Paper Citation Record · LEDGER

Continual Reinforcement Learning by Planning with Online World Models

As of 21 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 3 inbound Pith citation observations for arXiv:2507.09177.

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

pith.paper-citation-record.v1
2507.09177 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:10:19.203780Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:24:48.899301Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:36:26.615794Z

Reference resolution

61 of 61 outbound references displayed

verified exact0
verified fuzzy49
unresolved12
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bcef066c-562c-4fe1-ba23-6bce07c6075a · outbound

This paper cites write newline.

Continual Reinforcement Learning by Planning with Online World Models write newline

Reference 1

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Source-reported events for the cited work

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Observation a1428363-22f9-41ee-83ee-3a81a56f7c4b · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:11:19.973952Z

Source-reported events for the cited work

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Observation 3bef8d88-881f-4838-90c0-b3b628cda6a7 · outbound

This paper cites P., and Singh, S.

Continual Reinforcement Learning by Planning with Online World Models P., and Singh, S

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.808082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 222e613d-4b87-4f7a-b59f-0a43e2adcbf6 · outbound

This paper cites Gradient based sample selection for online continual learning.

Continual Reinforcement Learning by Planning with Online World Models Gradient based sample selection for online continual learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.663322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f52bf769-aa2d-482c-a0bd-033c5c328056 · outbound

This paper cites Selfless sequential learning.

Continual Reinforcement Learning by Planning with Online World Models Selfless sequential learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.517144Z

Source-reported events for the cited work

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Observation b403af44-f0e8-4aba-9ef5-e21207d7242d · outbound

This paper cites G., Naddaf, Y., Veness, J., and Bowling, M.

Continual Reinforcement Learning by Planning with Online World Models G., Naddaf, Y., Veness, J., and Bowling, M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.420054Z

Source-reported events for the cited work

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Observation 7654f282-6daa-4a29-91e2-68d2b5fbcb0a · outbound

This paper cites A markovian decision process.

Continual Reinforcement Learning by Planning with Online World Models A markovian decision process

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.223912Z

Source-reported events for the cited work

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Observation a01e71f1-fe0c-45d4-b2da-790ffd11e411 · outbound

This paper cites Class-incremental continual learning into the extended der-verse.

Continual Reinforcement Learning by Planning with Online World Models Class-incremental continual learning into the extended der-verse

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:19.107268Z

Source-reported events for the cited work

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Observation 24baf1fd-cd45-4741-a602-7956ec43e93a · outbound

This paper cites Efficient lifelong learning with A-GEM.

Continual Reinforcement Learning by Planning with Online World Models Efficient lifelong learning with A-GEM

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.941144Z

Source-reported events for the cited work

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Observation b9e7680b-8b1b-4a3e-80a5-e88a5cc8ec24 · outbound

This paper cites Continual learning with tiny episodic memories.

Continual Reinforcement Learning by Planning with Online World Models Continual learning with tiny episodic memories

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.833051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 03b9ab8f-27f1-427e-8697-3abb3aa88aa7 · outbound

This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models.

Continual Reinforcement Learning by Planning with Online World Models Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Reference 11

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Source-reported events for the cited work

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Observation a86586b8-8ace-452e-a33f-ca8228b5ea28 · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

Continual Reinforcement Learning by Planning with Online World Models Leveraging procedural generation to benchmark reinforcement learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.739423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8488fd5-6762-45da-8876-c217ef5c9211 · outbound

This paper cites P., Mannor, S., and Rubinstein, R.

Continual Reinforcement Learning by Planning with Online World Models P., Mannor, S., and Rubinstein, R

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.641746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bba66180-0264-4d0a-853b-d3b57dd244d4 · outbound

This paper cites Orthogonal gradient descent for continual learning.

Continual Reinforcement Learning by Planning with Online World Models Orthogonal gradient descent for continual learning

Reference 14

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Source-reported events for the cited work

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Observation 4806d2a2-973d-4563-b189-dec4109d1ea3 · outbound

This paper cites E., Prett, D.

