Pith. sign in

Paper Citation Record · LEDGER

A Survey of Continual Reinforcement Learning

As of 17 August 2026, this Paper Citation Record lists 100 of 209 outbound references and 11 inbound Pith citation observations for arXiv:2506.21872.

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

pith.paper-citation-record.v1
2506.21872 v2

Coverage vector

measured 100 of 209 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T08:27:03.376909Z

measured 111 of 111 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:00:57.517509Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T19:30:07.939680Z

Reference resolution

100 of 209 outbound references displayed

  • verified exact10
  • verified fuzzy88
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 967ff375-bbf9-4eec-a065-dca450e2480d · outbound

This paper cites an unresolved cited work.

A Survey of Continual Reinforcement Learning Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-05-19T08:27:12.432852Z

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=2026-05-19T08:27:03.376909Z digest=sha256:1a2f79f4d1219fcb31e4daed1ce99731e9be5a2de2ce2dabdb51f00b936d4a25

Observation b3ad794c-f3ad-4b68-84b7-2a597d68c1dd · outbound

This paper cites Human-level control through deep reinforcement learning.

A Survey of Continual Reinforcement Learning Human-level control through deep reinforcement learning

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.448167Z

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=2026-05-19T08:27:03.376909Z digest=sha256:ed74c5b130a0a4868dc2695908e78ad089b772f6fbbcb4644d22b66208e7ab5d

Observation 46da753e-c281-4e5a-91ce-e0800ffc2317 · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.

A Survey of Continual Reinforcement Learning A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.443848Z

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=2026-05-19T08:27:03.376909Z digest=sha256:13ab708c47d6738d31c95f694ff036536549d2fe82541011b46292d0ee52482a

Observation 1a703971-a976-4bd5-9a91-3183a9e62fb2 · outbound

This paper cites Improved prediction of protein-protein interactions using AlphaFold2.

A Survey of Continual Reinforcement Learning Improved prediction of protein-protein interactions using AlphaFold2

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.439976Z

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=2026-05-19T08:27:03.376909Z digest=sha256:a264a664d1635a6dc2e30c1ad6afdbd7035d5b057eb7dc3c592788097e4444ae

Observation 0a3884d8-166e-41e2-81ab-7e0e354fc3b2 · outbound

This paper cites Learning high-accuracy error decoding for quantum processors.

A Survey of Continual Reinforcement Learning Learning high-accuracy error decoding for quantum processors

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.436340Z

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=2026-05-19T08:27:03.376909Z digest=sha256:af02db65a9637824128d3d3882f71df5719d5f59a54372d7171c5f656d6fe928

Observation c979f30f-04a4-446b-a926-4f0b575cd962 · outbound

This paper cites Training language models to follow instructions with human feedback.

A Survey of Continual Reinforcement Learning Training language models to follow instructions with human feedback

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.430350Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2e1439d3aa5400941f8f60e2910b5507a09519d0a0460d232237f38437215008

Observation 859afd83-0bbf-4300-869f-cfc4b186ff60 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

A Survey of Continual Reinforcement Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.207615Z

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=2026-05-19T08:27:03.376909Z digest=sha256:546ee84261218b521a424581a3c535f7060a39dc8afae2d125cc61a1dbeabaa3

Observation b5ca8f71-1075-4796-bac6-09c8befb8c90 · outbound

This paper cites Deepther- mal: Combustion optimization for thermal power generating units using offline reinforcement learning.

A Survey of Continual Reinforcement Learning Deepther- mal: Combustion optimization for thermal power generating units using offline reinforcement learning

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.426347Z

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=2026-05-19T08:27:03.376909Z digest=sha256:6ea3a3b9edb9d3859e1296f77317573c52833292d5f0b2499bf39e0ebaf99180

Observation 1cacd800-3716-4f5b-8895-678a8211897d · outbound

This paper cites Magnetic control of tokamak plasmas through deep reinforcement learning.

A Survey of Continual Reinforcement Learning Magnetic control of tokamak plasmas through deep reinforcement learning

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.237995Z

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=2026-05-19T08:27:03.376909Z digest=sha256:0da280dedc3ef77e5fc6580ef14fd3736b8ad609d05e676d39920be2e9d67205

Observation a8835f6b-7ae6-4705-aa3c-56f3ebdcd1c6 · outbound

This paper cites Dense reinforcement learning for safety validation of autonomous vehicles.

A Survey of Continual Reinforcement Learning Dense reinforcement learning for safety validation of autonomous vehicles

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.046499Z

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=2026-05-19T08:27:03.376909Z digest=sha256:bfc879d25bca0480eeb6c345c1b19e79bc1f972a618e9b8ec45e1ec7cc4b14b6

Observation 206c745d-f98f-4a76-8f4c-371382266358 · outbound

This paper cites Grandmaster level in StarCraft II using multi-agent reinforcement learning.

A Survey of Continual Reinforcement Learning Grandmaster level in StarCraft II using multi-agent reinforcement learning

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.080902Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e54dc00c6d7ce61ab458556a04e3ca34e7b70b93719776a5d6af86b07f4defcb

Observation 280239a3-c4c6-4640-b83f-1ec173301a41 · outbound

This paper cites Towards sample efficient reinforcement learning.

A Survey of Continual Reinforcement Learning Towards sample efficient reinforcement learning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.863549Z

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=2026-05-19T08:27:03.376909Z digest=sha256:5dd524b835defc2274dabc3996545b59027e00fd878354d461318fd456939d6c

Observation ab61ff0e-9777-4df5-a632-40f83bbe1815 · outbound

This paper cites Ding and H.

A Survey of Continual Reinforcement Learning Ding and H

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.004858Z

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=2026-05-19T08:27:03.376909Z digest=sha256:88aa1a9f7a4b63dfc39f76fa156d9d8b9469cdece78f6db056c3e10a27af9dd9

Observation b7545c45-2671-410a-8b5b-ce74d6ea4e5d · outbound

This paper cites Challenges of real-world reinforcement learning: definitions, benchmarks and analysis.

