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Paper Citation Record · LEDGER

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2607.21645.

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

pith.paper-citation-record.v1
2607.21645 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:15:01.013306Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:36:48.715440Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T11:36:51.722949Z

Reference resolution

39 of 39 outbound references displayed

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  • unresolved39
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 13f9d3b8-bf1c-4612-affc-bc612cadf060 · outbound

This paper cites DIAMOND-LoL: Enforcing Lieb-Robinson locality in diffusion world models for long-horizon consistency.OpenReview preprint, 2026.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not DIAMOND-LoL: Enforcing Lieb-Robinson locality in diffusion world models for long-horizon consistency.OpenReview preprint, 2026

Reference 1

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source=pdf_text observed=2026-08-01T12:14:55.660973Z digest=sha256:09127ca8c2781380bee66e65e78ee06ea2e4d46b7e67ab9380a9efe01c94ec42

Observation c7dab323-19b3-4adc-9ff1-5d45b76ac7f5 · outbound

This paper cites an unresolved cited work.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-01T12:14:55.751468Z digest=sha256:2462c4b09f64e1a761b2396102a1d8e33aefd41bdfad1ec94c8043df68441f11

Observation d823b14a-b97d-4d1c-900e-0cf573a3ab62 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Self-supervised learning from images with a joint-embedding predictive architecture

Reference 3

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source=pdf_text observed=2026-08-01T12:14:55.924036Z digest=sha256:b63e2511715c3d0476c62bb41427f7e1efd4cd03137679af07e7b0e80996836b

Observation 1e8b1894-0a82-4f92-bc32-0148e143cc9e · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 4

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source=pdf_text observed=2026-08-01T12:14:55.978703Z digest=sha256:e3800d6f6e76713591050ac548c55d61d34f13cafb89dce3f70ff9d91bb2de19

Observation 3e924c19-dc7f-47b4-bc85-2046000898aa · outbound

This paper cites Springer, Berlin, 2nd edition, 2006.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Springer, Berlin, 2nd edition, 2006

Reference 5

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source=pdf_text observed=2026-08-01T12:14:56.124687Z digest=sha256:88cebb7496701e83d2d1e9666d9cb3948b52d875b22e0634b281684cf3ef0f0e

Observation b6c674e8-cd03-4af3-931d-fd8531acde17 · outbound

This paper cites Revisiting feature prediction for learning visual representations from video.Transactions on Machine Learning Research, 2024.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Revisiting feature prediction for learning visual representations from video.Transactions on Machine Learning Research, 2024

Reference 6

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source=pdf_text observed=2026-08-01T12:14:56.276830Z digest=sha256:4687afe3ed3480970abb552314c6c51b8fff1036e5c23914b549eab756ee2c6d

Observation 4bc5fc81-c0dc-45c7-87ad-6a77a2d7474d · outbound

This paper cites Baron and David A.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Baron and David A

Reference 7

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source=pdf_text observed=2026-08-01T12:14:56.425536Z digest=sha256:de0d395ae536d3428b59e8a95931a0876eb4ca94473afa521e37ca506d68520d

Observation 7790b1b1-a96d-4358-b6f0-5783ebade057 · outbound

This paper cites Antisymmetric RNN: A dynamical system view on recurrent neural networks.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Antisymmetric RNN: A dynamical system view on recurrent neural networks

Reference 8

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source=pdf_text observed=2026-08-01T12:14:56.510445Z digest=sha256:0e31b69e158f8e2beaf994a48c84988c4c6eba577f6d9c62de11ca34fa8a0160

Observation 19d13687-123b-45a6-b513-b65c679b2359 · outbound

This paper cites ATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not ATM: Action-Consistency Transfer Matrix for Diagnosing and Improving Latent World Models

Reference 9

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source=pdf_text observed=2026-08-01T12:14:56.647338Z digest=sha256:6681ee3ea6b2fc966a9384ace86e895a07c6d4917971adfc5dff2e87088f13b8

Observation fa22aff1-cd0d-4e31-a92a-a8535f374366 · outbound

This paper cites DeepMDP: Learning continuous latent space models for representation learning.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not DeepMDP: Learning continuous latent space models for representation learning