Continual Reinforcement Learning by Planning with Online World Models E., Prett, D

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.440369Z

Source-reported events for the cited work

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Observation 735f65e6-ddd4-446b-8ada-4a9df7c2d61e · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:11:18.295080Z

Source-reported events for the cited work

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Observation 87b97f19-ff11-477c-bdb8-802b38c5f9bc · outbound

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

Continual Reinforcement Learning by Planning with Online World Models Building a subspace of policies for scalable continual learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.221087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7dd5b270-61f1-4a4e-ac3b-cbff40db3651 · outbound

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

Continual Reinforcement Learning by Planning with Online World Models Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:18.077729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c32fd81-cccc-402b-a755-74a42ac606f3 · outbound

This paper cites K., et al.

Continual Reinforcement Learning by Planning with Online World Models K., et al

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.934115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7432bbca-23d6-4717-845d-783972b524e0 · outbound

This paper cites Continual model-based reinforcement learning with hypernetworks.

Continual Reinforcement Learning by Planning with Online World Models Continual model-based reinforcement learning with hypernetworks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.739091Z

Source-reported events for the cited work

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Observation 1231d891-ef7e-40fc-ba0f-6b350745ef2d · outbound

This paper cites A theory of universal artificial intelligence based on algorithmic complexity.

Continual Reinforcement Learning by Planning with Online World Models A theory of universal artificial intelligence based on algorithmic complexity

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.626556Z

Source-reported events for the cited work

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Observation d0b8e610-57df-4736-9afd-ba62473f840c · outbound

This paper cites and Cosgun, A.

Continual Reinforcement Learning by Planning with Online World Models and Cosgun, A

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.518041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9d65a93-6dde-4dc3-9a1a-b8862b131f64 · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 23

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Observation fb3d6e27-a890-42f3-8b73-d412c66b0039 · outbound

This paper cites R., Hwang, S.

Continual Reinforcement Learning by Planning with Online World Models R., Hwang, S

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.252092Z

Source-reported events for the cited work

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Observation af2c2f29-c797-4ed1-8bf8-0865b9f59b3d · outbound

This paper cites J., Zohren, S., and Roberts, S.

Continual Reinforcement Learning by Planning with Online World Models J., Zohren, S., and Roberts, S

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:17.162487Z

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Observation aa13dbb3-1635-41ec-a7bc-caeb50cfb20b · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 26

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Observation 76988404-6cc3-4711-bfd3-8f78e750082a · outbound

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

Continual Reinforcement Learning by Planning with Online World Models Towards continual reinforcement learning: A review and perspectives

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.999266Z

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Observation 7d2667ea-0b44-435a-8f5e-51689b3a4949 · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 28

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Observation 87b20be5-795c-4899-8e7d-7adad73193e9 · outbound

This paper cites A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.

Continual Reinforcement Learning by Planning with Online World Models A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.906941Z

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Observation dd1b8742-3c0b-4850-ac32-dc19d36c1a78 · outbound

This paper cites AI2-THOR: An Interactive 3D Environment for Visual AI.

Continual Reinforcement Learning by Planning with Online World Models AI2-THOR: An Interactive 3D Environment for Visual AI

Reference 30

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Source-reported events for the cited work

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Observation edd5aaa1-ab79-4980-92a4-8921934cbc10 · outbound

This paper cites u ttler, H., Nardelli, N., Miller, A., Raileanu, R., Selvatici, M., Grefenstette, E., and Rockt \.

Continual Reinforcement Learning by Planning with Online World Models u ttler, H., Nardelli, N., Miller, A., Raileanu, R., Selvatici, M., Grefenstette, E., and Rockt \

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.801382Z

Source-reported events for the cited work

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Observation e45eaaae-6a69-490a-a0f2-98975964f710 · outbound

This paper cites S., and Lin, M.

Continual Reinforcement Learning by Planning with Online World Models S., and Lin, M

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.658919Z

Source-reported events for the cited work

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Observation 51524395-bfbd-4be1-ae87-baac32b95709 · outbound

This paper cites and Lazebnik, S.