A Survey of Continual Reinforcement Learning Challenges of real-world reinforcement learning: definitions, benchmarks and analysis

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.805347Z

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=2026-05-19T08:27:03.376909Z digest=sha256:3ecc5bf6b967122234f9d92a728ac6b6de3768299563e18ba3c5198a19b17efa

Observation 6d1a35f1-f732-46d8-916a-ee5841acdec8 · outbound

This paper cites Biological underpinnings for lifelong learning machines.

A Survey of Continual Reinforcement Learning Biological underpinnings for lifelong learning machines

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.041850Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4ac809dbc8385538d411078af98fc9c450a360f8aca16dc5397036015a34632a

Observation 31e9954b-12b6-4795-bedc-2132e5022cbe · outbound

This paper cites Continual lifelong learning with neural networks: A review.

A Survey of Continual Reinforcement Learning Continual lifelong learning with neural networks: A review

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.017728Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2a3ce68442fd9f8006dd261aab5394b793496b35e66660b810eed7383b4b37e1

Observation 7e16880e-0531-4610-8cf2-350e38c0b5a9 · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks.

A Survey of Continual Reinforcement Learning A continual learning survey: Defying forgetting in classification tasks

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.774606Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e997757decf6d4b10307dc6a826dfe813369b0ef001363414ab4af3809199963

Observation 2b1357f1-c02a-47dc-84c2-408b6c4c1fc8 · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application.

A Survey of Continual Reinforcement Learning A comprehensive survey of continual learning: Theory, method and application

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.189475Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4feefc7bd54946fee4ddc086272662330ed0746b77d4c7e7a29e63cc54cc4ff1

Observation 95c9d345-2f1d-4c35-b1d5-cdf07f31624e · outbound

This paper cites Federated continual learning via knowledge fusion: A survey.

A Survey of Continual Reinforcement Learning Federated continual learning via knowledge fusion: A survey

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.373331Z

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=2026-05-19T08:27:03.376909Z digest=sha256:f584cf12bc621a6e5e8d202461336af351a871f5fd705cd1f4751b9c998aabb6

Observation 40a4d10c-71b1-4a6a-b752-1e7d63822726 · outbound

This paper cites CHILD: A first step towards continual learning.

A Survey of Continual Reinforcement Learning CHILD: A first step towards continual learning

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.832538Z

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=2026-05-19T08:27:03.376909Z digest=sha256:1a45f4e8c0423d455e404f8903bc5d769aded890e0d7c49dd6ad80707790b560

Observation 2d837f75-7687-492f-b5aa-138e00975d5d · outbound

This paper cites Towards continual reinforcement learning: A review and perspectives.

A Survey of Continual Reinforcement Learning Towards continual reinforcement learning: A review and perspectives

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.628921Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2d79240949ff632e6232fc9fe9f1ea056babeff96186c574669d5b535ce7a92e

Observation 08d46c16-7cee-4883-a305-bd0c05b87d66 · outbound

This paper cites Fast TRAC: A parameter-free optimizer for lifelong reinforcement learning.

A Survey of Continual Reinforcement Learning Fast TRAC: A parameter-free optimizer for lifelong reinforcement learning

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.965198Z

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=2026-05-19T08:27:03.376909Z digest=sha256:a75ae2929b8d1bc40ae7276936e67f8bb095cac2c866562ee391ee526765f120

Observation 9fe53a8b-fa4d-4b2c-acd9-f3c34d3c4d01 · outbound

This paper cites A comprehensive survey of forgetting in deep learning beyond continual learning.

A Survey of Continual Reinforcement Learning A comprehensive survey of forgetting in deep learning beyond continual learning

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.140951Z

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=2026-05-19T08:27:03.376909Z digest=sha256:9051d467677fe22de4b8b7f1a9745f7dc0b6ac93b9468ebb8601d2718f1ea3aa

Observation 484f22d7-9290-4000-8245-3e71bd78e1a4 · outbound

This paper cites Markov decision processes.

A Survey of Continual Reinforcement Learning Markov decision processes

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.405697Z

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=2026-05-19T08:27:03.376909Z digest=sha256:d320c4f9582a07b03ff09705ce48691ebec74a21a85017905330e72ce0c50506

Observation 8e3bc458-7486-4c8b-842f-a0a2f3d45f40 · outbound

This paper cites an unresolved cited work.

A Survey of Continual Reinforcement Learning Unresolved cited work

Reference 25

Resolution
unresolved
raw_fallback, observed 2026-05-19T08:27:11.740074Z

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=2026-05-19T08:27:03.376909Z digest=sha256:d53e5b666c3da6b67d6f2f33906374f3c87b9acf3df7478594ef03e47b437dd9

Observation 4abe9bc8-35b3-454e-893a-6b468a84c1d5 · outbound

This paper cites Prioritized experience replay.

A Survey of Continual Reinforcement Learning Prioritized experience replay

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.076735Z

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=2026-05-19T08:27:03.376909Z digest=sha256:20b7c9c2f15659a7e576bd92a5f270450f35141fd401c22d764b699862b6e9a2

Observation 2b2dc532-3797-4124-8304-bfdb11072062 · outbound

This paper cites Dueling network architectures for deep reinforcement learning.

A Survey of Continual Reinforcement Learning Dueling network architectures for deep reinforcement learning

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.072661Z

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=2026-05-19T08:27:03.376909Z digest=sha256:3e143987493e2c5d1434cd2c140e805d95d19d6e748a16ad29f8a54e61ca4331

Observation 155d3c30-45e2-48f8-b761-92ba9be4a797 · outbound

This paper cites Deep recurrent Q-Learning for partially observable mdps.

