Paper Citation Record · LEDGER

Continual Learning as Computationally Constrained Reinforcement Learning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2307.04345.

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

pith.paper-citation-record.v1
2307.04345 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:44.481432Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T12:54:07.351101Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e79f6b9-11c6-470c-8261-502306f55608 · inbound

Learning Model Successors cites this paper.

Learning Model Successors Continual Learning as Computationally Constrained Reinforcement Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-09T19:58:58.811971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa181d1-99f1-40a5-aaf4-d653d4f794a7 · inbound

Decision Making in Hybrid Environments: A Model Aggregation Approach cites this paper.

Decision Making in Hybrid Environments: A Model Aggregation Approach Continual Learning as Computationally Constrained Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T17:24:36.499928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f76e2d38-6922-4e82-b427-0bb18df945f4 · inbound

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn cites this paper.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Continual Learning as Computationally Constrained Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.020778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad39768a-f3a9-4ef1-aa5c-74487b7c2846 · inbound

Memory Allocation in Resource-Constrained Reinforcement Learning cites this paper.

Memory Allocation in Resource-Constrained Reinforcement Learning Continual Learning as Computationally Constrained Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:14.408841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c080a08e-0bfc-46c1-a6fa-cf97e8b436c5 · inbound

Optimizers Qualitatively Alter Solutions And We Should Leverage This cites this paper.

Optimizers Qualitatively Alter Solutions And We Should Leverage This Continual Learning as Computationally Constrained Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:21.643315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40f6d51a-f2a1-40eb-8142-1ff5667e6117 · inbound

Capacity-Constrained Continual Learning cites this paper.

Capacity-Constrained Continual Learning Continual Learning as Computationally Constrained Reinforcement Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:54:07.397982Z

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 42e671c6-30d9-48c4-a982-7ee30ad37c4b · inbound

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control cites this paper.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Continual Learning as Computationally Constrained Reinforcement Learning

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T00:48:44.481432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.