Paper Citation Record · LEDGER

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications

As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2508.00042.

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

pith.paper-citation-record.v1
2508.00042 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:46:01.725632Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:12:08.396662Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T00:12:08.533003Z

Reference resolution

31 of 31 outbound references displayed

verified exact2
verified fuzzy22
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce3ca0d0-c14f-42f5-9922-5f2036f9e6f7 · outbound

This paper cites The role of ai enablers in overcoming impairments in 6g networks,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications The role of ai enablers in overcoming impairments in 6g networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.409294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3220dfdf-8556-4eb6-8dd3-a4f26f85ebc9 · outbound

This paper cites Machine learning and wi-fi: Unveiling the path toward ai/ml-native ieee 802.11 networks,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Machine learning and wi-fi: Unveiling the path toward ai/ml-native ieee 802.11 networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.392189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f8f297da-b1b6-4ebe-afca-85baf845700c · outbound

This paper cites Operationalizing ai/ml in future net- works: A bird’s eye view from the system perspective,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Operationalizing ai/ml in future net- works: A bird’s eye view from the system perspective,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.371067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10ea4acb-27ca-4c77-bb15-1436e14f393f · outbound

This paper cites Misconfig- uration in o-ran: Analysis of the impact of ai/ml,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Misconfig- uration in o-ran: Analysis of the impact of ai/ml,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.354355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c6e4c021-b8cd-41e3-94b6-ce1550f11a7c · outbound

This paper cites Learning under concept drift: A review,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Learning under concept drift: A review,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.598423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23c57d87-7166-4155-809f-3c894a2dda94 · outbound

This paper cites Leaf: Navigating concept drift in cellular networks,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Leaf: Navigating concept drift in cellular networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.327329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 442a6eac-731e-4782-9355-e2b7b9cda8df · outbound

This paper cites Log-a-tec testbed outdoor localization using ble beacons,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Log-a-tec testbed outdoor localization using ble beacons,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.311194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 423d0a98-355d-4f9e-a4fd-42f15a8851ad · outbound

This paper cites Resource-aware time series imaging classification for wireless link layer anomalies,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Resource-aware time series imaging classification for wireless link layer anomalies,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.295749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b969d22a-3ff4-4b9d-8a30-fc6d33d435e4 · outbound

This paper cites A unifying view on dataset shift in classification,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications A unifying view on dataset shift in classification,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.279984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6b4edaec-2a2b-4f6f-b66b-898bf8425431 · outbound

This paper cites Learning under concept drift: A review,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Learning under concept drift: A review,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.265131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51cf57ca-efee-4956-9192-6503981258c8 · outbound

This paper cites Learning with drift detection,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Learning with drift detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.250342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c2edc8c-6d50-40a9-8ed6-3cb1119c11df · outbound

This paper cites Continuous inspection schemes,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Continuous inspection schemes,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-06T10:46:02.041100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99d29e03-669a-4c03-82d0-1d0545b781e4 · outbound

This paper cites Bifet and R.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Bifet and R

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.638411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299458d6-7aa8-4ba8-9982-8b0896877dfe · outbound

This paper cites Detecting concept drift using statistical testing,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Detecting concept drift using statistical testing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.233288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b1d3b74-2cee-41b4-942e-ca5152b5a494 · outbound

This paper cites Concept drift detection using autoencoders in data streams processing,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Concept drift detection using autoencoders in data streams processing,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.218686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bcfb20e2-05b2-482c-9467-1e75ee79cf87 · outbound

This paper cites Recent advances in concept drift adaptation methods for deep learning.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Recent advances in concept drift adaptation methods for deep learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.203154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce0b3e42-b070-433e-9242-91807bf042f2 · outbound

This paper cites Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.188363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc0396f0-6203-4ffe-b412-ad39b33128ee · outbound

This paper cites A survey on machine learning for recurring concept drifting data streams,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications A survey on machine learning for recurring concept drifting data streams,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.173256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7747914f-894a-4679-878e-26ac3b30c978 · outbound

This paper cites Insomnia: Towards concept-drift robustness in network intrusion detection,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Insomnia: Towards concept-drift robustness in network intrusion detection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.156915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e9bdf676-007e-4636-8900-b4a3b26e7f39 · outbound

This paper cites Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous iot edge,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous iot edge,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.141407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c008b574-d750-4788-ae0b-df16072d29e6 · outbound

This paper cites Intrusion detection in the iot data streams using concept drift localization,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Intrusion detection in the iot data streams using concept drift localization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.124683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39d6d37c-b6b5-4df9-9913-28c921d960fc · outbound

This paper cites A multi-agent adaptive deep learning framework for online intrusion detection,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications A multi-agent adaptive deep learning framework for online intrusion detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.106769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b5a3788b-9e5c-4e77-9626-7353370a1258 · outbound

This paper cites 23when training and test sets are different: Characterizing learning transfer,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications 23when training and test sets are different: Characterizing learning transfer,

Reference 23

Resolution: malformed identifier; no resolver link, observed 2026-08-06T10:46:01.687297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 436491af-bb05-47b7-b5f1-8701adab304a · outbound

This paper cites On the impact of industrial delays when mitigating distribution drifts: an empirical study on real-world financial systems,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications On the impact of industrial delays when mitigating distribution drifts: an empirical study on real-world financial systems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.089373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8d1920a6-4cdb-4570-a922-ce0735e00231 · outbound

This paper cites Detection of data drift and outliers affecting machine learning model performance over time.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Detection of data drift and outliers affecting machine learning model performance over time

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.696517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc223cf-abf9-4e38-8cca-c81df9026b42 · outbound

This paper cites Why the pseudo label based semi-supervised learning algorithm is effective?.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Why the pseudo label based semi-supervised learning algorithm is effective?

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:46:01.926421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 95446a7e-5ef9-47f5-8f10-f68a38769c5b · outbound

This paper cites Pseudo-labeling and confirmation bias in deep semi-supervised learning,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Pseudo-labeling and confirmation bias in deep semi-supervised learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.072499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1587dc6-9c7e-4dca-ac92-6b8562aa72cc · outbound

This paper cites In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.710479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 649f405d-296b-4dd0-9984-0e519cd23a3a · outbound

This paper cites Xgboost: A scalable tree boosting system,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Xgboost: A scalable tree boosting system,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.715673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3dfe9093-d50b-4870-99a0-c503bb16ad5f · outbound

This paper cites TabNet: Attentive Interpretable Tabular Learning.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications TabNet: Attentive Interpretable Tabular Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T10:46:01.720065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b572cc4-74ee-4838-8c0a-271314b1cd47 · outbound

This paper cites Analysis of descriptors of concept drift and their impacts,.

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications Analysis of descriptors of concept drift and their impacts,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:46:02.056455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 394d9473-0f0c-4c5f-96ac-ee0738cd07a9 · inbound

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting cites this paper.

Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:12:08.539689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.