A Survey of Continual Reinforcement Learning Deep recurrent Q-Learning for partially observable mdps

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.910398Z

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=2026-05-19T08:27:03.376909Z digest=sha256:3b5a2f2a45477e58c07f2e843bcc3652b0d9bbe72b3df419768ee98e263e8592

Observation 0a865d94-e831-4b8e-b43f-81305b19c246 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

A Survey of Continual Reinforcement Learning Asynchronous methods for deep reinforcement learning

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.184912Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2ef4712bf80e59e2a109e3ee6931a4d1c0338397cc17cb2e5d7038e929cb4f5a

Observation ba3c09b4-617a-44ee-9a2b-b59e5347c484 · outbound

This paper cites Continuous control with deep reinforce- ment learning.

A Survey of Continual Reinforcement Learning Continuous control with deep reinforce- ment learning

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.819433Z

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=2026-05-19T08:27:03.376909Z digest=sha256:601b90b3b572404462e6331186276010f8f95d0b113e256b61f04157e0399664

Observation f1161da1-f9e5-4cfd-9b11-9bd95126cc35 · outbound

This paper cites Addressing function ap- proximation error in actor-critic methods.

A Survey of Continual Reinforcement Learning Addressing function ap- proximation error in actor-critic methods

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.729214Z

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=2026-05-19T08:27:03.376909Z digest=sha256:8ebc8d0fc18170efa5250d1279ef24ce89bc38751fc6592e98480308dadea194

Observation 85692d8d-42f1-4d4d-8326-5658bb22746f · outbound

This paper cites Trust region policy optimization.

A Survey of Continual Reinforcement Learning Trust region policy optimization

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.711997Z

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=2026-05-19T08:27:03.376909Z digest=sha256:97d5dbc0aad0df98b626626c63d9412b82365c2bceb52e48ec814dbb145e353e

Observation dc52effd-16ad-4450-93bd-04d7d315908b · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Survey of Continual Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.179467Z

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=2026-05-19T08:27:03.376909Z digest=sha256:eeb9e31cc08eee78b2502a04225e1953fcf08e681e9860c337a69a95b0459112

Observation 6084fd90-87f7-4980-8fff-c7c5bb1ffb6e · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

A Survey of Continual Reinforcement Learning Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.991637Z

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=2026-05-19T08:27:03.376909Z digest=sha256:cbbdb7bfd61b083fcdb5e830d10a73c0ea35a3580496a602af6a236358541ded

Observation 717ba5ac-84e0-40fe-ac83-c6518315f0e7 · outbound

This paper cites A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning.

A Survey of Continual Reinforcement Learning A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.401957Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4497b149332afbd93c7d8af681b122c63f0e61f9e24b1e1144db7982d18c81a2

Observation 0c7961cc-3f26-469f-83ad-4ed780f19ef6 · outbound

This paper cites Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges.

A Survey of Continual Reinforcement Learning Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.956405Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2a798e5b7a3a078c14dfd2c6fda016d948ee11a4cc6f9f703e1ab43818b0d92e

Observation 01461a72-d0a8-40c8-b145-7fbbe9de06a6 · outbound

This paper cites Continual variational autoencoder via continual generative knowledge distillation.

A Survey of Continual Reinforcement Learning Continual variational autoencoder via continual generative knowledge distillation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.116126Z

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=2026-05-19T08:27:03.376909Z digest=sha256:b39e66e5f07c835423a09b15b1f9d69e2387b63e74574ac9b804bbad1abb5d1d

Observation 814a68dd-9aa1-46ff-bb13-21661b254b63 · outbound

This paper cites iCaRL: Incremental classifier and representation learning.

A Survey of Continual Reinforcement Learning iCaRL: Incremental classifier and representation learning

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.171662Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4b240364f6a54ab1bec022c7b4a585ba96854dcd07637b78e196eadd351c7ff6

Observation bdff437e-3417-408b-8a67-a659810f3018 · outbound

This paper cites Continual learning with deep generative replay.

A Survey of Continual Reinforcement Learning Continual learning with deep generative replay

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.721486Z

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=2026-05-19T08:27:03.376909Z digest=sha256:a91b787270a75edfcaf1aa35c25e26ae1ab5411239b11da3b385b0aa28c517b4

Observation 0ae8e263-5e4e-4666-aa1c-7ca597e36698 · outbound

This paper cites Class-incremental learning via deep model consolida- tion.

A Survey of Continual Reinforcement Learning Class-incremental learning via deep model consolida- tion

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.102519Z

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=2026-05-19T08:27:03.376909Z digest=sha256:61b80f110e3278cf2be9dec8210388fe947d61dbee3a12fa8e215e3d2faf6553

Observation e437c4b7-df05-4f04-a258-b3706b0868c2 · outbound

This paper cites Differential privacy preservation in robust continual learning.

A Survey of Continual Reinforcement Learning Differential privacy preservation in robust continual learning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.843767Z

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=2026-05-19T08:27:03.376909Z digest=sha256:58e7e37c1aeb75cb3551e55b99f17f9703da4ffd3dcc26a3b9553138e7a30cdc

Observation 766a0077-67bd-4f60-b2d5-bcbaa054e9e0 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

A Survey of Continual Reinforcement Learning Overcoming catastrophic forgetting in neural networks

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.206444Z

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=2026-05-19T08:27:03.376909Z digest=sha256:652e13ea6762ed481b39db6b3272f1eca17c152ab444db52c68f4768a8c4aebd

Observation c6ea9064-a1e4-41e9-9830-9d266c186e7d · outbound

This paper cites Continual learning via inter-task synaptic mapping.

A Survey of Continual Reinforcement Learning Continual learning via inter-task synaptic mapping

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.210606Z

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=2026-05-19T08:27:03.376909Z digest=sha256:489e1660c8436d23af6819e87da308ab7b6a927c8eaf46a9bb44169c9ad10aac

Observation df6a1fc9-53bb-4fb3-aa0e-55a41832d4d3 · outbound

This paper cites Learning without forgetting.

