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Paper Citation Record · LEDGER

Maintaining Plasticity in Continual Learning via Regenerative Regularization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2308.11958.

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

pith.paper-citation-record.v1
2308.11958 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:31:07.781548Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:38:55.631407Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
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  • 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 e7cfef08-df5d-406c-a6cb-6f94a450dca7 · inbound

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control cites this paper.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T20:31:07.781548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:31:07.781548Z digest=sha256:5c61433e4ca5f20f168a756206b9c1f1abc705ace16a3dcf761763d6ec47d54b

Observation c4edfe33-53e8-4374-87f7-20dc05562855 · inbound

Recovering Plasticity of Neural Networks via Soft Weight Rescaling cites this paper.

Recovering Plasticity of Neural Networks via Soft Weight Rescaling Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:23.120388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:23.120388Z digest=sha256:d31a6794b28fa39566e52e739720035836b259a7d8ee2c5688586259c979440f

Observation cef2994e-e1ef-44d0-bf1b-526390a46469 · inbound

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning cites this paper.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T15:55:34.366900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:55:34.366900Z digest=sha256:5147dca4ad11b80a841e3b3b5def9cd10150f4756cdfc7c1a1011685aeec7425

Observation d8a5a345-7909-4c61-9dc7-80eaa095069a · inbound

Activation Function Design Sustains Plasticity in Continual Learning cites this paper.

Activation Function Design Sustains Plasticity in Continual Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T13:01:23.588260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T13:00:27.749673Z digest=sha256:205cb9ed9f25449f2bb003f5b9aba9c6299a65e6c183f14f5c7c71b992e08c94

Observation b3bdfb90-74f9-4db1-921a-cf4c0a189227 · inbound

Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity cites this paper.

Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:50:36.731030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-21T20:46:47.856655Z digest=sha256:a0bc8a113c9856b5241031d5278238abc0ddd8662d518e554dc4ec0410587f59

Observation 6d72d327-113a-4599-9836-5a5cea5e414d · inbound

Weight Decay Improves Language Model Plasticity cites this paper.

Weight Decay Improves Language Model Plasticity Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 1991

Resolution
unresolved
no resolver link, observed 2026-08-03T00:15:37.314940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:15:37.314940Z digest=sha256:c2876316a0e9d50b049eab610e421ef21fffb45d712f491e680d5011eed3d934

Observation 234a8da6-a606-4937-9911-061f7f370a6d · inbound

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning cites this paper.

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T11:05:08.895791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T11:01:17.325738Z digest=sha256:6b0e0dea48f39f6d180b0582c07dafcc6bd90736803526069c8d5be90ef4d32f

Observation a9a1b00e-8830-4d71-965a-fdfd57e64870 · inbound

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning cites this paper.

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-12T19:46:39.624903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T19:46:39.624903Z digest=sha256:7b3bbcfed9e079f02e7fe3d0313fdb37c9f31b608571d3f188743805099553e0

Observation abd819cb-3f6b-4c46-b0f2-16dd55de3d39 · inbound

Rotation-Preserving Supervised Fine-Tuning cites this paper.

Rotation-Preserving Supervised Fine-Tuning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:27:24.390271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T06:26:20.393476Z digest=sha256:bb25dfa993e228eedf95b7868b76c0fce50f69a16dd5db31c50adc06b250910e

Observation 541754b5-3304-4103-9017-aa795e9576d3 · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-19T15:42:38.715161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T15:38:00.503752Z digest=sha256:db7a0b732c4e80eed7c64fbd6f693654ee1a94a00ef8ad51e431cdaa93c86db8

Observation 9770658a-58dd-4b3f-bf95-ed31ca3ed894 · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-06-30T20:55:03.838165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T20:53:15.045006Z digest=sha256:a80d2e3468e2d717a127385c10d66e310aedd970fa4ba33643be154be65e5de9

Observation af9ad2d5-c595-4d3d-8363-895d580c5f22 · inbound

Preserving Plasticity in Continual Learning via Dynamical Isometry cites this paper.

Preserving Plasticity in Continual Learning via Dynamical Isometry Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T00:07:28.534335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T17:26:20.769515Z digest=sha256:5bee3b45dc899c2785b7fa9c2a6b50879a0728309d3b4f4f7c807d4a982e91c0

Observation 8d47aac0-d306-42cc-bb72-8235e788a133 · inbound

SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR cites this paper.

SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T20:38:55.633287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T01:15:18.237335Z digest=sha256:b64b29eb6ffc2a714b4d047af1e09767e06df0acc73541de920b02ea4632716c

Observation b37f0e33-7311-4673-9e7d-032c38a7bda5 · inbound

Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization cites this paper.

Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-02T05:21:31.649236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T05:21:31.649236Z digest=sha256:27bf08384d4ad2b2d760a8365fe4bbe94967a7416b1194026c60de45708cee21

Observation 8f4e7ccd-aa41-49b8-8c93-d0697b0e24d7 · inbound

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform cites this paper.

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-31T18:18:00.204764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T18:18:00.204764Z digest=sha256:355f8928585b0323489763cca5cc18cbc472022f20f33e9e5b6f5d58d042bd36

Observation 73b94363-6393-43dc-b041-c0868fd83d9a · inbound

Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning cites this paper.

Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-07-31T04:05:50.132486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T04:05:50.132486Z digest=sha256:7277e467b3590c3ecd6dd02f33bf4ed77212b625d494ce3565ef4c1c5ebca5d8