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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.414866Z digest=sha256:15c4821883c47107f8bfa95ff7839fb66f5976ae0404d0b4728afb0207388005

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:b259de5fc56910cf235c4cb0ff203aabda8524d5ccef5773cef81d75ba606dfd

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.423050Z digest=sha256:4de5b3158b98d2964b261555a9b4c67f97be5dc45ccf7077b1fabd45a8fe55a1

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.427294Z digest=sha256:253a5936515dd4e508a15f1457c5137afe9cd9ea9f6e9957fbe3479befa2507f

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.431332Z digest=sha256:2da54d0fba584b661a252f1db8f881fedc9cfce2cbbb02e90890d58e7adcf2ec

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:e41d0bc86b6445154e19c74bfa07abd5244d9a9ea68e3eebfcae147dbe2499ec

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.440207Z digest=sha256:45c743b3cac56cd02cdd373a1447e100d67b298060837e18b5b4d9cc2ac17e0f

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.444322Z digest=sha256:203862b755b25ce7ff025c504845b88c064c2a3aee40b739a558b97cbe1b1587

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=sha256:5d14979757ca1bf24d5ef744038a7a6906df9eb6946a9774c57fab29889ec6e3

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.455353Z digest=sha256:a5061cb2511c1309a00167c16be3576f9855f60e42e9cdac83f4c80f1cc91033

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.459306Z digest=sha256:02122da5b17c6c7f2b5dfb3f27a548cfd491452b4f658898ee09f4e2bbb8c98c

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.463006Z digest=sha256:6d623dbce3abbe6013004b8d03876f4cb095357621d02fb718cf165329831754

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.466922Z digest=sha256:16d389cb751f60f50350d9536a7231231b95db8b695ec0d0e178e8c542ee5658

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.470829Z digest=sha256:4ca525cd7686e7e8898bdd7cf3c8b6fda2ac0a9cdca15f1cbe85cd3c098f81c5

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.474687Z digest=sha256:44feb09899f734d381e46ffefb1b51212760d4d1176e08d49e25b0273ae38cd7

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.478611Z digest=sha256:0b216ddc867819d01d59b5a4ff287f866d623a7332084bf1a7a635f283d91de9

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.482370Z digest=sha256:7d59c37f40a9d3a585675bc900bbcec9caa1cec7b233bfd5a293b2ccb47636c3

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.486393Z digest=sha256:0f1949aa3b16ce486011b3ec5d4f5f24dff7ebe0994acc35867a2daddc4c737d

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.490760Z digest=sha256:6c5e791a5551017faf08759c774776da64235261db0d13ee80c9508408f8704a

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.494492Z digest=sha256:3f12e6026783d78d92d4b2e3c22b01d9164b7aecbf269952b7a9d4f0f84a3f57

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.498388Z digest=sha256:400193fbd6ab252f30162a20d350b88999b4266e2d6ea2805962dc7d5fe10694

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T04:07:30.502161Z digest=sha256:776638b524a7bfcd4c2252fcf31bae70a778f38316cc33647a406b087e7066d5

Pith citing papers

No inbound Pith citation observations are available.