A Survey of Continual Reinforcement Learning Learning without forgetting

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.681615Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e30c69d02cca2b925087293192f14bbfa37b8c5f8fbc3b45206aa853238afe67

Observation eaad7dcf-3b91-4f68-96da-c2d49ee0e448 · outbound

This paper cites Class-incremental learning by knowl- edge distillation with adaptive feature consolidation.

A Survey of Continual Reinforcement Learning Class-incremental learning by knowl- edge distillation with adaptive feature consolidation

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.247226Z

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=2026-05-19T08:27:03.376909Z digest=sha256:90cc9478186fcafd1a2edec216cd2d6c2ed097bee76106530a8a5caaa65326d7

Observation a60c4aad-e340-4cec-bb05-6ecf79b68333 · outbound

This paper cites PackNet: Adding multiple tasks to a single network by iterative pruning.

A Survey of Continual Reinforcement Learning PackNet: Adding multiple tasks to a single network by iterative pruning

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.241824Z

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=2026-05-19T08:27:03.376909Z digest=sha256:badb0d42eb23a18b7b786e80ffab328fb8c373eb4ccad4a665bb5530a59f0c21

Observation f99e7528-3d36-4728-b8cc-bfebf65a2031 · outbound

This paper cites Piggyback: Adapting a single network to multiple tasks by learning to mask weights.

A Survey of Continual Reinforcement Learning Piggyback: Adapting a single network to multiple tasks by learning to mask weights

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.757431Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2b65152b40dc7e0eb6b2d3cef689903fa3b7330e0b89b3c5db6148a16a003154

Observation 626a6b33-eadc-4be7-8691-d3edcd1b0aa8 · outbound

This paper cites Lifelong generative modelling using dynamic expansion graph model.

A Survey of Continual Reinforcement Learning Lifelong generative modelling using dynamic expansion graph model

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.897391Z

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=2026-05-19T08:27:03.376909Z digest=sha256:1bfe645c553ec1f0542215e00e58fae3009d9d4b514d5db8cd7dfc47566db895

Observation 24575a05-0ab4-4036-a0d5-ef3b258183ef · outbound

This paper cites Few-shot incremental learning with continually evolved classifiers.

A Survey of Continual Reinforcement Learning Few-shot incremental learning with continually evolved classifiers

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.744031Z

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=2026-05-19T08:27:03.376909Z digest=sha256:7e7eb16f62c6e66707d2161da6e22c6cf5eb6744558df59a820e68b8a2090872

Observation 515b7a97-b40b-46c2-9620-14d3b4c05486 · outbound

This paper cites Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines.

A Survey of Continual Reinforcement Learning Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.172394Z

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=2026-05-19T08:27:03.376909Z digest=sha256:9be1c7d7757194801c0e67e35b80e205c6027bb8be81cdc6c5c7a3f46008f3ae

Observation 9e81b28d-15c5-49f6-a6cc-ebe4dbb9798a · outbound

This paper cites Three types of incremental learning.

A Survey of Continual Reinforcement Learning Three types of incremental learning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.973937Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4cc92ef79d2e9cd424eb56731fa37d212f2b6d98a0649e128d80ccca7efda147

Observation b707d3f7-1f83-4d44-8d73-22d6add971db · outbound

This paper cites A definition of continual reinforcement learning.

A Survey of Continual Reinforcement Learning A definition of continual reinforcement learning

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.413783Z

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=2026-05-19T08:27:03.376909Z digest=sha256:8203f2e13573ea289a0df4c3b72922035b4fb196207fe3f38947c50d9ea578ef

Observation 0cff2393-36f5-419e-860b-6b5b10059ce6 · outbound

This paper cites Loss of plasticity in continual deep reinforcement learning.

A Survey of Continual Reinforcement Learning Loss of plasticity in continual deep reinforcement learning

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.055108Z

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=2026-05-19T08:27:03.376909Z digest=sha256:ad6876727248da5e3db9c547446cdc945562cb4b63e0e9ca17a7393052fbd718

Observation 99681c59-4e18-43b7-8853-ff4b38474a13 · outbound

This paper cites A survey of multi-task deep reinforcement learning.

A Survey of Continual Reinforcement Learning A survey of multi-task deep reinforcement learning

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.193570Z

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=2026-05-19T08:27:03.376909Z digest=sha256:d5f624332ac4a7b0e143a19e24312ebc72180879aa667e543e2a2478ec8b2d73

Observation e80d4a7c-178e-4c6c-a776-530f5724bd8c · outbound

This paper cites Transfer learning in deep reinforcement learning: A survey.

A Survey of Continual Reinforcement Learning Transfer learning in deep reinforcement learning: A survey

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.068456Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e8481fd90c0cdca2572b25696d52f2499d5226ce49183b67a6fb7fc208d9bd03

Observation 70ddc7db-ee9e-4555-b5f7-83e858eb2d5f · outbound

This paper cites Continual reinforcement learning with complex synapses.

A Survey of Continual Reinforcement Learning Continual reinforcement learning with complex synapses

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.022265Z

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=2026-05-19T08:27:03.376909Z digest=sha256:db727d537fadac84358d37bc6a5bc9cee60e29e5e4c9e58d50f290df066f9ae3

Observation eb5cad15-b9fe-40ba-ab37-2241bb6b4caa · outbound

This paper cites Loss of plasticity in deep continual learning.

A Survey of Continual Reinforcement Learning Loss of plasticity in deep continual learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.270042Z

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=2026-05-19T08:27:03.376909Z digest=sha256:57521d3c16b093183d1f9090b37d68829b498fae93c75f1a51fb6e53831f6726

Observation d1afbb7e-de5f-4f15-9c70-999c49d8b9fc · outbound

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

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

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.132613Z

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=2026-05-19T08:27:03.376909Z digest=sha256:75e9940a4a0c55703a791424331ee31ed56ddb885794088ddc2d25806719e89a

Observation 1a47ce9d-d194-4dfa-ac0a-f630c79b98d5 · outbound

This paper cites A study of plasticity loss in on-policy deep reinforcement learning.

