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Paper Citation Record · LEDGER

Dynamic Reinforcement Learning for Actors

As of 8 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2502.10200.

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

pith.paper-citation-record.v1
2502.10200 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:07:25.450652Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T06:36:56.956656Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

69 of 69 outbound references displayed

  • verified exact9
  • verified fuzzy26
  • unresolved33
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1ec14da0-79da-4785-90b6-29f5601b08a4 · outbound

This paper cites Can I say, now machines can think?.

Dynamic Reinforcement Learning for Actors Can I say, now machines can think?

Reference 1

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local_arxiv, observed 2026-08-07T19:07:26.397047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 28349bd4-8bc5-4941-8b46-6ebec2dfccc2 · outbound

This paper cites Aihara, T.

Dynamic Reinforcement Learning for Actors Aihara, T

Reference 2

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source=arxiv_source observed=2026-08-07T19:07:25.160579Z digest=sha256:08ed757bff54f0172454ce3f54e73d378ccf15b0c6bea3e4db79e2fd375a9533

Observation 1c0abd69-1693-432d-b8c0-603607caf7f3 · outbound

This paper cites Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective.

Dynamic Reinforcement Learning for Actors Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective

Reference 3

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1c7bb315-e3da-4197-bf53-2ca238a407ad · outbound

This paper cites Andrychowicz, F.

Dynamic Reinforcement Learning for Actors Andrychowicz, F

Reference 4

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 259cb42c-a97a-4417-a27d-fcfc03b285b6 · outbound

This paper cites Azizi and G.

Dynamic Reinforcement Learning for Actors Azizi and G

Reference 5

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Observation 377bd31a-6354-49d7-811a-6b6bbe73a8d4 · outbound

This paper cites Berlyne and W.

Dynamic Reinforcement Learning for Actors Berlyne and W

Reference 6

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c853da98-0f1a-4364-b8fe-df3bc71d3fb0 · outbound

This paper cites Active Divergence with Generative Deep Learning -- A Survey and Taxonomy.

Dynamic Reinforcement Learning for Actors Active Divergence with Generative Deep Learning -- A Survey and Taxonomy

Reference 7

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source=arxiv_source observed=2026-08-07T19:07:25.186334Z digest=sha256:d48bba14b1a3da5768ff21108dbf8fdca488c253b0398e3d37931357f2d0de18

Observation c484f11f-3c07-4f53-bd01-8ebdf8385ffb · outbound

This paper cites Statement on AI risk, 2025 a.

Dynamic Reinforcement Learning for Actors Statement on AI risk, 2025 a

Reference 8

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ead8142e-04d5-40a2-b5b3-3867c44848f2 · outbound

This paper cites An overview of catastrophic AI risks, 2025 b.

Dynamic Reinforcement Learning for Actors An overview of catastrophic AI risks, 2025 b

Reference 9

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c52952a1-8c40-4e3e-a4ef-03244c2af132 · outbound

This paper cites Art or Artifice? Large Language Models and the False Promise of Creativity.

Dynamic Reinforcement Learning for Actors Art or Artifice? Large Language Models and the False Promise of Creativity

Reference 10

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Observation 26a6625f-4522-48ef-b3ec-5a134256a63d · outbound

This paper cites The alternative uses test, 2018.

Dynamic Reinforcement Learning for Actors The alternative uses test, 2018

Reference 11

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4ddcb73b-9a6c-4eb9-8b9a-b7c24c5ae6b5 · outbound

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 12

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9aa920ac-df9a-4ec0-a580-31af5c6ce784 · outbound

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 13

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e6541dd2-6962-439b-b162-da5c76e9c24d · outbound

This paper cites Creative Beam Search: LLM-as-a-Judge For Improving Response Generation.

Dynamic Reinforcement Learning for Actors Creative Beam Search: LLM-as-a-Judge For Improving Response Generation

Reference 14

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Observation 0c6b79a1-bce9-445a-b509-10bc37ffacc5 · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 04004886-f679-42fd-b756-62e850f00150 · outbound

This paper cites Fujimoto, H.

