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Paper Citation Record · LEDGER

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.18004.

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

pith.paper-citation-record.v1
2607.18004 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:26:18.026231Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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  • verified fuzzy0
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89cdaf55-842f-4e91-999e-2cf9b6da2057 · outbound

This paper cites Nature , volume=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Nature , volume=

Reference 1

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source=arxiv_source observed=2026-08-01T16:26:17.899984Z digest=sha256:3b2705401d133e16365efb1dea401a378eb721592bc6eb3b421489c44c185853

Observation 53fdcc43-ba04-4194-90ce-2938ab39b771 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-01T16:26:17.905905Z digest=sha256:a8ce763415db7e9eac868dbad0e4100c473f7511902197627e3dc569b192a590

Observation 92ec1a19-7a38-4938-ab56-a2ebcd8e7b17 · outbound

This paper cites Auto-Encoding Variational Bayes.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Auto-Encoding Variational Bayes

Reference 3

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source=arxiv_source observed=2026-08-01T16:26:17.910076Z digest=sha256:a92e6d41c42aea001fafeaa6e7e91617595cf65a820ad5ace2f391d1a6e48992

Observation d47916ce-04fc-42f9-9002-1a74b90bec4e · outbound

This paper cites World Models.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning World Models

Reference 4

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source=arxiv_source observed=2026-08-01T16:26:17.914397Z digest=sha256:2c8d28b998e251c3f9c1dde123a6d2233c037c3832512c756ae44aa9f2af0b52

Observation 7c716b6b-6b3b-496f-8a8e-371ddfe73742 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Representation Learning with Contrastive Predictive Coding

Reference 5

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source=arxiv_source observed=2026-08-01T16:26:17.918955Z digest=sha256:4260b86c8195bc3f3e04bee7047804ed365e4fd354b80c16e6b62ef7919d84b9

Observation 7d23ba4b-9dce-4671-8e2f-3c063b7a8d15 · outbound

This paper cites Conference on robot learning (pp.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Conference on robot learning (pp

Reference 6

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source=arxiv_source observed=2026-08-01T16:26:17.923104Z digest=sha256:e837d72e1fc4ccff89e4302172e13627b428eb3a4e4762bc8347344955c3a3f9

Observation 4d04b6a7-d0f5-4fa5-856b-b97c85eee392 · outbound

This paper cites Proximal Policy Optimization Algorithms.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 7

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source=arxiv_source observed=2026-08-01T16:26:17.928065Z digest=sha256:ea660cd0d3db0375ae77baf51920be87c3f8b40c4bcc2dfde0642d2ca8bd7181

Observation f917e09f-fc34-4c14-9c47-f3b2c7ccc033 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-01T16:26:17.932576Z digest=sha256:00649fcacc22bfff9342743663d5123a9b8d4cca69edaaf4d7e82eff46bacbf5

Observation e5a44dfc-4cff-46b2-8fd2-f4066707a724 · outbound

This paper cites Scalable.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Scalable

Reference 9

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source=arxiv_source observed=2026-08-01T16:26:17.936424Z digest=sha256:d1670d14e1d6e5547571964124bce97204877a5fad14dd3157a7da08e04316be

Observation ed3a79f8-abf8-4d42-aac6-81bfaa57ed45 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 10

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source=arxiv_source observed=2026-08-01T16:26:17.940899Z digest=sha256:68c556fc97abba7978710c2519462bd688019236d345440d0d54d19817af0ffa

Observation 8d071f52-143e-47f3-915c-460f5c310d36 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-01T16:26:17.944744Z digest=sha256:535a8d7a05928a71a1ae93a6f7b7b6aa3c028dada589379a4ef886a6005aaffa

Observation c6df20f4-ad7c-41b5-a516-0aa1852b5f9c · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-01T16:26:17.948441Z digest=sha256:344eec4fdc12b38d465c23e4a400b189afdd0c8abf12a05c4101f659829acd1a

Observation 6d72236a-272d-4fcb-9680-2ecb3f45674d · outbound

This paper cites Mastering Diverse Domains through World Models.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Mastering Diverse Domains through World Models

Reference 13

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source=arxiv_source observed=2026-08-01T16:26:17.952148Z digest=sha256:8db64b71c0ed1b362b57ca2107f6a98a29569043ffd97d796d012dd146df6f7d

Observation c47536ad-d400-4540-b82a-cb11a388130e · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 14

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source=arxiv_source observed=2026-08-01T16:26:17.956351Z digest=sha256:89b8b74825e75f088f220221278f25bf61b94f08e0c16881610205146ee3069d

Observation 83bb7020-4dfe-41a7-945b-6d21af52c29a · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-01T16:26:17.960509Z digest=sha256:df25bf41874b808ba4504c7695faf2e7d6905ea9b07261163557e97057431c46

Observation b3a18fbf-78e6-49f4-a2f1-ebcb1b4f1ffd · outbound

This paper cites Data-Efficient Reinforcement Learning with Self-Predictive Representations.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Data-Efficient Reinforcement Learning with Self-Predictive Representations

Reference 16

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source=arxiv_source observed=2026-08-01T16:26:17.964188Z digest=sha256:8da1e4b0b239abd52cb0091c3ce515f9d5b55e88acfc1be84e665a96abe3a2a2

Observation fa7bee20-451f-490d-8f38-7e39a2851e8e · outbound

This paper cites Metrics for.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Metrics for

Reference 17

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source=arxiv_source observed=2026-08-01T16:26:17.968414Z digest=sha256:24b5ac06fedc1ac65f52edf494521a36b1f597412b7da2a0b742e1a3c47e2003