Continual Reinforcement Learning by Planning with Online World Models and Lazebnik, S

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.471864Z

Source-reported events for the cited work

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Observation 9ce9e3f1-959b-482b-a229-0fe5e5776b5a · outbound

This paper cites Deep online learning via meta-learning: Continual adaptation for model-based rl.

Continual Reinforcement Learning by Planning with Online World Models Deep online learning via meta-learning: Continual adaptation for model-based rl

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.353061Z

Source-reported events for the cited work

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Observation d2104027-d851-4333-ba15-e566b568854d · outbound

This paper cites R., De Schutter, B., Wiering, M.

Continual Reinforcement Learning by Planning with Online World Models R., De Schutter, B., Wiering, M

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:16.259082Z

Source-reported events for the cited work

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Observation d0299dec-4fab-402a-b3e2-d175b8d33653 · outbound

This paper cites and Vidal, R.

Continual Reinforcement Learning by Planning with Online World Models and Vidal, R

Reference 36

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Source-reported events for the cited work

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Observation 3884e2c7-1e0c-4e97-b4c0-114dd928cec7 · outbound

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Continual Reinforcement Learning by Planning with Online World Models V., and Vidal, R

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.968542Z

Source-reported events for the cited work

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Observation 477a1953-439a-4b86-8e8d-a78d2bbf0d10 · outbound

This paper cites O., and Calandra, R.

Continual Reinforcement Learning by Planning with Online World Models O., and Calandra, R

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.857791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70a7bfb6-2d6a-4799-8535-c0e3911b44bb · outbound

This paper cites Sample-efficient cross-entropy method for real-time planning.

Continual Reinforcement Learning by Planning with Online World Models Sample-efficient cross-entropy method for real-time planning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.756384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea16f5d9-736c-4f97-8771-f2d6264ad600 · outbound

This paper cites Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents.

Continual Reinforcement Learning by Planning with Online World Models Cora: Benchmarks, baselines, and metrics as a platform for continual reinforcement learning agents

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.616354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4f7d7f4-8338-42c5-a0cb-1f810958a38e · outbound

This paper cites Learning to learn without forgetting by maximizing transfer and minimizing interference.

Continual Reinforcement Learning by Planning with Online World Models Learning to learn without forgetting by maximizing transfer and minimizing interference

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.553428Z

Source-reported events for the cited work

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Observation f232caa8-7826-467d-98a3-01518d114fce · outbound

This paper cites Experience replay for continual learning.

Continual Reinforcement Learning by Planning with Online World Models Experience replay for continual learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.527004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87c44d83-294b-4227-9b40-e659a84e8c8f · outbound

This paper cites The cross-entropy method for combinatorial and continuous optimization.

Continual Reinforcement Learning by Planning with Online World Models The cross-entropy method for combinatorial and continuous optimization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.435368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation adf74431-fe53-4ee6-a69b-d0fa59e045d3 · outbound

This paper cites Curious exploration via structured world models yields zero-shot object manipulation.

Continual Reinforcement Learning by Planning with Online World Models Curious exploration via structured world models yields zero-shot object manipulation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.338379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b726db6-5975-45df-b231-b5696bf2a301 · outbound

This paper cites M., Grabska-Barwinska, A., Teh, Y.

Continual Reinforcement Learning by Planning with Online World Models M., Grabska-Barwinska, A., Teh, Y

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.258193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1cbf20f-c36a-441b-a36b-5366a0c145d2 · outbound

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Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:11:15.123043Z

Source-reported events for the cited work

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Observation 36674e8b-0cdd-4ef2-a85c-3c37a992cdf2 · outbound

This paper cites Autonomous reinforcement learning: Formalism and benchmarking.

Continual Reinforcement Learning by Planning with Online World Models Autonomous reinforcement learning: Formalism and benchmarking

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:15.029920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 187b17b3-fbb3-47e8-b78c-bc6a6075ade2 · outbound

This paper cites Alfred: A benchmark for interpreting grounded instructions for everyday tasks.

