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Paper Citation Record · LEDGER

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2607.18589.

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

pith.paper-citation-record.v1
2607.18589 v1

Coverage vector

measured 43 of 43 reference resolution

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measured 43 of 43 standing notices

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43 of 43 outbound references displayed

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Outbound references

Observation 7175be2c-5a82-4c74-83cd-8182aa26c547 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Relational inductive biases, deep learning, and graph networks

Reference 1

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Observation 21c47137-978d-45ae-b1c3-b57e1e39931c · outbound

This paper cites Interpreting emergent planning in model-free reinforcement learning.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Interpreting emergent planning in model-free reinforcement learning

Reference 2

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Observation ad7b2732-9443-42a4-89f4-fa3688995c6d · outbound

This paper cites Cohen and Howard Eichenbaum.Memory, Amnesia, and the Hippocampal System.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Cohen and Howard Eichenbaum.Memory, Amnesia, and the Hippocampal System

Reference 3

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Observation 88b8a5a6-bec6-4c7b-8845-698c480b8931 · outbound

This paper cites Emergent Response Planning in LLMs.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Emergent Response Planning in LLMs

Reference 4

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Observation eda69215-df16-4d0e-b26e-cb5c3d912bac · outbound

This paper cites On the integration of space, time, and memory.Neuron, 95(5):1007–1018,.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States On the integration of space, time, and memory.Neuron, 95(5):1007–1018,

Reference 5

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Observation 139b592d-c656-4a31-a783-721bdb8889f1 · outbound

This paper cites IM- PALA: Scalable distributed deep-RL with importance weighted actor-learner architectures.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States IM- PALA: Scalable distributed deep-RL with importance weighted actor-learner architectures

Reference 6

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Observation 138a9800-8a70-47c1-85af-933b6add9294 · outbound

This paper cites Hybrid computing using a neural network with dynamic external memory.Nature, 538(7626): 471–476, 2016.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Hybrid computing using a neural network with dynamic external memory.Nature, 538(7626): 471–476, 2016

Reference 7

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Observation f0211f95-3e38-422b-b997-ee3247d0b762 · outbound

This paper cites Highway and Residual Networks learn Unrolled Iterative Estimation.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Highway and Residual Networks learn Unrolled Iterative Estimation

Reference 8

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Observation 65152fbd-010f-41d6-a396-a23689fac086 · outbound

This paper cites An investigation of model-free planning.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States An investigation of model-free planning

Reference 9

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Observation 8a999ab7-8275-4085-83e2-8241eccc7856 · outbound

This paper cites World Models.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States World Models

Reference 10

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Observation 2e6ceb7b-c584-4c50-bc92-d9583185caef · outbound

This paper cites Learning latent dynamics for planning from pixels.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Learning latent dynamics for planning from pixels

Reference 11

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Observation 85994764-c10a-4269-9d1a-861554acd880 · outbound

This paper cites Mastering Diverse Domains through World Models.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Mastering Diverse Domains through World Models

Reference 12

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Observation 18781938-fef4-4540-bbc8-5594c53a825e · outbound

This paper cites Evidence of Learned Look-Ahead in a Chess-Playing Neural Network.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Evidence of Learned Look-Ahead in a Chess-Playing Neural Network

Reference 13

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Observation dc309a59-8dd9-49f8-bf1f-fdb5ddcc6e27 · outbound

This paper cites An empirical exploration of recurrent network architectures.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States An empirical exploration of recurrent network architectures

Reference 14

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Observation a313df4e-2275-461a-ae53-5a001b719130 · outbound

This paper cites Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Reference 15

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Observation 9e2f8787-29d7-45c7-a28e-2339186de769 · outbound

This paper cites Neural rela- tional inference for interacting systems.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Neural rela- tional inference for interacting systems

Reference 16

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Observation 6e5ce3d9-56da-43db-aee4-ec5054d8097e · outbound

This paper cites Actor-critic algorithms.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Actor-critic algorithms

Reference 17

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Observation c44de500-899b-4947-9050-536b1ec00fa1 · outbound

This paper cites an unresolved cited work.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

Reference 18

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Observation 2d2ae8f8-48b3-4f16-976f-e7f402591358 · outbound

This paper cites Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra

Reference 19

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Observation 286f2150-bd29-4b40-b0b9-82e858668d62 · outbound

This paper cites Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg

Reference 20

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Observation 569930b3-89d8-4396-bee1-3f84ae1ac784 · outbound

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Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

Reference 21

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Observation ce5a85a2-29a5-436e-b039-69de08bd295f · outbound

This paper cites Puterman.Markov Decision Processes: Discrete Stochastic Dynamic Programming.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Puterman.Markov Decision Processes: Discrete Stochastic Dynamic Programming

Reference 22

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Observation cd3c98ed-e35a-43e1-b6a5-f08151da5aae · outbound