Dynamic Reinforcement Learning for Actors Fujimoto, H

Reference 16

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 95473fc4-335b-4e89-9cc1-bfb984da374f · outbound

This paper cites Research priorities for robust and beneficial artificial intelligence: An open letter, 2015.

Dynamic Reinforcement Learning for Actors Research priorities for robust and beneficial artificial intelligence: An open letter, 2015

Reference 17

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d300d54c-f16b-44d4-9b4f-e2e93dc2e476 · outbound

This paper cites Large Language Models Are Not Strong Abstract Reasoners.

Dynamic Reinforcement Learning for Actors Large Language Models Are Not Strong Abstract Reasoners

Reference 18

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Observation 1a2574c0-1756-4d9d-ae57-7a3cc3da2e9d · outbound

This paper cites Goto and K.

Dynamic Reinforcement Learning for Actors Goto and K

Reference 19

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ccef354b-ffe4-41ca-990e-653835134b3e · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 20

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Observation 1bbccc8e-a87c-4b59-bdf6-0ef6844955aa · outbound

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 21

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Observation ac096356-3161-4daf-8dfa-c9693e4791c9 · outbound

This paper cites Haarnoja, A.

Dynamic Reinforcement Learning for Actors Haarnoja, A

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7dbe6518-1db2-4038-85dc-f67eeb01d6bc · outbound

This paper cites Huang, S.

Dynamic Reinforcement Learning for Actors Huang, S

Reference 23

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Observation 0912888d-f5fa-42a4-a746-fd74fd9a6fba · outbound

This paper cites Creativity in AI: Progresses and Challenges.

Dynamic Reinforcement Learning for Actors Creativity in AI: Progresses and Challenges

Reference 24

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Observation 438312f1-2ed0-43a6-87ba-195065a4c6a8 · outbound

This paper cites BRAINTEASER: Lateral Thinking Puzzles for Large Language Models.

Dynamic Reinforcement Learning for Actors BRAINTEASER: Lateral Thinking Puzzles for Large Language Models

Reference 25

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source=arxiv_source observed=2026-08-07T19:07:25.260570Z digest=sha256:bcb65cfa2fd94d63ba27d5242f9e5930404e7b32c58e119f6a0b66c40a5fafd8

Observation 47709292-6a33-488f-a45b-f0f35bd9f59c · outbound

This paper cites Creativity.

Dynamic Reinforcement Learning for Actors Creativity

Reference 26

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 47f466da-ef4c-4232-baa8-1c231d480aff · outbound

This paper cites Khachaturyan, S.

Dynamic Reinforcement Learning for Actors Khachaturyan, S

Reference 27

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Observation b5361bcc-dd35-4e15-ab15-ab4093a9582c · outbound

This paper cites Koivisto and S.

Dynamic Reinforcement Learning for Actors Koivisto and S

Reference 28

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 29

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Observation e94891ad-91c4-41bc-9e3d-34f99356c730 · outbound

This paper cites Kurzweil.

Dynamic Reinforcement Learning for Actors Kurzweil

Reference 30

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation baa3fa8b-e7ae-4a56-b9f9-4bfb0d0b1347 · outbound

This paper cites AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text.

Dynamic Reinforcement Learning for Actors AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

Reference 31

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Observation 64867c5a-43a9-479a-8ab3-647acc2032cd · outbound

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Dynamic Reinforcement Learning for Actors Matsuki and K

Reference 32

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation dcc27c0c-fdb8-4a56-bff2-43d80cff4c55 · outbound

This paper cites Matsuki, Y.

Dynamic Reinforcement Learning for Actors Matsuki, Y

Reference 33

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d62fe747-6738-4245-bdb7-464459f28e64 · outbound

This paper cites McCulloch and W.

Dynamic Reinforcement Learning for Actors McCulloch and W

Reference 34

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a623cb31-b245-4796-abf5-cd563d500e77 · outbound

This paper cites Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks.

