Paper Citation Record · LEDGER

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2501.00068.

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

pith.paper-citation-record.v1
2501.00068 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:20:52.274323Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

23 of 23 outbound references displayed

verified exact3
verified fuzzy20
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 503f8029-5e38-4a96-965a-c5e745e14dbc · outbound

This paper cites Chen and R.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Chen and R

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.640360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cc2ea8ca-798f-437c-8be1-79a8221079a5 · outbound

This paper cites Jones et al., ”Predictive Disk Failure Detection Using Machine Learning,” USENIX F AST, pp.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Jones et al., ”Predictive Disk Failure Detection Using Machine Learning,” USENIX F AST, pp

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.627462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation db9f82fc-d67c-47f0-8f07-838b16dfadb3 · outbound

This paper cites Wang and H.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Wang and H

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.614266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 63f80ddc-34e3-4ea4-99c8-dad973f43031 · outbound

This paper cites Brown and M.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Brown and M

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.600305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 686da3fd-b67f-4f88-809b-22905d163aa1 · outbound

This paper cites Davis and B.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Davis and B

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.586081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d4bafc51-9436-4a01-9173-57156713dda8 · outbound

This paper cites Smith et al., ”LearnSched: Reinforcement Learning for Disk Schedul- ing,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Smith et al., ”LearnSched: Reinforcement Learning for Disk Schedul- ing,” IEEE Trans

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.572256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8ebc4617-bf5d-4139-8ee2-bdf8c7cbadc7 · outbound

This paper cites Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:20:52.360799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b25dedd0-0960-4359-8f7e-965f39f1581f · outbound

This paper cites Harris and P.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Harris and P

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.558510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df22988b-9d3c-428c-8f26-d6c8087d876e · outbound

This paper cites Liu et al., ”Reward-Driven Cache Management with Reinforcement Learning,” VLDB, vol.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Liu et al., ”Reward-Driven Cache Management with Reinforcement Learning,” VLDB, vol

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.545356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 975de80d-b24e-40e8-9720-2b18df0e9862 · outbound

This paper cites Robinson and G.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Robinson and G

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.532254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 156c7590-7489-4841-96a6-b628454b82c6 · outbound

This paper cites White et al., ”Latency-Optimized Storage Through Reinforcement Learning,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques White et al., ”Latency-Optimized Storage Through Reinforcement Learning,” IEEE Trans

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.519076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 49bc8bdc-532e-47f6-914d-6110513e70ce · outbound

This paper cites Xu and L.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Xu and L

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.505710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 235b1ee3-c630-4a72-aab5-14a997314bc3 · outbound

This paper cites Richards et al., ”Predictive Storage Failure Management with ML,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Richards et al., ”Predictive Storage Failure Management with ML,” IEEE Trans

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.492497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06ca1acf-d256-4c81-a94a-0c393be552d1 · outbound

This paper cites Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:20:52.339660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 769901b8-8d20-4a78-bce3-1d5e8cf89e22 · outbound

This paper cites Nelson and T.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Nelson and T

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.478384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fbb4bc00-a344-4bc5-9bee-9ddf50d0ca69 · outbound

This paper cites Wright et al., ”QueueSched: Reinforcement Learning for Storage Queue Management,” USENIX ATC, pp.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Wright et al., ”QueueSched: Reinforcement Learning for Storage Queue Management,” USENIX ATC, pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.464439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 42e9c47e-7cbb-42fb-8169-f4890ea3a889 · outbound

This paper cites Yamada and S.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Yamada and S

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.450061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a26dbccb-ae5d-4f17-ac72-b142fb93f7ef · outbound

This paper cites Oliver et al., ”Predicting File System Workloads Using Deep Learn- ing,” ACM SoCC, pp.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Oliver et al., ”Predicting File System Workloads Using Deep Learn- ing,” ACM SoCC, pp

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.435536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 64a48b96-5331-4f09-9a63-768ea6bfc74b · outbound

This paper cites Johnson et al., ”CacheOpt: Cache Placement Using Multi-Agent RL,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Johnson et al., ”CacheOpt: Cache Placement Using Multi-Agent RL,” IEEE Trans

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.421236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 279c1a26-9528-4356-840d-c63a16e859c3 · outbound

This paper cites Garcia et al., ”IOBrain: Deep Reinforcement Learning for I/O Optimization,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Garcia et al., ”IOBrain: Deep Reinforcement Learning for I/O Optimization,” IEEE Trans

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.406491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b912a37-23e8-44a0-b290-bacf0cda4654 · outbound

This paper cites Nguyen and H.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Nguyen and H

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.390699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 38b473b2-2ac4-4b03-9ec9-c70cf1d472d2 · outbound

This paper cites TD3 Based Collision Free Motion Planning for Robot Navigation.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques TD3 Based Collision Free Motion Planning for Robot Navigation

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:20:52.316807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 19943910-9d7c-4931-b989-b7bd88f44028 · outbound

This paper cites Peters et al., ”MLTier: Intelligent Tiering for Hybrid Storage Systems,” IEEE Trans.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques Peters et al., ”MLTier: Intelligent Tiering for Hybrid Storage Systems,” IEEE Trans

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:20:52.375989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.