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Paper Citation Record · LEDGER

Dynamic Reinforcement Learning for Actors

As of 10 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 2026-08-10T06:31:04.303077+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
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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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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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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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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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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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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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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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 2026-08-10T06:31:04.303077+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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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

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 15

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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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Observation ccef354b-ffe4-41ca-990e-653835134b3e · outbound

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Dynamic Reinforcement Learning for Actors Unresolved cited work

Reference 20

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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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 2026-08-10T06:31:04.303077+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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Observation 47709292-6a33-488f-a45b-f0f35bd9f59c · outbound

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Dynamic Reinforcement Learning for Actors Creativity

Reference 26

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

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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

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Dynamic Reinforcement Learning for Actors Kurzweil

Reference 30

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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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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 2026-08-10T06:31:04.303077+00:00.

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

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Dynamic Reinforcement Learning for Actors McCulloch and W

Reference 34

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+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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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=sha256:92d1681e58005533dfb111239d05e5f91370f7a6569d4fab578d122c098b91b4

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T19:07:25.319587Z digest=sha256:31bf96dbd5dda04bc82371f5aabcea2692e9d122d2a6ab7f512cbef28218530f

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.323985Z digest=sha256:f464ee57ba8e522c3a3e369a0d3ab5c15a24c35fce5607cedc5fd9bb34d4651c

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.328142Z digest=sha256:a038a780356788672af02bbdac8d391deed5b0170ca3ab420ca066e91e23717e

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.331524Z digest=sha256:af44beb2af6a0bfdb4f0b28447222c766d88180ea231d2193358b003ee8c2b24

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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unresolved
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=sha256:269dbfefc31611949150d65b1bad03df3e23914d2d8a63408f537c4875d907bf

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=sha256:8f87c029c9f5ada43daf95705081a2e7e10a2652d84664b3c92ed52241c27a81

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.343037Z digest=sha256:2971c500a9870eb1bfe126fa9cacb85e7de5d2131ca74e6ec44be64cbb78f969

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.346949Z digest=sha256:7cf9f7a0efae7eac709e6821d4fa5cdab9353b7f89902c6180a14c815e159cd2

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.352097Z digest=sha256:cc3643c0d436bede1a1f73bda8d704ed6e1017823f87a1d0253bcc31ceaf1411

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.356240Z digest=sha256:c22a467cc953c34a0440faed8efbf334fc7dd64654bc1a0e6f5819722030b9fa

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:c41120aa543e7b7931f78e5609100b7db5702de30985c5eca6c13617000e5eec

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:ff39488deaca71cd4cd88ddcf484c50e9fdc5e005e0651218c27d608f50d4d4e

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.369666Z digest=sha256:87b6ff9465dff7720adac979a55ebfe115f82368d9542217c5627761b20d7171

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.374248Z digest=sha256:0b099aa1a8d7c9f1c1e4afb4878a7db2b9f2202e5123de2a44292d80bc5d4d2b

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.377514Z digest=sha256:9e41065c51d22540f2fd6a725641c2f7b9154c2f2df469162c0e50ed8885bde3

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

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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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.380938Z digest=sha256:c2e9b6cec35670642b4a14350ed25f5e9e28ddd7e72deb337ca247a2930e1633

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.385087Z digest=sha256:341c79f6dce5a340de66eb0097afc06fad9ba4ede8c7dd7bd9d198da8ebe0b6a

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.388480Z digest=sha256:287b3647f72ee953b807205dffb51de3d00a01cc6c347dece471217235c323a3

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.391950Z digest=sha256:c444a3312fc33abc309eb35a7a7be8bdbeeb27919e6cc8910e1e0b92ae4160e1

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.395959Z digest=sha256:fe51b5be2af8cab44dd62f5a5135e56e116736529c9192602489ceb9af18c12b

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.399998Z digest=sha256:53fe5d1f5859bdc6e6927483a4df91ca6193550990c7ede50effb38c583a4e5b

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.405265Z digest=sha256:42fd1f1342ba6ee4bd5f9e805f0da5332a402c3ec35694e0073d25e32ad38032

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=sha256:73a47f52f0ca3f9bfc08a4bb5c7e8cb3b7764310f084cec818ec0f2fb0f24e22

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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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=sha256:1a2acb5e02450278fce92d33f0d5c09fa486e42ae2408b91805398e0a174aa0d

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.421085Z digest=sha256:8833c784ac24aeb14f6716c6733c713b4afb56b2af9338688f5eb3ef4beb8407

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=sha256:273e83bcb05767fd467bfa15e8d11c85a81fab966bf4f1700812e4d7692f62c5

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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unresolved
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=sha256:063ef319f2b880c588c6435743495211b51bb5f80c1fa05b246fddf263f445c5

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=sha256:2d75770247f59ce721803d1b18ebb6d8af16b055c16cffbef6815549d7878391

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.437982Z digest=sha256:6dba8f073756c878401601d4141989280dabcba70220fb727390b99cadff812b

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

This paper cites Yamashita and J.

Dynamic Reinforcement Learning for Actors Yamashita and J

Reference 67

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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=sha256:7e238bd601c8eebf6cb95466e4d7abcc6a81c07df44babe7138323bcd3c723f8

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-07T19:07:25.446718Z digest=sha256:123548bcec320f1c383d406d8866e3214a195eecfcffe427c09ef2979c74646b

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

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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:cda44ff7efc73324316033ae4b1936b6b46ec2e9f40be93bb0eb3062a1f6d149

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T06:36:56.956656Z digest=sha256:08d1e623b6bed9a9981c65409f1e7cb875991efc9cd9b4b9b751e093ac9829b5