Dynamic Reinforcement Learning for Actors Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks

Reference 35

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Observation 8b7c4dc0-c179-4bb3-b4ed-4e10f2f5aba2 · outbound

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-08-07T19:07:25.311005Z digest=sha256:ae58c3ba19cfaac242c6bdac5a80882d56ee4796803fee9e0c2d83a7ff65f485

Observation c4c17a51-bb00-45b6-ad81-8d7f197e4ec4 · outbound

This paper cites Asynchronous Methods for Deep Reinforcement Learning.

Dynamic Reinforcement Learning for Actors Asynchronous Methods for Deep Reinforcement Learning

Reference 37

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source=arxiv_source observed=2026-08-07T19:07:25.315321Z digest=sha256:11712a234748c3a8cfa182b4c9773a6545aa952da9fe3c652d68bce5a0c48134

Observation 0d1bdc33-c100-4c6d-9621-38acb2cb02f0 · outbound

This paper cites Characterising the Creative Process in Humans and Large Language Models.

Dynamic Reinforcement Learning for Actors Characterising the Creative Process in Humans and Large Language Models

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.319587Z digest=sha256:32d35ab3a8a233c2fd60a4674df4f117faa486893028f771fdb62a71636ba2d5

Observation 2e3cc862-1886-4615-8d71-feb6f9f05d46 · outbound

This paper cites Newell, J.

Dynamic Reinforcement Learning for Actors Newell, J

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.768498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.323985Z digest=sha256:1e64516d9fc1f1f2bacedb0da5c6f0171271fe0914f9365a50b38cc7135e6e21

Observation ba9a4523-4bae-4a4c-995c-0f04e6588a3c · outbound

This paper cites OpenAI Five , 2019.

Dynamic Reinforcement Learning for Actors OpenAI Five , 2019

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.754853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.328142Z digest=sha256:3c8f187901cebc97a8abc228e0054848b5276b76374bf640d2c95234063cffcc

Observation 7d817cc5-7d64-4846-acb4-c42cad585af0 · outbound

This paper cites GPT-4 , 2023.

Dynamic Reinforcement Learning for Actors GPT-4 , 2023

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.742591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.331524Z digest=sha256:a64e7f2a5ed299c623dfd62b78d808fa4e1db364865671073d12b9dc2e8fbcb0

Observation 45d1ec93-e47c-4a30-bd16-4aa64f3ad1b2 · outbound

This paper cites GPT-4 Technical Report.

Dynamic Reinforcement Learning for Actors GPT-4 Technical Report

Reference 42

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no resolver link, observed 2026-08-07T19:07:25.335218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.335218Z digest=sha256:b5ab29a061d5809c95fb2ad919e42d008acef1fb0937a2147a0e089ffd9f361e

Observation b6f3f176-c7af-4d0e-9bfd-c363115b7966 · outbound

This paper cites Is Temperature the Creativity Parameter of Large Language Models?.

Dynamic Reinforcement Learning for Actors Is Temperature the Creativity Parameter of Large Language Models?

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.339154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.339154Z digest=sha256:72dc5e114f4dd0323e978cd99d9a3b609a9f356917f198bd6cc3bc49694485a3

Observation fd14ff61-dea1-47f7-8e25-9b68f6f0f1df · outbound

This paper cites Pichai, D.

Dynamic Reinforcement Learning for Actors Pichai, D

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.731960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.343037Z digest=sha256:c3d49749e5758383ae69cf45367aee3f3f3d3d24d744b51c839a84ed6628b366

Observation bbf03383-89cf-482e-a3d6-80f36187af94 · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:07:26.719324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.346949Z digest=sha256:f50f8616c20bd55e41f4461e9fd50a7ac0e3ce2d8affdfc14e93531ad22f07a8

Observation f5e8aa09-3ccf-4ba8-b3ae-a5403abb21f3 · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:07:26.677348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.352097Z digest=sha256:c4e26d3a9dda3b0f7888d851b059790fa083119c2ca7baed35f686e82dae4bf8

Observation b260205c-741e-4f7d-b7e2-88b0f7dcb6c9 · outbound

This paper cites Sawatsubashi, M.

