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Paper Citation Record · LEDGER

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2108.06325.

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

pith.paper-citation-record.v1
2108.06325 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:38:06.777641Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:06.953256Z

Reference resolution

0 of 0 outbound references displayed

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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 4071d506-5b6b-4840-a554-bb28b80e121d · inbound

Parseval Regularization for Continual Reinforcement Learning cites this paper.

Parseval Regularization for Continual Reinforcement Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 12

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no resolver link, observed 2026-08-11T19:03:05.907147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:03:05.907147Z digest=sha256:16d13cefc3475ac65620933ed49fa141ab1833566a22f3528c937d83f5f93d53

Observation e8ac5cf3-46be-47d9-a53e-5633a6ef8f9b · inbound

Torque-Aware Momentum cites this paper.

Torque-Aware Momentum Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 6

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no resolver link, observed 2026-08-11T04:32:51.512603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T04:32:51.512603Z digest=sha256:b9f1135f2785806b23ff4477789d53a0c3e25729e26f0b15acf59d76e60d9de8

Observation ba5e11ce-a69d-434d-a3d1-e0573161f456 · 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 Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 9

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no resolver link, observed 2026-08-09T15:43:34.341918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T15:43:34.341918Z digest=sha256:f15f2daa14f5d6e6f154764825a397646426ac54405126f9a4b8b6c3f25331ce

Observation 88f6d029-0ae1-46a1-b4cb-a3513eac9717 · inbound

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

Efficient Adaptation of Reinforcement Learning Agents to Sudden Environmental Change Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 216

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no resolver link, observed 2026-08-15T21:16:01.390559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:01.390559Z digest=sha256:dc8bdb2054a21f3a08d0fc9f6420c867dfb26c8234c79b0c0bf5cdc02458a681

Observation 6141deb0-1f43-459e-96cb-e193431be8df · inbound

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

Optimizers Qualitatively Alter Solutions And We Should Leverage This Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 23

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unresolved
no resolver link, observed 2026-08-06T16:56:21.558771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:56:21.558771Z digest=sha256:a9a4d24f955283963be899be7c0c28d546ce3317afa1110afaaf9af2f27d166f

Observation a560902e-5e70-47a7-ac13-2172d4976cb3 · inbound

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

What Can Grokking Teach Us About Learning Under Nonstationarity? Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 8

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no resolver link, observed 2026-08-15T17:59:10.317139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:59:10.317139Z digest=sha256:2910e753d502188b915569d0b9478992c493fb6e773eb54777c0682f94c70c15

Observation 3b27e44f-831c-4928-a701-4a6c52aa74ae · inbound

Reinitializing weights vs units for maintaining plasticity in neural networks cites this paper.

Reinitializing weights vs units for maintaining plasticity in neural networks Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T10:21:45.119866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:21:45.119866Z digest=sha256:744ca703549ffd814d86ad9f336b3c263f66411d0cad6dc678d9f01765cdfd5b

Observation acd1b070-c135-4a20-8c24-74c906ce65b2 · 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 Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:50:36.734608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T20:46:47.856655Z digest=sha256:9228ac6641582bb7edabb38444d327f0abf30d5a302d845b54af0f003b60a747

Observation 8420449b-5ca7-450a-9a9b-4748b0b06dfc · inbound

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

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T11:05:08.925433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T11:01:17.325738Z digest=sha256:8a01dfb84d84b679f40d26b4cc34e14392c27ad8a21190fd00156ba0ba858211

Observation 464aca60-e4df-4ec6-8b3d-f3a6a74bf823 · inbound

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

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 7

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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:b889dbe27ab0cbbf06d0b268d8627fb60fecfbb9d63c3b986ec13f0f26a260cb

Observation 947f2bfd-f56f-4970-880f-4f9e7c08f9c6 · inbound

Task Switching Without Forgetting via Proximal Decoupling cites this paper.

Task Switching Without Forgetting via Proximal Decoupling Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 43

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T11:30:19.480645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T04:51:37.828717Z digest=sha256:f16f3184ccdd19fe384d77169cbe2fa6d2557717b46167ea1b1a30ff8ef08b6c

Observation 9024205d-0cf4-4dce-8ce9-ed58c1126758 · inbound

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

Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:00:56.850735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-11T00:54:07.569467Z digest=sha256:f2beeafd1ab6d38f4d324b5b84e4dd4d3c27046342ab9ddbe433bc4233618c85

Observation a7124105-e1db-418a-8ef2-3c4ffd68ad8e · inbound

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning cites this paper.

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:29:13.173673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T23:26:23.989545Z digest=sha256:901f30195c62edb9b26bf133f7a4b0730c6d0d8cc898b2682602027560c66aee

Observation b25d48db-7454-4a87-8d9e-8952c9e8ab78 · inbound

Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods cites this paper.

Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 23

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T19:30:06.954940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-25T21:21:37.477077Z digest=sha256:04f58eac3dc03184284b15b144f8149ea157b5d3023e46020e2de984a838a770

Observation 99a5d01c-a610-4c78-b1b6-84125ccd6861 · inbound

To Retain or to Adapt? Generalizing Continual Learning cites this paper.

To Retain or to Adapt? Generalizing Continual Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 44

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unresolved
no resolver link, observed 2026-07-11T05:02:16.134830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T05:02:16.134830Z digest=sha256:c7fb6f6ee512393a5fbbeacb0ff05cd4d0834a3deb8581629012ad37d2dda365

Observation 2bf9b853-847d-4ec8-a039-b3941c086905 · inbound

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

Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-31T04:05:50.110821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T04:05:50.110821Z digest=sha256:69dac298703128b08692ae224b1b82fca8d39fbd611702ab73ea74dcf57a77b5

Observation e00af9aa-e572-4c40-a6cf-af1cfbabf268 · 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 Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 145

Resolution
unresolved
no resolver link, observed 2026-08-12T00:48:45.258831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T00:48:45.258831Z digest=sha256:21034bc82aeb5158e8d352f2c9b22c010cfaca3c680539a7ef20ca696cd03690

Observation 51ed391e-9767-4e0c-ac4b-11c0b3cf4a7d · inbound

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

Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T21:38:06.777641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:38:06.777641Z digest=sha256:329987bc439819dce9f354199033981d1dd470d7e3e900940c97df2c8742b02c