Reference 10

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Observation 05b0ed0d-ab46-415e-9891-1b1f02b71146 · outbound

This paper cites World models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not World models

Reference 11

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source=pdf_text observed=2026-08-01T12:14:56.993511Z digest=sha256:c98dc1632b0c13cf851a067cc9526331423522d7285e0ad007b4e3a0f36bcd26

Observation 84d686f5-1713-4668-b215-5eb9dafd4eb4 · outbound

This paper cites Learning latent dynamics for planning from pixels.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Learning latent dynamics for planning from pixels

Reference 12

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source=pdf_text observed=2026-08-01T12:14:57.145650Z digest=sha256:cbe196d73f4c5f563fc0a50978cb9f33a3f9477656c72acd7c73b8dbaa39e1b7

Observation 3ab54721-b19d-4911-846a-38d985988d48 · outbound

This paper cites Mastering atari with discrete world models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Mastering atari with discrete world models

Reference 13

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source=pdf_text observed=2026-08-01T12:14:57.395692Z digest=sha256:03d4a06c89e2daddfd55b67c9509a006d35476d34c8fd2da3bdc030f2d0e137f

Observation 56769ffe-cee6-476c-a608-5c5d17b0ee68 · outbound

This paper cites Mastering Diverse Domains through World Models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Mastering Diverse Domains through World Models

Reference 14

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source=pdf_text observed=2026-08-01T12:14:57.577781Z digest=sha256:58ed4e5fcea3232c625b3cec035f18321e423cf2d2fdcaa12a3d4ee6f0d88457

Observation c95fd775-ccf6-4731-a400-0eadc3bc82c0 · outbound

This paper cites TD-MPC2: Scalable, robust world models for continuous control.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not TD-MPC2: Scalable, robust world models for continuous control

Reference 15

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source=pdf_text observed=2026-08-01T12:14:57.739672Z digest=sha256:5c3cfa4c32b5984c9d2dd75d0b9eb68d94727568fb36b2020294c2eb45351b7d

Observation 51548e07-8452-43a8-a006-f9063df660cc · outbound

This paper cites Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI

Reference 16

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source=pdf_text observed=2026-08-01T12:14:57.944998Z digest=sha256:4b80bbe7b6a5010515112d02d66286d1178e0d315fc1463e99a723f1c98b6791

Observation d1d11eeb-cee6-48b2-8c42-e45b66f1a5d6 · outbound

This paper cites Contrastive learning of structured world models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Contrastive learning of structured world models

Reference 17

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source=pdf_text observed=2026-08-01T12:14:58.120165Z digest=sha256:f37cec10595cb92c00ad4139b41c7a7c7c1864ba8ed6cf17085f0bf4143b3bf8

Observation fe2786ad-5949-43b8-a6f0-9b1716cdff11 · outbound

This paper cites A path towards autonomous machine intelligence.OpenReview, 2022.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not A path towards autonomous machine intelligence.OpenReview, 2022

Reference 18

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source=pdf_text observed=2026-08-01T12:14:58.278038Z digest=sha256:5c6d218a8baffc95da34386e12a3659fd05b0dd3a8d335dc366300f55d90ebb0

Observation 1924122d-f1d0-4cfa-8437-e312bf3906ea · outbound

This paper cites Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Reference 19

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source=pdf_text observed=2026-08-01T12:14:58.415580Z digest=sha256:0ab527768028c6526851346991c10ddf02e82ee3124134abf6fbaabba78b70cc

Observation 1d097ea4-f646-4b41-add2-fc1905efafdc · outbound

This paper cites Stable Recurrent Models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Stable Recurrent Models

Reference 20

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source=pdf_text observed=2026-08-01T12:14:58.509943Z digest=sha256:b107b2c2c8fdb7439fd2c4532e5afb653c543e2ff5811b85d3173b9f9c2355d7

Observation ecf029b8-2a8e-460e-ac67-46baa4ab993d · outbound

This paper cites Spectral normalization for generative adversarial networks.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Spectral normalization for generative adversarial networks

Reference 21

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source=pdf_text observed=2026-08-01T12:14:58.686027Z digest=sha256:513f7e5a23ec70bfa6e3669a14699730579d5f4df9b75c7b19a9c28ab7a1b51d