A Survey of Continual Reinforcement Learning A study of plasticity loss in on-policy deep reinforcement learning

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.084992Z

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=2026-05-19T08:27:03.376909Z digest=sha256:dc72ff8de8d07340c59c64a5d5335d48a9253e622a1c4882654d40d964deaff2

Observation 5cf69aa2-3cac-4005-894c-24cdd69c6010 · outbound

This paper cites Contin- ual world: A robotic benchmark for continual reinforcementlearning.

A Survey of Continual Reinforcement Learning Contin- ual world: A robotic benchmark for continual reinforcementlearning

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.341334Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e74208700bc3c895fd0016a4419ebeadf772b6d472e420c405afe5ff8f74df67

Observation 2e254c6f-13fc-4e2b-81a1-ee45be498e4f · outbound

This paper cites Disentangling transfer in continual reinforcement learning.

A Survey of Continual Reinforcement Learning Disentangling transfer in continual reinforcement learning

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.381893Z

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=2026-05-19T08:27:03.376909Z digest=sha256:203d329489e7f115a7c8adb3dcac1ea0ebbbb8b074d5e678a917e85dfd776fed

Observation 17f51a78-7825-4d18-8b38-1d90f8d5ebe1 · outbound

This paper cites Self-composing policies for scalable continual reinforcement learning.

A Survey of Continual Reinforcement Learning Self-composing policies for scalable continual reinforcement learning

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.377738Z

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=2026-05-19T08:27:03.376909Z digest=sha256:62d7187322bdd18378b866535d3334bcc8b75bae4a2348dfcc8fda187268bccb

Observation 2478aee0-0810-43ff-ab83-f1e5abae3856 · outbound

This paper cites Continuous coordination as a realistic scenario for lifelong learning.

A Survey of Continual Reinforcement Learning Continuous coordination as a realistic scenario for lifelong learning

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.810086Z

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=2026-05-19T08:27:03.376909Z digest=sha256:ab5c013d4146c84d19a40bb65b3394534fcee26249d2c42587cf79cef8a216fc

Observation 39074b72-6c35-492d-97a5-6bfd955aa841 · outbound

This paper cites L2Explorer: A Lifelong Reinforcement Learning Assessment Environment.

A Survey of Continual Reinforcement Learning L2Explorer: A Lifelong Reinforcement Learning Assessment Environment

Reference 64

Resolution
verified exact
arxiv_id, observed 2026-05-19T08:27:11.165479Z

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=2026-05-19T08:27:03.376909Z digest=sha256:af6bd965b155101efdfacea8fd9b87a0c01faec3b31111b15226f741f1ce69af

Observation 45dafd26-e4db-4958-8372-2652816ed364 · outbound

This paper cites Building a subspace of policies for scalable continual learning.

A Survey of Continual Reinforcement Learning Building a subspace of policies for scalable continual learning

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.345029Z

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=2026-05-19T08:27:03.376909Z digest=sha256:ea743da73f23a7c10613baf6ecf0644e4789f69d43bf407bf0efd1e55ab65394

Observation 18d40ed3-d7ae-40c4-8a07-53d26acecdb8 · outbound

This paper cites Model-based lifelong reinforcement learning with bayesian exploration.

A Survey of Continual Reinforcement Learning Model-based lifelong reinforcement learning with bayesian exploration

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.348927Z

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=2026-05-19T08:27:03.376909Z digest=sha256:14898ac4b417dc589361375eb60bdc61e13c568223c762de07770a2674981bd5

Observation a4c19a8f-7830-4ab5-9aaf-986e40db702c · outbound

This paper cites Continual reinforcement learning in 3D non-stationary environments.

A Survey of Continual Reinforcement Learning Continual reinforcement learning in 3D non-stationary environments

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.353185Z

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=2026-05-19T08:27:03.376909Z digest=sha256:d8a6051c4b8195c0fbc01f3cc3a397d1e78592d310706ceaea2d10c18ca4730a

Observation 97c30c7c-a467-4e55-8725-f7eeaca1f00e · outbound

This paper cites CORA: Benchmarks, baselines, and metrics as a platform for continual rein- forcement learning agents.

A Survey of Continual Reinforcement Learning CORA: Benchmarks, baselines, and metrics as a platform for continual rein- forcement learning agents

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.389860Z

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=2026-05-19T08:27:03.376909Z digest=sha256:39d107c48b5d473f9831c09da07029949ece1c499c21c7d0d8fe1c30e43ec86c

Observation de4c8c94-5768-449a-8295-2d110be8c0ea · outbound

This paper cites COOM: A game benchmark for continual reinforcement learning.

A Survey of Continual Reinforcement Learning COOM: A game benchmark for continual reinforcement learning

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.315650Z

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=2026-05-19T08:27:03.376909Z digest=sha256:5c51a39eb31d800334f03e0923a14cedd57070e25bf96097388e1796474a85b9

Observation a17bc714-3f58-46bc-bca6-50698560b051 · outbound

This paper cites Policy and value transfer in lifelong reinforcement learning.

A Survey of Continual Reinforcement Learning Policy and value transfer in lifelong reinforcement learning

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.331084Z

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=2026-05-19T08:27:03.376909Z digest=sha256:bebcb6f9b43ea561f8a415634b550a07b03f67893d67caf8680c5db6071882f1

Observation 020c2df7-8e4d-4379-adba-e0239c9af79c · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning envi- ronments for goal-oriented tasks.

A Survey of Continual Reinforcement Learning Minigrid & miniworld: Modular & customizable reinforcement learning envi- ronments for goal-oriented tasks

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.328324Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2530590048ab984709b109ff8a378d462936c89b1556b5e2b1ec988cbac4b2a7

Observation dbb71f58-8c2a-4b0e-825c-ebf1b0115f54 · outbound

This paper cites DeepMind Lab.

