b3163db6e0883197936aa10aaa44

Observation 10e30c01-6b12-497b-a3b1-99731f2b4eea · outbound

This paper cites Meta-learning in neural networks: A survey.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Meta-learning in neural networks: A survey

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.797086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.431332Z digest=sha256:b8b0739fa4d573b7048ed3ac0a48f9dff30bb8a25a1bfa7fe56d601a4e743506

Observation 907a2aa7-133a-4896-8173-7c5f619cf900 · outbound

This paper cites Data Collection and Labeling Techniques for Machine Learning.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Data Collection and Labeling Techniques for Machine Learning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:30.435383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:30.435383Z digest=sha256:3c464b71038b481642b3a0c7acaf8b8037fc67221733480320d2f6872d856c2b

Observation 57d62643-a13d-4469-9f5f-381be3f385b6 · outbound

This paper cites AI for CSI Prediction in 5G-Advanced and Beyond.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks AI for CSI Prediction in 5G-Advanced and Beyond

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:07:30.557480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.440207Z digest=sha256:1db378f8dac46f07ccbc60c49408765017ec79f37f2a680e33d6be45d20fe149

Observation 63b2ec11-5c8c-4cbe-bc11-b74eacaff5ba · outbound

This paper cites The bridge toward 6G : 5G -advanced evolution in 3GPP release 19.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks The bridge toward 6G : 5G -advanced evolution in 3GPP release 19

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.783847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.444322Z digest=sha256:db3ae3ce2726a637b9b7a613c692579dcf5dd2fa28580fc574feccc53ecc8f7b

Observation 2ab28d17-cd1e-4f23-be24-65626c9c87d8 · outbound

This paper cites Self-supervised learning: Generative or contrastive.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Self-supervised learning: Generative or contrastive

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:30.447922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:30.447922Z digest=sha256:9099e58fa416965aa997b1299d5f185b7967530e60a5bfaee5dfb4e459783977

Observation 736b7560-77d2-4989-979d-2490df219614 · outbound

This paper cites Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T04:07:30.451469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:07:30.451469Z digest=sha256:7686a02abda2d0b9e66b0f9545dadc9192b79861e09c58b9c7d8243cb0507370

Observation 5167ec1c-efab-46e6-a05e-dfa37763a877 · outbound

This paper cites I know what you trained last summer: A survey on stealing machine learning models and defences.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks I know what you trained last summer: A survey on stealing machine learning models and defences

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.761994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.455353Z digest=sha256:0c98cbe217ef6ccd97064acbd04912665829d5652269a31e8ae1d510ac456188

Observation bb457913-deea-4cc8-b343-49a1f7d23a8b · outbound

This paper cites Radio foundation models: Pre-training transformers for 5g-based indoor localization.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Radio foundation models: Pre-training transformers for 5g-based indoor localization

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.749796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.459306Z digest=sha256:9851dc1ae36935ae4290dfedbf9b751b52802d4a80b2021f9343f9b4a12b4e0d

Observation 60274d05-66fd-45a4-b880-a0d156b45a9c · outbound

This paper cites B., Chen, X., and Wang, X.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks B., Chen, X., and Wang, X

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.737001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.463006Z digest=sha256:1af386284226d4278faea39170b36514dc13ef58ebff3725779a8a980613c888

Observation c1e5dc76-2bf0-4f92-a239-bfdd985a36bc · outbound

This paper cites Exploring LLM -based agents for root cause analysis.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Exploring LLM -based agents for root cause analysis

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.724317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.466922Z digest=sha256:8de29f3096ade03e400a642f277acb30ab755de367f4ac8fc0f9222c29ef9184

Observation c2469283-384a-4ede-9638-c84a39686213 · outbound

This paper cites and Norvig, P.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks and Norvig, P

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.711804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.470829Z digest=sha256:faf29bd3e409f9302a00196097e9becca40af6febd91b291a208e01403dd7f3d

Observation fb1ad5d0-a76d-4031-9282-96a9fa6d6e6e · outbound

This paper cites Channel charting: Locating users within the radio environment using channel state information.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Channel charting: Locating users within the radio environment using channel state information

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.698524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.474687Z digest=sha256:afa5c1f5fdffb20cd082bae0259fa3fbbe49d5f4b649da8087f775012e2dd447

Observation 9e19db04-5926-4b72-8972-945f80c1e7ca · outbound

This paper cites and Barto, A.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks and Barto, A

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.685512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.478611Z digest=sha256:549db30032ea2b7392cab3460bac52597f0d9d394f686b7ee43557387ffb0eb1

Observation a89856d1-eaf6-435e-8f3f-1a25b9c19387 · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Branchynet: Fast inference via early exiting from deep neural networks

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.671303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.482370Z digest=sha256:7a97ae371af43508d910b68dd921d4c600b3db38e60b71131b256e6c8d074502

Observation 11db3155-e777-447b-9cb8-0a9d3e03f206 · outbound

This paper cites K., and Ristenpart, T.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks K., and Ristenpart, T

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.656794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.486393Z digest=sha256:e7e2671f716087bd1998abdf142ca559925c60cf28634fedc7ab0cd73b20325c

Observation 70c2bd30-87fd-4b00-8fa6-a72ec0522a64 · outbound

This paper cites an unresolved cited work.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-07T04:07:30.643428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.490760Z digest=sha256:5669a6c9fd057a61a842c7cbc545c2415b5d79c85e8772fc0bd2cdfc5f07bd2a

Observation a974d940-2f66-401d-b07a-b99285592472 · outbound

This paper cites AI/ML for beam management in 5G -advanced: a standardization perspective.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks AI/ML for beam management in 5G -advanced: a standardization perspective

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.630240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.494492Z digest=sha256:35b6f53056e32190f10c56ff1f5fb6245a7228f206e4233c58bd2aecc98daae8

Observation 32f50a4c-cd5d-4471-b15a-c7f757a72b69 · outbound

This paper cites Deep learning based recommender system: A survey and new perspectives.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks Deep learning based recommender system: A survey and new perspectives

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.616941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.498388Z digest=sha256:448a36a2472bf30e0655cd18a0920eb37b3619a1cc7aa57c9dad1d92c386c86b

Observation c1c3546d-0cfa-4052-af83-6ecd988df8c1 · outbound

This paper cites and Yang, Q.

Position Paper: Rethinking AI/ML for Air Interface in Wireless Networks and Yang, Q

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:07:30.602587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.502161Z digest=sha256:909aae19b7fdb99a91e879cd7bcd5216d395396f95c1b7b4658f4c5744486ca0

Pith citing papers

No inbound Pith citation observations are available.