Observation 4a315921-82c4-43ed-bbce-ffb54c69629e · outbound

This paper cites Training Transformers with Enforced Lipschitz Constants.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Training Transformers with Enforced Lipschitz Constants

Reference 22

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source=pdf_text observed=2026-08-01T12:14:58.798163Z digest=sha256:8c4f3cf842f25a563ccf6ff480341ded4209834bcaf7b891bbf622a02718e38a

Observation 0382d89b-3849-4ddd-a48a-a639d07ed2c3 · outbound

This paper cites Causal inference in statistics: An overview.Statistics Surveys, 3:96–146, 2009.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Causal inference in statistics: An overview.Statistics Surveys, 3:96–146, 2009

Reference 23

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source=pdf_text observed=2026-08-01T12:14:58.923413Z digest=sha256:f9a34803e0725c1e7469b1f9b149042560fe1180d7f823a4df9f42fc28ee9ea0

Observation ec3c8678-11ce-4799-8b1b-af2484e50ac2 · outbound

This paper cites Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Predictive Objectives Discard Exogenous Control-Relevant Features: A Controlled Mechanistic Study

Reference 24

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Observation 0a8680d1-f178-4a7b-a748-2a6b2c6c7a95 · outbound

This paper cites Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models

Reference 25

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Observation 4aaef45e-3bf9-423c-b0a5-c871e6eb8a19 · outbound

This paper cites Unsupervised learning of video representations using LSTMs.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unsupervised learning of video representations using LSTMs

Reference 26

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Observation f3a961fe-6f44-4952-8ef6-f26c35bf2b5e · outbound

This paper cites Model regularization for stable sample rollouts.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Model regularization for stable sample rollouts

Reference 27

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Observation 351baab3-b0a5-4747-b52d-e91b562d7c9a · outbound

This paper cites DeepMind Control Suite.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not DeepMind Control Suite

Reference 28

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Observation 67b9e913-e420-42b5-a019-cbb5710d71c5 · outbound

This paper cites Benchmarking World-Model Learning with Environment-Level Queries.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Benchmarking World-Model Learning with Environment-Level Queries

Reference 29

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source=pdf_text observed=2026-08-01T12:15:00.017643Z digest=sha256:5d1217ad4419b44aa31a71cf41f76eec3fe0d1a5c01076f4ec3ee65926832177

Observation e87f5694-c886-4f81-952b-0bbc2c1a9c1e · outbound

This paper cites Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning

Reference 30

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Observation 1876430f-f3d1-42a2-a359-2a173c7c787a · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

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Observation 16ac9a68-cf3d-4898-8b02-2d8b70723ba4 · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

Reference 34

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Observation b5c2a75a-cbad-45d2-98cd-7cc0a61d6254 · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

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Observation 6df8ca6b-8e20-46ac-9813-7d860d7534ea · outbound

This paper cites 2.Seed table.Per-seed values at the critical operating point, not only means.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not 2.Seed table.Per-seed values at the critical operating point, not only means

Reference 36

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Observation 5d34b945-2d3b-403a-ac5a-4222c3e6f143 · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

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Observation aae24f98-794f-44fa-a9d2-e02cc882ced2 · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

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source=pdf_text observed=2026-08-01T12:15:00.867019Z digest=sha256:b40a4f7479876ed346e61a4156e326349d9fc2e249645e78616e6a477b394662

Observation fe279c98-66d0-428e-b6ed-4c16f25fa53a · outbound

This paper cites Our main text and appendices are written to satisfy (1)–(6) for the MMNIST critical pair and the η-law primary arm.

Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Our main text and appendices are written to satisfy (1)–(6) for the MMNIST critical pair and the η-law primary arm

Reference 39

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

Reference 2015

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Observation 3eafea17-2232-4d4b-b077-5636fdaabbb6 · outbound

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Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not Unresolved cited work

Reference 2019

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Pith citing papers

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Metric Non-Collapse in Learned World Models for Control: Approximation Theory, Finite-Sample Geometric Guarantees, and Deterministic Planning Transfer cites this paper.

Metric Non-Collapse in Learned World Models for Control: Approximation Theory, Finite-Sample Geometric Guarantees, and Deterministic Planning Transfer Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not

Reference 32

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