A Survey of Continual Reinforcement Learning DeepMind Lab

Reference 73

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.193393Z

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=2026-05-19T08:27:03.376909Z digest=sha256:60ce6394e93771c51aa67cbb4a46b841f7cca75c79f99990eed9939960441611

Observation 02f6d114-3c64-4a32-881c-1c44015ba22e · outbound

This paper cites Progress & compress: A scalable framework for continual learning.

A Survey of Continual Reinforcement Learning Progress & compress: A scalable framework for continual learning

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.297774Z

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=2026-05-19T08:27:03.376909Z digest=sha256:5a45e5b4e1121b7475b79c03c91810bb1c50e2e4ac3550c54ff63151f968d627

Observation ddd202c7-ae37-4a30-8447-3b99b380b73b · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

A Survey of Continual Reinforcement Learning Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 75

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.186560Z

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=2026-05-19T08:27:03.376909Z digest=sha256:359d10f1ef571d9f8de8122b13bb47f81779f4eccfc3e1163e121752c9243836

Observation 35081155-68e4-431e-8434-5153db5b9898 · outbound

This paper cites Same state, different task: Continual reinforcement learning without interference.

A Survey of Continual Reinforcement Learning Same state, different task: Continual reinforcement learning without interference

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.311291Z

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=2026-05-19T08:27:03.376909Z digest=sha256:5ed0214caa4773774c8cbd565a50de621259dfc6695c3418d2c79ec9dc823cf9

Observation b64a08aa-db33-4884-adeb-73882401fdfc · outbound

This paper cites MuJoCo: A physics engine for model-based control.

A Survey of Continual Reinforcement Learning MuJoCo: A physics engine for model-based control

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.333984Z

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=2026-05-19T08:27:03.376909Z digest=sha256:3608d0b22a95cf2db46801ad336a2600c0b0c96684c764d90f22b831c5699bcf

Observation 82198ff5-e320-4e48-9dda-8a13420048ec · outbound

This paper cites Policy consolidation for continual reinforcement learning.

A Survey of Continual Reinforcement Learning Policy consolidation for continual reinforcement learning

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.369300Z

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=2026-05-19T08:27:03.376909Z digest=sha256:491088b963ff1139e9e4c73a2eaa96a0052a220acc0b9d625fc73120cd985ae8

Observation a6ff9d46-9a2a-48c3-8a76-9e83157444c9 · outbound

This paper cites IMPALA: scalable distributed deep-RL with impor- tance weighted actor-learner architectures.

A Survey of Continual Reinforcement Learning IMPALA: scalable distributed deep-RL with impor- tance weighted actor-learner architectures

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.265828Z

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=2026-05-19T08:27:03.376909Z digest=sha256:f56a553706f8c2d53e355f28206c429cb523aa8159beb5c632feb520b63d298e

Observation a428e50b-c3e8-4acb-92b8-66aad0e30a1f · outbound

This paper cites Prediction and control in continual rein- forcement learning.

A Survey of Continual Reinforcement Learning Prediction and control in continual rein- forcement learning

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.279403Z

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=2026-05-19T08:27:03.376909Z digest=sha256:6a8ea1f9e71a1048174edff36cfde67ce7acad87cbd38e7d6d2d44d5018a0b89

Observation 868c6bfb-980a-43e9-b82e-3640d785e62e · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

A Survey of Continual Reinforcement Learning The arcade learning environment: An evaluation platform for general agents

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.947778Z

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=2026-05-19T08:27:03.376909Z digest=sha256:2e75565eb6a9294ef6e0cfcdc938189e3142df6a355054ecee28b139788f7892

Observation 9423d14f-91ec-4f24-b28c-de36e6d8bc10 · outbound

This paper cites Progressive Neural Networks.

A Survey of Continual Reinforcement Learning Progressive Neural Networks

Reference 82

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.158048Z

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=2026-05-19T08:27:03.376909Z digest=sha256:e71296af24712015a61ff8c946f7f6657bb6ea6ba7657ea857d9d899e8bb33ae

Observation 2ce40946-0517-48f3-80c3-2264f19c5da5 · outbound

This paper cites StarCraft II: A New Challenge for Reinforcement Learning.

A Survey of Continual Reinforcement Learning StarCraft II: A New Challenge for Reinforcement Learning

Reference 83

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.200227Z

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=2026-05-19T08:27:03.376909Z digest=sha256:aa7098543c63a86799f4512a2301576b47849dab1e7289a4db6cee082c75254f

Observation 32a6c183-0629-404a-8029-233133c45b14 · outbound

This paper cites Lifelong reinforcement learning with temporal logic formulas and reward machines.

A Survey of Continual Reinforcement Learning Lifelong reinforcement learning with temporal logic formulas and reward machines

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.931889Z

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=2026-05-19T08:27:03.376909Z digest=sha256:7dbad5b20bc8ceb195140a2d253e0912c1b715720d7c44062d1d75f3f077ba98

Observation 33925282-0e5a-4b8a-a1fa-820a11ee2696 · outbound

This paper cites Reset-free lifelong learning with skill-space planning.

A Survey of Continual Reinforcement Learning Reset-free lifelong learning with skill-space planning

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.202370Z

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=2026-05-19T08:27:03.376909Z digest=sha256:9e253b4e63e11cb83083f6442e0469ee8c2fed953c68feca9d4b8db656a4a284

Observation 55b3df8d-281a-490a-8296-16a4f7b4fa18 · outbound

This paper cites Model-free generative replay for lifelong reinforcement learning: Application to starcraft-2.