Dynamic Reinforcement Learning for Actors Sawatsubashi, M

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.587740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.356240Z digest=sha256:5640054dd632e0aaa31575287668d64064c79f0340115cf0d1cf1ac530b50bdd

Observation 0b462b86-7a7d-4100-9c3a-a52baeb23fc6 · outbound

This paper cites Prioritized Experience Replay.

Dynamic Reinforcement Learning for Actors Prioritized Experience Replay

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.360788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.360788Z digest=sha256:6cbfe11eb2d6b528bc6e9c85d454140154e60c3e382b68bb1f80f7aa56dca78d

Observation 5e8545b7-1c80-47e6-b156-7e3e5479e483 · outbound

This paper cites Schrittwieser, I.

Dynamic Reinforcement Learning for Actors Schrittwieser, I

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.365514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.365514Z digest=sha256:0cef2490b6dd04046b6a68898b59d7e84be0e3945f9ef186877dff80c2f96ee3

Observation 7f6d76df-97fd-4a7e-9849-4024a80b709c · outbound

This paper cites Schulman, F.

Dynamic Reinforcement Learning for Actors Schulman, F

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.552584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.369666Z digest=sha256:1e84b3d057ae646f902841e9b9a0537f6b6a4c9be4c9de3093d248e56354da11

Observation 4968dc46-e457-406f-a229-667915bec91f · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:07:26.536810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.374248Z digest=sha256:c277b0650880c77f1ae399d430194075a669e315bfd586b80931b63e27501035

Observation 52e13e57-b625-484f-8339-6315628f90fc · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 52

Resolution
malformed identifier
doi_truncated, observed 2026-08-07T19:07:25.518668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.377514Z digest=sha256:41a807a766aeef49efb5152f67e2bca721b60e72715ff7839eca46596c7042b8

Observation 54b98b14-b07b-4a97-9015-c9bd09a26fa0 · outbound

This paper cites Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network.

Dynamic Reinforcement Learning for Actors Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-07T19:07:25.853708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.380938Z digest=sha256:69ea5094b94e3c772884b5b3dffd14fb7a23ab6510a15f1880d96520b74d1d51

Observation 33424638-049a-4dd4-87a1-231258f290b2 · outbound

This paper cites Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -.

Dynamic Reinforcement Learning for Actors Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-07T19:07:25.836100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.385087Z digest=sha256:5090b778a672930fa1f18bc2efb2e8cb375377d9f0ce5bf07c49c785e99a4b56

Observation 6f2d4dfe-5aba-4b7e-a397-15e3d7adb2ef · outbound

This paper cites Shibata and K.

Dynamic Reinforcement Learning for Actors Shibata and K

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.520135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.388480Z digest=sha256:cdafb799f4a695d475cd9f698f439e7925bee1b498e1ea31ccb353e03c78466b

Observation 44d3b80a-8a8e-4fdd-b614-855afaf68761 · outbound

This paper cites Shibata and Y.

Dynamic Reinforcement Learning for Actors Shibata and Y

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.505885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.391950Z digest=sha256:2af970acec17750a06731cff692f2fedd4404e0c6a5d057ab5ea5185efd3b9bc

Observation 5ec49e9a-0d84-426a-b665-2f8f5000210e · outbound

This paper cites Shibata and Y.

Dynamic Reinforcement Learning for Actors Shibata and Y

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.491095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.395959Z digest=sha256:a554ed93bb679fc1f9c009c62e2c2515911920648cef07e5925591a06e8baab6

Observation 5bb07383-ff04-4a80-947c-f193d8a4c461 · outbound

This paper cites Shibata, T.

Dynamic Reinforcement Learning for Actors Shibata, T

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:07:26.476426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.399998Z digest=sha256:075d00e1ccae7bdfda8dc45b81d782847a5bc7b00ea42f74ac065f7d311781d8

Observation ba4ae6c7-14e9-4da0-b737-dc48e0b74834 · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 59

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:07:26.462242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.405265Z digest=sha256:062f30ddcae76a4329754a73e1252b6eaa71e149a4b320b2f99e76e83e1de3e3

Observation 87713aa5-2dcf-4f16-8bca-6267426a7050 · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.409586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.409586Z digest=sha256:e5466ea39f00c41faa9c6be883b8e0ab9734c9a340e4c05a5abb25aa00b14c9b

Observation eb6d35b8-cef8-4e61-82b7-2004467e4f7f · outbound

This paper cites Evaluating the Factual Consistency of Large Language Models Through News Summarization.

