Paper Citation Record · LEDGER

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2507.06901.

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

pith.paper-citation-record.v1
2507.06901 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:56:58.336341Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

30 of 30 outbound references displayed

verified exact8
verified fuzzy14
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f386769-ab60-48d7-b929-23f186b791f8 · outbound

This paper cites Learning from Time-Changing Data with Adap- tive Windowing.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Learning from Time-Changing Data with Adap- tive Windowing

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbb4dfc3-f208-415b-87b0-01dce7dd2520 · outbound

This paper cites L., and Carrera, D.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams L., and Carrera, D

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.121372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8dcf1b97-2292-4862-8366-cd1edbdd5c67 · outbound

This paper cites SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:56:58.949393Z

Source-reported events for the cited work

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Observation 9091e482-7ab6-4f0d-be5c-ea4d9fc5c357 · outbound

This paper cites Streaming Deep Reinforcement Learning Finally Works.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Streaming Deep Reinforcement Learning Finally Works

Reference 4

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Source-reported events for the cited work

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Observation a7cce9d4-c662-4ab0-ae8a-4b7fba62403d · outbound

This paper cites Effects of Sliding Window Variation in the Performance of Acceleration-Based Human Activity Recognition Using Deep Learning Mod- els.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Effects of Sliding Window Variation in the Performance of Acceleration-Based Human Activity Recognition Using Deep Learning Mod- els

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.111168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e19f2cd-fcd8-4e72-beec-07f304840df6 · outbound

This paper cites In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics , pages 1–10, 2015.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics , pages 1–10, 2015

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:56:58.924591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1bc403d7-04ef-4f71-9f05-5fa1c8d79c96 · outbound

This paper cites Adaptive Windowing for Online Learning from Multiple Inter- related Data Streams.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Adaptive Windowing for Online Learning from Multiple Inter- related Data Streams

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:56:58.851401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 521bafc7-4e2f-4a08-82ab-ddaab8fe4655 · outbound

This paper cites Online Machine Learning in Big Data Streams.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Online Machine Learning in Big Data Streams

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:56:58.766819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9f8715e-6087-46bc-9441-39cc0547f55e · outbound

This paper cites Machine Learning for Streaming Data: State of the Art, Challenges, and Opportunities.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Machine Learning for Streaming Data: State of the Art, Challenges, and Opportunities

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.088264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5202cdf4-d191-432e-aacd-8abacbd7fa8a · outbound

This paper cites Maintaining Stream Statis- tics over Sliding Windows.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Maintaining Stream Statis- tics over Sliding Windows

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.077329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5481d590-4a41-4f89-975e-e318a1082eae · outbound

This paper cites S., and Barto, A.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams S., and Barto, A

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e13f843e-2dbb-41bb-9e13-028ac05e7539 · outbound

This paper cites Density-Based Data Streams Clustering over Sliding Windows.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Density-Based Data Streams Clustering over Sliding Windows

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.067281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d53f7327-1779-4cd8-b3fb-0fc829ba5246 · outbound

This paper cites Human-level control through deep reinforcement learn- ing.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Human-level control through deep reinforcement learn- ing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.056323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c0c57e4-e179-4cad-b22a-ec79da974fce · outbound

This paper cites Prioritized Experience Replay.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Prioritized Experience Replay

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:56:58.289753Z

Source-reported events for the cited work

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Observation f17f57da-2493-414e-874b-0f477db4ddae · outbound

This paper cites Dueling Network Architectures for Deep Reinforce- ment Learning.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Dueling Network Architectures for Deep Reinforce- ment Learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.045077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67b05968-5181-49e7-a871-06fbb8d8f158 · outbound

This paper cites V., Guez, A., and Silver, D.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams V., Guez, A., and Silver, D

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.034520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 167edfe9-fac4-4a4f-b498-65f0a8f39f56 · outbound

This paper cites Noisy Networks for Exploration.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Noisy Networks for Exploration

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:56:58.303846Z

Source-reported events for the cited work

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Observation 92d95f83-dd5a-4f19-ab8a-af7ca131f4f5 · outbound

This paper cites B., and Johnson, C.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams B., and Johnson, C

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.010416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6d99be5c-7e97-4f8a-ad38-96f808b2f302 · outbound

This paper cites Continuous control with deep reinforcement learning.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Continuous control with deep reinforcement learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:56:58.310626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46b9add8-f796-44b9-93b2-924c2b5fd276 · outbound

This paper cites Batch Nor- malization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Batch Nor- malization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:58.998357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73f5219a-61bb-453c-94af-76ffeb57ee0a · outbound

This paper cites Automa- tion of Sequential Feature Scanning in Streaming Environments.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Automa- tion of Sequential Feature Scanning in Streaming Environments

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:58.985983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0439e1c6-7b93-40e6-9811-bb8a98153cd1 · outbound

This paper cites A Transformer-based Framework for 13 Multivariate Time Series Representation Learning.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams A Transformer-based Framework for 13 Multivariate Time Series Representation Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:56:58.320117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0b564f-ca5d-4dc7-8def-9ef802c50f8d · outbound

This paper cites Intro- ducing a New Benchmarked Dataset for Activity Monitoring.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Intro- ducing a New Benchmarked Dataset for Activity Monitoring

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:58.974380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 48ce5daf-94c5-4ed7-8f1a-c1901630062f · outbound

This paper cites Stream-Based Active Learning in the Presence of Verification Latency.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Stream-Based Active Learning in the Presence of Verification Latency

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:58.961627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 644b76d9-439a-450e-b0f0-d9fe8d13e608 · outbound

This paper cites https://doi.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams https://doi

Reference 408

Resolution: verified exact; doi, observed 2026-08-06T18:56:58.362131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3081b2b3-6321-4381-9a2f-543a70550b21 · outbound

This paper cites net/publication/221086643_ Density-Based_Data_Streams_ Clustering_over_Sliding_Windows.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams net/publication/221086643_ Density-Based_Data_Streams_ Clustering_over_Sliding_Windows

Reference 2009

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:56:58.610935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86592bce-ccd8-42ae-9215-9865c60946e6 · outbound

This paper cites an unresolved cited work.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Unresolved cited work

Reference 2012

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:56:58.439341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b8045ee-b4f4-4a52-ae62-cddb45110ee5 · outbound

This paper cites php/AAAI/article/view/10295.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams php/AAAI/article/view/10295

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:56:59.023744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fb4ce65c-1ef7-49c1-a1eb-169642e0ca0c · outbound

This paper cites an unresolved cited work.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Unresolved cited work

Reference 2019

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:56:58.751253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3279695b-185b-473d-9688-47aae985844c · outbound

This paper cites an unresolved cited work.

Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:56:59.098893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.