Continual Reinforcement Learning by Planning with Online World Models Alfred: A benchmark for interpreting grounded instructions for everyday tasks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.929917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ae1e6d2b-ad26-4669-811a-b8dbcf469a76 · outbound

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Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:11:14.902729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2289b0f7-af4c-40f8-8a28-f763a7cb7df3 · outbound

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

Continual Reinforcement Learning by Planning with Online World Models Mujoco: A physics engine for model-based control

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.838708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2bc98a74-7c0c-4211-bf50-332b3052b698 · outbound

This paper cites an unresolved cited work.

Continual Reinforcement Learning by Planning with Online World Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:11:14.755578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9465a9fc-214e-4397-b9f4-cf242043823e · outbound

This paper cites and Ba, J.

Continual Reinforcement Learning by Planning with Online World Models and Ba, J

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.610854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ba237eb-ed53-4787-ba6c-59981f511b95 · outbound

This paper cites Model Predictive Path Integral Control using Covariance Variable Importance Sampling.

Continual Reinforcement Learning by Planning with Online World Models Model Predictive Path Integral Control using Covariance Variable Importance Sampling

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:18.638329Z

Source-reported events for the cited work

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Observation 238598ba-ef15-4136-8ce9-e21085f140ff · outbound

This paper cites Continual world: A robotic benchmark for continual reinforcement learning.

Continual Reinforcement Learning by Planning with Online World Models Continual world: A robotic benchmark for continual reinforcement learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.518922Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0b98d82-4964-4612-90d2-2583f87170d5 · outbound

This paper cites Continual task allocation in meta-policy network via sparse prompting.

Continual Reinforcement Learning by Planning with Online World Models Continual task allocation in meta-policy network via sparse prompting

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.418368Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 438b62fe-351d-46cf-bf4e-e9796a7295cc · outbound

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

Continual Reinforcement Learning by Planning with Online World Models Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.267972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c36e89c5-84d1-4553-94a0-59853dfb233f · outbound

This paper cites Continual learning through synaptic intelligence.

Continual Reinforcement Learning by Planning with Online World Models Continual learning through synaptic intelligence

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.891697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61f55a7f-cb68-44fa-b2b9-84baa3f27b02 · outbound

This paper cites The Schur complement and its applications, volume 4.

Continual Reinforcement Learning by Planning with Online World Models The Schur complement and its applications, volume 4

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.723179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 29fabc1a-1ff6-4b94-9699-6590b2f89abe · outbound

This paper cites ACIL : Analytic class-incremental learning with absolute memorization and privacy protection.

Continual Reinforcement Learning by Planning with Online World Models ACIL : Analytic class-incremental learning with absolute memorization and privacy protection

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.581119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 572e2b75-52be-4b2c-86cd-ae47ff919a37 · outbound

This paper cites GKEAL : Gaussian kernel embedded analytic learning for few-shot class incremental task.

Continual Reinforcement Learning by Planning with Online World Models GKEAL : Gaussian kernel embedded analytic learning for few-shot class incremental task

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.459214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69969800-dead-4048-a9d5-4e087eb8d5b2 · outbound

This paper cites DS-AL : A dual-stream analytic learning for exemplar-free class-incremental learning.

Continual Reinforcement Learning by Planning with Online World Models DS-AL : A dual-stream analytic learning for exemplar-free class-incremental learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.377619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation f477ed61-5763-4485-a849-ab24ba3c43b7 · inbound

Agentic Reasoning for Large Language Models cites this paper.

Agentic Reasoning for Large Language Models Continual Reinforcement Learning by Planning with Online World Models

Reference 186

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:14:26.172861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 289902f6-1611-4218-b269-0f3be319bdd3 · 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 Continual Reinforcement Learning by Planning with Online World Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:36:26.617162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8bb22d9-ae2e-47c6-847a-11af09512cfd · inbound

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling cites this paper.

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling Continual Reinforcement Learning by Planning with Online World Models

Reference 154

Resolution: unresolved; no resolver link, observed 2026-07-11T19:24:48.899301Z

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