Dynamic Reinforcement Learning for Actors Evaluating the Factual Consistency of Large Language Models Through News Summarization

Reference 61

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unresolved
no resolver link, observed 2026-08-07T19:07:25.414178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.414178Z digest=sha256:d8c8446fa1b951442d10caae3d17d220590041f7b582e45806ecf767ce112c45

Observation cc849c82-3175-4ab0-913b-26b928d99c6c · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:07:26.433177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.421085Z digest=sha256:9ad7054db132860543145a4894ef43c88de3a48709028887848ad9583aa8a062

Observation 86ff78fb-4582-4079-b070-c53680d4c48e · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 63

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unresolved
no resolver link, observed 2026-08-07T19:07:25.425137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.425137Z digest=sha256:c43d5876d7bc51248f6a055965037907e5a6bc3cfb8b1f9f71ede0f87300f380

Observation 9ba79934-6a43-44e3-927a-48126c95bca5 · outbound

This paper cites Attention Is All You Need.

Dynamic Reinforcement Learning for Actors Attention Is All You Need

Reference 64

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no resolver link, observed 2026-08-07T19:07:25.429336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.429336Z digest=sha256:860fddc79d2214220315a0e2e0b771269e52b8ac4946409b0fc23ff4be4d3e58

Observation 1ee40a16-25a4-4651-bd07-51b1109295b4 · outbound

This paper cites Vinyals, I.

Dynamic Reinforcement Learning for Actors Vinyals, I

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.433908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.433908Z digest=sha256:abe1227a27e9551984a5b5a6036e8e62a497dcaecef491b80b4279c9f34f3acd

Observation 218db37f-57fb-4ea6-bc5c-381fbf37392b · outbound

This paper cites an unresolved cited work.

Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 66

Resolution
verified exact
doi, observed 2026-08-07T19:07:25.496759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.437982Z digest=sha256:e0170ac77def02b68ce30ee728199e059cf6141be6fedee6e104cb6e1533e995

Observation 8f1ca0b8-3041-4d76-a353-93973d99a0a4 · outbound

This paper cites Yamashita and J.

Dynamic Reinforcement Learning for Actors Yamashita and J

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.441917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.441917Z digest=sha256:2e19826487a39d9a375730c546f708f9c64895252a7e5515ff6e3f59c0d6564d

Observation ac7a4ff4-5507-4e75-8529-9f068298d984 · outbound

This paper cites Yudkowsky.

Dynamic Reinforcement Learning for Actors Yudkowsky

Reference 68

Resolution
verified exact
raw_fallback, observed 2026-08-07T19:07:25.705668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.446718Z digest=sha256:e56c4d2852285a92902c7c5aa50473f54ffaf4f14c70dbee6fb2ab5a62cfdbda

Observation c005c9ce-927a-41a9-a49f-f7ab3b8cf065 · outbound

This paper cites Assessing and Understanding Creativity in Large Language Models.

Dynamic Reinforcement Learning for Actors Assessing and Understanding Creativity in Large Language Models

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-07T19:07:25.450652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.450652Z digest=sha256:749cf7e31f54d1c26c400a32018d628507f69951ee9577477fc9fd80b0c03f9c

Pith citing papers

Observation 7be5454f-03bf-4c06-9b4d-673946b1e8ed · inbound

Temporally smoothed incremental model-based heuristic dynamic programming for command-filtered cascaded online learning flight control cites this paper.

Temporally smoothed incremental model-based heuristic dynamic programming for command-filtered cascaded online learning flight control Dynamic Reinforcement Learning for Actors

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:37:07.045942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T06:36:56.956656Z digest=sha256:d21b7313a5a8bde62c814dd34f71d443fd527b8bea0da5cd6e3464c8c3b285e9