A Survey of Continual Reinforcement Learning Model-free generative replay for lifelong reinforcement learning: Application to starcraft-2

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.120628Z

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=2026-05-19T08:27:03.376909Z digest=sha256:6152fbb5d23d06ef75c193511559c1d0467b231f194197f47f30f8a90aaa1ac3

Observation c0e337d2-531b-49d5-a644-76028358c161 · outbound

This paper cites Lifelong federated reinforcement learn- ing: A learning architecture for navigation in cloud robotic systems.

A Survey of Continual Reinforcement Learning Lifelong federated reinforcement learn- ing: A learning architecture for navigation in cloud robotic systems

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.026730Z

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=2026-05-19T08:27:03.376909Z digest=sha256:ad302cab8c17cf73380a1971497369fff1cf9080234de3ea144c7f2d8afb7220

Observation 118af6e2-62a8-4ad1-8ba3-f645e0ae9516 · outbound

This paper cites Discorl: Continual reinforcement learning via policy distillation.

A Survey of Continual Reinforcement Learning Discorl: Continual reinforcement learning via policy distillation

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.421526Z

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=2026-05-19T08:27:03.376909Z digest=sha256:d06efab137edca3e287feb0fbbebb94937cf9c3894eda574843c0cef93fd76e6

Observation 44082620-c4a0-499c-8455-dd0329c42bff · outbound

This paper cites Continual vision-based reinforcement learning with group symme- tries.

A Survey of Continual Reinforcement Learning Continual vision-based reinforcement learning with group symme- tries

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.227470Z

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=2026-05-19T08:27:03.376909Z digest=sha256:f03c047a15aa75e4b3fe729a93f79925b99c9a85eeecd0265dcfdae9fbac1527

Observation 303b791b-35c3-49d4-a128-4311420e6a65 · outbound

This paper cites Evaluating continual learning on a home robot.

A Survey of Continual Reinforcement Learning Evaluating continual learning on a home robot

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.288214Z

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=2026-05-19T08:27:03.376909Z digest=sha256:abdc99c70a7b5a20ccb829a323a27e33e8d50e877b4d657633a7894a184c39df

Observation 495f9316-a600-41bd-a01c-b05782289478 · outbound

This paper cites The Hanabi challenge: A new frontier for AI research.

A Survey of Continual Reinforcement Learning The Hanabi challenge: A new frontier for AI research

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.987401Z

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=2026-05-19T08:27:03.376909Z digest=sha256:3d617ab1f0fffdecb3c32f813c8ac6512b7b869542d3f39c54a275498e5d1e81

Observation fa2a6d7c-5931-4e10-9d8e-dd7bf093dac1 · outbound

This paper cites Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning.

A Survey of Continual Reinforcement Learning Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.283887Z

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=2026-05-19T08:27:03.376909Z digest=sha256:5944691b89d0f45bc4cff22defc87a061ded0f24f603023c065962e811d23126

Observation 91bde47c-78d3-4ab4-b208-4c170d3656f9 · outbound

This paper cites Using task features for zero-shot knowledge transfer in lifelong learning.

A Survey of Continual Reinforcement Learning Using task features for zero-shot knowledge transfer in lifelong learning

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.038028Z

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=2026-05-19T08:27:03.376909Z digest=sha256:613d64f600852491012147bc2db3d08427e2bbae6c1d9e346dfd527c5c0bc884

Observation d0a384fc-ec91-4545-b10c-2a197faad6b8 · outbound

This paper cites A deep hierarchical approach to lifelong learning in minecraft.

A Survey of Continual Reinforcement Learning A deep hierarchical approach to lifelong learning in minecraft

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.951834Z

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=2026-05-19T08:27:03.376909Z digest=sha256:93b327259dbdd6efa0e002b772ca0b0ef59032d3adc2769665232ffee97b37cd

Observation 3ca6ef8f-5e1c-41b5-8d48-ceb974194ed7 · outbound

This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

A Survey of Continual Reinforcement Learning PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 95

Resolution
verified exact
local_arxiv, observed 2026-05-19T08:27:11.138798Z

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=2026-05-19T08:27:03.376909Z digest=sha256:b7d3d26eedce38ca97f6d20f2500feedc0bc6c9ca32c7699552f31f08f61ba4d

Observation ee9ec092-6db5-444f-825a-8c8360b50916 · outbound

This paper cites Selective experience replay for lifelong learning.

A Survey of Continual Reinforcement Learning Selective experience replay for lifelong learning

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.393970Z

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=2026-05-19T08:27:03.376909Z digest=sha256:a78e73a3f54196419066784faceb58f6646ed4bcbba279ff3667e249e6d95194

Observation 430f585f-d5e0-4884-bf77-89ed2dde9e5f · outbound

This paper cites State abstractions for lifelong reinforcement learning.

A Survey of Continual Reinforcement Learning State abstractions for lifelong reinforcement learning

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:12.417607Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4841c925c944879e1d26fda514e77c62025c092be52316456c4d458bff4b850b

Observation 01c0ebe3-3e3e-4411-8bc0-08c0b04746f1 · outbound

This paper cites Composing value functions in reinforcement learning.

A Survey of Continual Reinforcement Learning Composing value functions in reinforcement learning

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.854198Z

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=2026-05-19T08:27:03.376909Z digest=sha256:0219816098df83d762d410d27b83e34dea230e2f5ee235a5cddbe8d5022d30c9

Observation c2f767ba-2b53-4386-b31b-71d48db83b35 · outbound

This paper cites Experience replay for continual learning.

A Survey of Continual Reinforcement Learning Experience replay for continual learning

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.618533Z

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=2026-05-19T08:27:03.376909Z digest=sha256:bedea9518d79473be934211af911b7a6740c91fc76ba7784bf0d6d04ebe45660

Observation 41155558-dce9-4e25-b1b8-49382d35d32e · outbound

This paper cites Model primitives for hierarchical lifelong reinforcement learning.

