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Paper Citation Record · LEDGER

Maintaining Plasticity in Deep Continual Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2306.13812.

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

pith.paper-citation-record.v1
2306.13812 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:16:01.351139Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:26:29.423429Z

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 b6c0a6d6-0a9d-43e1-9372-e86d0247f9e5 · inbound

Plasticity Loss in Deep Reinforcement Learning: A Survey cites this paper.

Plasticity Loss in Deep Reinforcement Learning: A Survey Maintaining Plasticity in Deep Continual Learning

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-23T18:03:18.200332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-23T18:02:30.199552Z digest=sha256:7162468838a5289fd8b001e09d31cadd9646f8d10acc40c3890c461a6cf80ea4

Observation 53867a7a-1d4e-4f45-aff5-9ade45212742 · inbound

Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution cites this paper.

Efficient Language-instructed Skill Acquisition via Reward-Policy Co-Evolution Maintaining Plasticity in Deep Continual Learning

Reference 318

Resolution
unresolved
no resolver link, observed 2026-08-11T13:09:46.454679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:09:46.454679Z digest=sha256:65be2b81165baeb72e608e63ea87e6c7e4428ff0303b47790e80968a8a0258af

Observation 9e7a93a5-fdc3-40f5-b52c-7a601233e7da · inbound

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning cites this paper.

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning Maintaining Plasticity in Deep Continual Learning

Reference 2014

Resolution
unresolved
no resolver link, observed 2026-08-09T17:46:07.245077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:46:07.245077Z digest=sha256:340156067a975ae45195916e85abdb075c20285f86eb2074e31e7903bfb59e0e

Observation e636f14c-50ac-4ca8-8210-c76517af45c3 · 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 Deep Continual Learning

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-09T15:43:34.345429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:43:34.345429Z digest=sha256:61ee7654c82d4f36238287df826b0fdb4d202d94d96e9dcb17739ea40456e0d9

Observation 7a01f296-8cfe-46b0-9a99-17a71a86ff0b · inbound

Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change cites this paper.

Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Maintaining Plasticity in Deep Continual Learning

Reference 213

Resolution
unresolved
no resolver link, observed 2026-08-15T21:16:01.351139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:01.351139Z digest=sha256:701fb5c82b5bac94c77a2781b50d1c4e48ab46b06e8388c491373737717643ab

Observation b5fd2619-5719-453a-b739-20d862b81eea · inbound

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

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Maintaining Plasticity in Deep Continual Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T12:07:24.694086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:07:24.694086Z digest=sha256:a0f7b1046ee8130df56b8909d0773c53a9267bb4f3a695b1b3ff0a30fca76d2e

Observation f3d64815-1e32-4bc4-b540-b60984ab1afd · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Maintaining Plasticity in Deep Continual Learning

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T22:15:49.942327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-10T20:04:56.512544Z digest=sha256:c2abc0216b9e26c2f164fc5dcbc8652a50b2ad4c67cf8cd0ca5928a313446325

Observation 950c0a30-5f37-4c3d-93b2-7d24c6e556e3 · inbound

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control cites this paper.

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control Maintaining Plasticity in Deep Continual Learning

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T17:12:41.327292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-19T17:08:31.770889Z digest=sha256:46fd5414a1e726d3baa190dccc2933881922079470dbb04bd2eb1fd8e88f564d

Observation 416163b2-32de-4c7f-ac5c-249931f78a06 · inbound

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization cites this paper.

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Maintaining Plasticity in Deep Continual Learning

Reference 113

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:41:07.103838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-08T17:20:48.734193Z digest=sha256:657a16d6b3ead966bc86cb2e38e8b1f8b23e9ec8e3cc648c99efa78f5cf81fb7

Observation c23072a6-80f6-4ed1-bc9a-5d83e04cb867 · inbound

Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks cites this paper.

Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks Maintaining Plasticity in Deep Continual Learning

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T05:00:56.877257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-11T00:54:07.569467Z digest=sha256:49035e0fad99710ae6ccda246284ee0c8e9b030bde68080c55c37dd7427e14aa

Observation 206b76d1-e46d-4497-865a-c005a1821922 · inbound

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective cites this paper.

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective Maintaining Plasticity in Deep Continual Learning

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T02:41:17.743072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-12T02:38:44.847615Z digest=sha256:3ff3dabb0e55801ce45963c2bd25c9a4ad30c21419eee30ac018185a6e6f779f

Observation 8bd0698d-2c4c-4b19-98bd-00c2ebca09a7 · inbound

When is Warmstarting Effective for Scaling Language Models? cites this paper.

When is Warmstarting Effective for Scaling Language Models? Maintaining Plasticity in Deep Continual Learning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:02:53.011616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-14T20:00:47.126736Z digest=sha256:3ca6a2ce89987960fdda27e567923f7bb72aa85b2580711d636632db1a8b1427

Observation 6caa0baf-ce82-4f0a-825b-824ab3fd24e5 · inbound

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents cites this paper.

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents Maintaining Plasticity in Deep Continual Learning

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-07-02T03:26:29.425227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-28T10:03:49.270726Z digest=sha256:f658761af488a30aabfa35023a7f70fc4c3c5029c52676d1cf155c864499ca63

Observation b1338b8b-881d-4944-bd49-7cc6e514922c · 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 Deep Continual Learning

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-12T00:48:44.469730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T00:48:44.469730Z digest=sha256:cf249d0bc8d5e2fa891b831f2851a15bcd7f461380f172451daa979b373629e7