Observation 2b0b3e15-db14-46f9-8f2b-2040930f99e2 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 18

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source=arxiv_source observed=2026-08-01T16:26:17.971975Z digest=sha256:2c4ee4c97bf684215d13cb9929cc9a1591f3279550dde6706eb688af1a243c9e

Observation e831e539-39a9-42cb-906f-b6b64f683df3 · outbound

This paper cites SIAM Journal on Computing , volume=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning SIAM Journal on Computing , volume=

Reference 19

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source=arxiv_source observed=2026-08-01T16:26:17.975632Z digest=sha256:219d41617ce0dac4d0fbfa74ae4391211cc748f992123fd749b222ea75f1c1e6

Observation 410ea4cd-6c0c-485d-ba96-d8365fad3fef · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-01T16:26:17.979254Z digest=sha256:c4b92bd89022cef503c7b14404d304cb2d862d5ca00a5aa317e859b170659222

Observation 44297c8e-b120-4d91-bb2c-68a6c00f179a · outbound

This paper cites 2021 , primaryClass =.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning 2021 , primaryClass =

Reference 21

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source=arxiv_source observed=2026-08-01T16:26:17.982970Z digest=sha256:f72264eea3d3c5746af532035b70729b8135652ffb6dec6bfe2e051f74c8a54f

Observation 5ff93fbf-b5ed-47df-a947-7d015e43d0e2 · outbound

This paper cites A Moving Discontinuous Galerkin Finite Element Method with Interface Conservation Enforcement for Compressible Flows.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning A Moving Discontinuous Galerkin Finite Element Method with Interface Conservation Enforcement for Compressible Flows

Reference 22

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source=arxiv_source observed=2026-08-01T16:26:17.987014Z digest=sha256:a91440d8c2eca63ac6b0fdddbe29f1f634bd14def15c2a1ca28c9b4708d5592c

Observation 0c4131bb-2652-41ca-8551-759a7a98e40f · outbound

This paper cites IEEE Signal Processing Magazine , volume=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning IEEE Signal Processing Magazine , volume=

Reference 23

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source=arxiv_source observed=2026-08-01T16:26:17.991010Z digest=sha256:40ed8ab7b29f85271f30304617277a3306f74b4d05831a8a094a8b4f94a87c94

Observation 1d366a9c-240a-450a-9fcc-aa3b7aaad0b7 · outbound

This paper cites Journal of Machine Learning Research , volume=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Journal of Machine Learning Research , volume=

Reference 24

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source=arxiv_source observed=2026-08-01T16:26:17.994894Z digest=sha256:b1d9e4ac6941ee370256091cabe02492681528032a9fe04eada5d450eeaea91d

Observation d10b306a-46cc-43cb-9e87-ff11ecc61dff · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-01T16:26:17.998537Z digest=sha256:06c4264b6ad066eecf017d83ba3bda0dbd2b606f3a50465d0539abab24bb0570

Observation 663e6ec5-55d3-4db9-8427-bced213c9aae · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-01T16:26:18.002757Z digest=sha256:8e7bc1b730db27f5f79b59676544e33c0af3384c96c91673dc077f24424c9ade

Observation c82c686b-b2c3-4bf6-9829-fbc96128cf2b · outbound

This paper cites Kingma and Jimmy Ba , booktitle =.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Kingma and Jimmy Ba , booktitle =

Reference 27

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source=arxiv_source observed=2026-08-01T16:26:18.006811Z digest=sha256:5c960538140d95eee77c01fd2be9c6c494022e35aad8338ad60f785953b523b9

Observation 5eac8ac7-c300-4028-a145-8393605e26ce · outbound

This paper cites International conference on machine learning , pages=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning International conference on machine learning , pages=

Reference 28

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source=arxiv_source observed=2026-08-01T16:26:18.010616Z digest=sha256:6f078023eeae1c6966c7bb5669bb7bfa3d5026a3d028bcb07244c2dc0cbd0ae8

Observation 08a0b578-c0d8-4ba9-aecf-4872689c12f9 · outbound

This paper cites Proceedings of the 37th International Conference on Machine Learning , pages=.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Proceedings of the 37th International Conference on Machine Learning , pages=

Reference 29

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source=arxiv_source observed=2026-08-01T16:26:18.014507Z digest=sha256:2d124c21b3142b23cc2b2f8b80861b7e169780f89339c9259fa1033b3f865c8b

Observation 872f1706-b34c-4521-8751-99e8081fa486 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-01T16:26:18.018505Z digest=sha256:ed9974a248e42e3311d19841ea1f64096a391c0115e9968f8fd07a566fddf03b

Observation 59066cbf-8cab-4d72-a018-19c865860367 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 31

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source=arxiv_source observed=2026-08-01T16:26:18.022512Z digest=sha256:e34a13c0a3e5b829d4958d88c08a72e4535c38ba8058325e517fb2168914c05c

Observation 0676fda1-ce51-49ff-9c95-97f434747db0 · outbound

This paper cites an unresolved cited work.

PAMD: Structured Adaptive Distances for Bisimulation Representations in Visual Reinforcement Learning Unresolved cited work

Reference 32

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source=arxiv_source observed=2026-08-01T16:26:18.026231Z digest=sha256:a53ee64e7c7905a75793da2fdbdad1ef7597366e984f22bbbb24f4fa6f288446

Pith citing papers

No inbound Pith citation observations are available.