Paper Citation Record · LEDGER

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework

As of 7 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 1 inbound Pith citation observation for arXiv:2508.07085.

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

pith.paper-citation-record.v1
2508.07085 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:22:48.976249Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:44:39.466931Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T15:44:40.289426Z

Reference resolution

11 of 11 outbound references displayed

verified exact1
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5e82454-6ae7-4275-87d2-555fe5d9a00f · outbound

This paper cites Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.939917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a25b274b-6226-4614-b3b3-833b62815db6 · outbound

This paper cites Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T22:22:49.046129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5e98082-211c-4051-ab4d-8f7c85735090 · outbound

This paper cites METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.949035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f89dcc12-e64b-416d-8293-f604599d0e80 · outbound

This paper cites In: Proc.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework In: Proc

Reference 4

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T22:22:49.230138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1cdf14e-8266-4dba-a495-dda5b288178c · outbound

This paper cites Applied Sciences, 13(13), 6515 (2023).

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Applied Sciences, 13(13), 6515 (2023)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.956431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8442630-f618-4d4e-8efb-13f8c7edd97b · outbound

This paper cites In: 2023 International Joint Conference on Neural Networks (IJCNN), pp.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework In: 2023 International Joint Conference on Neural Networks (IJCNN), pp

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.959740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fa7465a-e81f-4670-ab3f-8a2d9b28e080 · outbound

This paper cites Electronics, 13(6), 1004 (2024).

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Electronics, 13(6), 1004 (2024)

Reference 7

Resolution: verified exact; doi, observed 2026-08-05T22:22:49.019877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cc345ec-dc91-4bde-ace3-29f2e5617333 · outbound

This paper cites Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.966148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c75fa047-43c9-42cc-8784-bee5c80b7c96 · outbound

This paper cites Attention Is All You Need.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Attention Is All You Need

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.969528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e09837fe-1b73-433d-a743-1636e957eaa3 · outbound

This paper cites CatBoost: gradient boosting with categorical features support.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework CatBoost: gradient boosting with categorical features support

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:48.972986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4059a8d7-49c9-4777-ac1d-099834aaa8e6 · outbound

This paper cites an unresolved cited work.

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-05T22:22:49.239900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 3d59fb5e-1a28-4c03-9309-037afe1233a1 · inbound

Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning cites this paper.

Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:44:40.354785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.