Pith. sign in

Paper Citation Record · LEDGER

Maintaining Plasticity in Continual Learning via Regenerative Regularization

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 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 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:38:06.826364Z

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
  • 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 533425b0-bec2-48c7-b74e-ad79c2eeaadb · inbound

Parseval Regularization for Continual Reinforcement Learning cites this paper.

Parseval Regularization for Continual Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T19:03:05.972473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:03:05.972473Z digest=sha256:b7ba593a81c0573f667e6831323c00b6544f2999a9f7cbc940d9e00d79152462

Observation f2e23cf0-b37e-49c4-b6c6-685bf6d3bb70 · inbound

Torque-Aware Momentum cites this paper.

Torque-Aware Momentum Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T04:32:51.583221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T04:32:51.583221Z digest=sha256:1ec8cff5795610f265101147e87ee7a239ff5e92e4cba141ca22fdfd31521ec8

Observation aaedbe56-acc7-4844-8859-809c19d6ee50 · inbound

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss cites this paper.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-09T15:43:34.502240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:43:34.502240Z digest=sha256:8178537b64f831d4f6732e7d0d22921c78b83a8a8d15926fad2640659126f20b

Observation f4f763f2-d9c2-4c3b-8366-782873ce2962 · inbound

Preserving Plasticity in Continual Learning with Adaptive Linearity Injection cites this paper.

Preserving Plasticity in Continual Learning with Adaptive Linearity Injection Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-15T21:33:59.029992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:33:59.029992Z digest=sha256:ccb69602d12bd389722f6f94d698a88a96ca7a337b266a598b329edc8eb15129

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:bdc398cf14fe62978d66032ece11070c5638de91777e1803f88462e44e52c733

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=sha256:9221b41bb26fa2ea1e7ff5d62af1a8c0de87adf79c01de1399580e18b4a098bc

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=sha256:7bfa625181e750a71d8012cbf36c3c01b71ec89a339d6f3a37f0209e04ac67eb

Observation 838c3a9d-5e08-49ea-95ad-313b3546d625 · inbound

What Can Grokking Teach Us About Learning Under Nonstationarity? cites this paper.

What Can Grokking Teach Us About Learning Under Nonstationarity? Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-15T17:59:10.328060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:59:10.328060Z digest=sha256:6914869086bdbeb4652f880e104bfe71fb15e12b6ab961735ecb3185c4e54519

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-18T13:00:27.749673Z digest=sha256:a701f4373c8730ce2b7578ccbe0fa090ee569ff66a0114cc9ee7382b22766276

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T20:46:47.856655Z digest=sha256:5951c5ca766b7f8921b39dbadda1daf9e9fc353d0dc1d39df675d7df8442298c

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:c6ba7ce96fd1545f336d83a19988617d015ff1aeb87b40f43e98d54d878bc4f7

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T11:01:17.325738Z digest=sha256:ce5426c9f9a64fd8a1f562216297a3fd3e2aa09f0e86cbac3a51127f3e1bccaa

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=sha256:2fe186bdc942849d40859171bd3e4fb69ca62273875738aeb01821935daff012

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-13T06:26:20.393476Z digest=sha256:e69cab9fbe599a032264c8d27c87804c0661f853b1f479f21f7058b973b1738c

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-19T15:38:00.503752Z digest=sha256:f78884bf4c7c51856dddc7d7ebe51c55fa2bda5625c19454d52d9b819be2a510

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-30T20:53:15.045006Z digest=sha256:03f759b49a106e55664c6d034377f7873ae9b0d73b746285650f7665e52b461b

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-27T17:26:20.769515Z digest=sha256:b487f62cb5de52c48194c0b48d27cd3d3520b99e7f7034f521975b731bb8a3c6

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-06-27T01:15:18.237335Z digest=sha256:14c12ca9ba5508f3d12b7852931a19261799aa86ec98e6a7a1715799998cdf6a

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:d35870a4ac87665ff8b84eaff6132a734c7e6fdb872ad7667f85648696edb665

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=sha256:899ae740eccc11a87b0f5c265644700b209d79c2cce83f28fbd6789f56e4e8a2

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:be1527db377b3baef0c970e7d5189c1776f9939a5087bb8f74832cff08ac1b78

Observation aaf84c7e-8d1a-457d-a952-401230e111d1 · inbound

NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning cites this paper.

NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-08T19:13:08.210893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T19:13:08.210893Z digest=sha256:4613ee29b7a47de222704d0d054fcc8370e6da4d9988fb10b59b032f585cd927

Observation 8c078f70-f851-4f6d-ba70-56c10cbd7c74 · 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 Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-12T00:48:44.464619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T00:48:44.464619Z digest=sha256:ffa719f2328d4cace2e425d33824f887755bcf2fd8b6ba5f75593d9cccb834cd

Observation b6c467b7-6154-4ce9-8baa-9c9b4eb18c80 · inbound

Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning cites this paper.

Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T21:38:06.826364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:38:06.826364Z digest=sha256:7612eb609f25b5819c76b7ebab9f46c40a27c2b330f5a971c3599a3bdc24f85b