A Survey of Continual Reinforcement Learning Model primitives for hierarchical lifelong reinforcement learning

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.960618Z

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=2026-05-19T08:27:03.376909Z digest=sha256:40f70bb58fa4098f19d4638967f99fa2fd04df1fc1d59541ea5c343fb8e2863c

Observation aa54b45f-78e4-4864-91f0-d07ac192f820 · outbound

This paper cites Deep reinforcement learning amidst lifelong non-stationarity.

A Survey of Continual Reinforcement Learning Deep reinforcement learning amidst lifelong non-stationarity

Reference 101

Resolution
verified fuzzy
raw_fallback, observed 2026-05-19T08:27:11.639980Z

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=2026-05-19T08:27:03.376909Z digest=sha256:4ab8d060b9c428db78251b611753568b36bfd81d489e623ee4039bf64c098441

Pith citing papers

Observation 94af6eae-201b-430b-b6fe-6754917d7091 · inbound

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning cites this paper.

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning A Survey of Continual Reinforcement Learning

Reference 139

Resolution
unresolved
no resolver link, observed 2026-08-05T20:31:47.521445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:31:47.521445Z digest=sha256:57f93c386a8a074ae0a8e2eacd8329bfe48daa347e4494c4208e7a6c18a544b8

Observation 2412ae16-5e9a-40b5-b564-7a85281ef93e · inbound

An LLM-Driven Closed-Loop Autonomous Learning Framework for Robots Facing Uncovered Tasks in Open Environments cites this paper.

An LLM-Driven Closed-Loop Autonomous Learning Framework for Robots Facing Uncovered Tasks in Open Environments A Survey of Continual Reinforcement Learning

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-05-11T19:36:14.650855Z

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=2026-05-08T11:27:34.992950Z digest=sha256:1bbc78e674ddbb31cb4eed8457d3062c510d8264c37cc5061cd91de01d3d219b

Observation 47743675-6bcd-41d1-8d14-00ae3593e0dd · inbound

Regime-Adaptive Continual Learning for Portfolio Management cites this paper.

Regime-Adaptive Continual Learning for Portfolio Management A Survey of Continual Reinforcement Learning

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-06-28T20:32:37.261070Z

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=2026-06-28T20:30:20.156082Z digest=sha256:959f1a5a2af97026aeae3a87a54360dbcbfc64f2f607ec27052a4d5a84c27138

Observation da285bb5-9a5b-4138-8aa5-fcc1d3441142 · inbound

Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions cites this paper.

Local Guidance, Global Impact: Gaussian-Reshaped Trust Region Unlocks Behavior Transitions A Survey of Continual Reinforcement Learning

Reference 21

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T02:36:26.590082Z

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=2026-06-28T10:51:30.546134Z digest=sha256:a4d611cfe13268b5a72551189a236c4b39bd2063e045eda0cfc62a2a50500c7c

Observation c20592d6-2b33-455d-a84e-ee351c363d1c · inbound

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models cites this paper.

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models A Survey of Continual Reinforcement Learning

Reference 60

Resolution
unresolved
no resolver link, observed 2026-07-12T15:01:54.826833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T15:01:54.826833Z digest=sha256:6d67cac38db600e32e2d7190026c0e9874ec73af2203bca57d99c5bc92ec4a04

Observation ef9c21df-094f-4543-b88d-c1505b9bcc70 · inbound

Emergent Language as an Approach to Conscious AI cites this paper.

Emergent Language as an Approach to Conscious AI A Survey of Continual Reinforcement Learning

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-07-02T13:16:58.586054Z

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=2026-06-28T01:28:54.111496Z digest=sha256:64a2f2bd58d64c7ff5e2ef99f5604cc44c1a30d3775b70a943dd75abb0fe921a

Observation 533b93a4-5150-45aa-9c08-05701e58881d · inbound

Continual Quadruped Robots Coordination via Semantic Skill Discovery cites this paper.

Continual Quadruped Robots Coordination via Semantic Skill Discovery A Survey of Continual Reinforcement Learning

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-07-02T21:37:25.181442Z

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=2026-06-27T19:37:53.168569Z digest=sha256:3b96aafa53cf023ae23c4da43121af1ea62a7e71152d7e3dcb9a6ae1b2873d57

Observation 21dd0c6d-fa7e-4830-b873-f87c5e74bbea · inbound

Offline Multi-agent Continual Cooperation via Skill Partition and Reuse cites this paper.

Offline Multi-agent Continual Cooperation via Skill Partition and Reuse A Survey of Continual Reinforcement Learning

Reference 18

Resolution
metadata mismatch
local_arxiv, observed 2026-07-04T19:30:07.940887Z

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=2026-06-25T21:14:31.778575Z digest=sha256:e9ca02a14ce6f13b3f750b15dfedeb10253cc266664afc4ed67aed12a7d0e2a6

Observation eabe5058-98a9-4b26-9e80-3762ad6ec7b1 · 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 A Survey of Continual Reinforcement Learning

Reference 31

Resolution
unresolved
no resolver link, observed 2026-07-31T18:18:00.298402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T18:18:00.298402Z digest=sha256:828709ed20a5f6f04b8ec1876c32e58f8140c7112b19b14e876d60f915ee9a3b

Observation fca4c27e-653f-412c-b5c1-84667ab4b2ca · inbound

PATH-Bench: Path-Dependent Evaluation of Lifelong Agents cites this paper.

PATH-Bench: Path-Dependent Evaluation of Lifelong Agents A Survey of Continual Reinforcement Learning

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T00:31:37.297766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:31:37.297766Z digest=sha256:0214fdae5109bf98e64d4585d225dd2cf14becc525cf3be0e0204dc5dda33d6c

Observation 3c78565f-e685-4545-bc56-87ba44375a07 · inbound

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing cites this paper.

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing A Survey of Continual Reinforcement Learning

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-10T14:00:57.517509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:00:57.517509Z digest=sha256:1964476c1378e745f5d0c534ba4a58a753788ae7a52115b2f317e9896303a49a