This paper cites Hopfield Networks is All You Need.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Hopfield Networks is All You Need

Reference 23

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Observation 9edd9547-3fac-45e2-be9d-574496b522ba · outbound

This paper cites Graph networks as learnable physics engines for inference and control.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Graph networks as learnable physics engines for inference and control

Reference 24

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Observation ac27f4c3-5c54-43a9-801a-857e34f1ff64 · outbound

This paper cites Mastering Atari, Go, chess and shogi by planning with a learned model.Nature, 588:604–609, 2020.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Mastering Atari, Go, chess and shogi by planning with a learned model.Nature, 588:604–609, 2020

Reference 25

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Observation 6b0b7c34-eba7-403b-9b54-8390918c4124 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 26

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Observation bf603cd5-7c34-4adb-af4c-6f0ea007a2c7 · outbound

This paper cites Convolutional LSTM network: A machine learning approach for pre- cipitation nowcasting.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Convolutional LSTM network: A machine learning approach for pre- cipitation nowcasting

Reference 27

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Observation 9ebfd173-a3ee-426f-a151-d7e6ecc25506 · outbound

This paper cites Mastering the game of Go without human knowledge.Nature, 550(7676):354–359,.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Mastering the game of Go without human knowledge.Nature, 550(7676):354–359,

Reference 28

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Observation 153f4473-7cae-4ddd-a47c-295c64da6971 · outbound

This paper cites Stachenfeld, Matthew M.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Stachenfeld, Matthew M

Reference 29

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Observation 7426db5f-acd1-4232-b5e2-2e942bee1e69 · outbound

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Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

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Observation 0c6ce0aa-c901-4507-8cae-3d0ea4318df8 · outbound

This paper cites Sutton and Andrew G.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Sutton and Andrew G

Reference 31

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Observation 20b2035b-40d6-4146-b760-48eb02093e87 · outbound

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Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

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Observation 7772a4fc-a757-49ac-bcc4-5dbddaaccfeb · outbound

This paper cites Planning in a recurrent neural network that plays Sokoban,.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Planning in a recurrent neural network that plays Sokoban,

Reference 33

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Observation bd602923-92c8-4cd6-92f7-a79a6cd34349 · outbound

This paper cites Path channels and plan extension kernels: A mechanistic description of planning in a Sokoban RNN.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Path channels and plan extension kernels: A mechanistic description of planning in a Sokoban RNN

Reference 34

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Observation db1077a8-e2d7-49f4-a838-744d92e3954c · outbound

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Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

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Observation 95a08ea6-0310-4a8f-9221-8dfeafe319c4 · outbound

This paper cites Sutton, David McAllester, Satinder Singh, and Yishay Mansour.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Sutton, David McAllester, Satinder Singh, and Yishay Mansour

Reference 36

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Observation b25fdc25-e292-4093-b69e-82f660107eeb · outbound

This paper cites Relational Deep Reinforcement Learning.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Relational Deep Reinforcement Learning

Reference 37

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Observation 501173ea-6396-4676-8443-578983c9e80c · outbound

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Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

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Observation 5eb28650-6644-43d2-b345-51c3ab44724b · outbound

This paper cites URL https://proceedings.mlr.press/v120/lambert20a.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States URL https://proceedings.mlr.press/v120/lambert20a

Reference 770

Resolution
unresolved
no resolver link, observed 2026-08-01T15:02:08.857631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 68313b1b-94db-4319-b0d4-28ef8993c0eb · outbound

This paper cites an unresolved cited work.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

Reference 1948

Resolution
unresolved
no resolver link, observed 2026-08-01T15:02:10.141206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:02:10.141206Z digest=sha256:abec84216dd1a07d7ebf8cdd946f672526f451f9ee7263dfaaa09a8917cfe5f8

Observation e1600f04-ab11-4765-8a3a-72b140fb7e4c · outbound

This paper cites an unresolved cited work.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-01T15:02:08.291294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:02:08.291294Z digest=sha256:dd23b2b3b3611aaf6680c290b4ee8c4f191e0b0003de31ab60739987c317fb0f

Observation 5950a635-ce08-4ff7-b107-71c8205766f9 · outbound

This paper cites an unresolved cited work.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Unresolved cited work

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-01T15:02:07.489269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:02:07.489269Z digest=sha256:35eb460fb526c03ba25bc1ca4a0939dc8c92da5fbf919f0b92f25605393c131d

Observation a902f4d8-9798-4d43-8998-298549eba040 · outbound

This paper cites Planning in a recurrent neural network that plays Sokoban.

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States Planning in a recurrent neural network that plays Sokoban

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-01T15:02:09.968163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:02:09.968163Z digest=sha256:ac64f7d869340c755800e09797b170aef4932be9d442511a7f56361f0f43a9f5

Pith citing papers

No inbound Pith citation observations are available.