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Paper Citation Record · LEDGER

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2608.07420.

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

pith.paper-citation-record.v1
2608.07420 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:59:30.123195Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

45 of 45 outbound references displayed

  • verified exact2
  • verified fuzzy9
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18a55230-a3d0-421e-8626-213ced7f1d97 · outbound

This paper cites DIAMOND: Diffusion for world modeling: Visual details matter in atari.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction DIAMOND: Diffusion for world modeling: Visual details matter in atari

Reference 1

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation bc17e08a-d9e3-4061-b923-471599a71ac0 · outbound

This paper cites Lipschitz Continuity in Model-based Reinforcement Learning.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Lipschitz Continuity in Model-based Reinforcement Learning

Reference 2

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source=pdf_text observed=2026-08-10T04:59:29.778982Z digest=sha256:8b3eb5601d4732326eea50a11bc2e407b1dfb7e5856721c60df84de67aa76a4b

Observation 8924a219-fff0-48f2-a868-c38026dba62f · outbound

This paper cites Combating the Compounding-Error Problem with a Multi-step Model.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Combating the Compounding-Error Problem with a Multi-step Model

Reference 3

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Observation ebd0a93a-89f6-4a9e-a688-8d5e9904f08d · outbound

This paper cites an unresolved cited work.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Unresolved cited work

Reference 4

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Observation 1bf57239-b047-4876-97af-b141bc0ba86b · outbound

This paper cites Multi-timestep models for model-based reinforcement learning, 2024.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Multi-timestep models for model-based reinforcement learning, 2024

Reference 5

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source=pdf_text observed=2026-08-10T04:59:29.831665Z digest=sha256:f4a8fe844bfdfb28a8ee4689901c1d2a2d2a2576f360e775f47791f26831e06a

Observation c9756a44-3dd8-4e7a-b65d-bdea80427dd6 · outbound

This paper cites Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Reference 6

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source=pdf_text observed=2026-08-10T04:59:29.836826Z digest=sha256:ac887cb9b16c698a49cb64f7da776e7e38c096670f6530eb4d3d735e24ba2d31

Observation 509f6108-6320-48dc-97de-017997baafdf · outbound

This paper cites Bengio, P.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Bengio, P

Reference 7

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source=pdf_text observed=2026-08-10T04:59:29.842128Z digest=sha256:e2a622ab5f9c5e2dce241717886639b970f4f633eeb36241e4b7ba4097cead56

Observation c3745dbc-1307-4d3e-8b80-58cec1498792 · outbound

This paper cites Genie: Generative Interactive Environments.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Genie: Generative Interactive Environments

Reference 8

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source=pdf_text observed=2026-08-10T04:59:29.852319Z digest=sha256:b8663092a603730574ac7020c78b12e841c92760024beb4c25f030018dac4752

Observation c5e39d5e-cb7f-46d8-9686-a160288d0aba · outbound

This paper cites an unresolved cited work.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-10T04:59:29.864749Z digest=sha256:63a9e6ac390cba0e03b9997c01b509120211b69a40c7b8b70ef58585995263d7

Observation ecf415a8-e5bc-46ec-9d9d-bd1722164bb7 · outbound

This paper cites Diversity is all you need: Learning skills without a reward function, 2018.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Diversity is all you need: Learning skills without a reward function, 2018

Reference 10

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source=pdf_text observed=2026-08-10T04:59:29.880604Z digest=sha256:072b78ee18df16929458da17eaa57e83fa2881e86bee4d41f4cce6d4fc0e2558

Observation 8484d6b5-9a5e-4446-807b-24198e626695 · outbound

This paper cites Unsupervised Learning for Physical Interaction through Video Prediction.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Unsupervised Learning for Physical Interaction through Video Prediction

Reference 11

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Observation 2efd76cf-11c6-4d3f-aa1c-fa2dad9e4eeb · outbound

This paper cites Professor forcing: a new algorithm for training recurrent networks.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Professor forcing: a new algorithm for training recurrent networks

Reference 12

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source=pdf_text observed=2026-08-10T04:59:29.921602Z digest=sha256:8acffe7975dc62ae4b7e7225f8dffab676edff41f3cfaba54cbbab871562a328

Observation eb109124-017c-4354-bbe8-85b802985da1 · outbound

This paper cites an unresolved cited work.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Unresolved cited work

Reference 13

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Observation 7ecb51a3-1aed-4ab5-9597-832a27ff0e9e · outbound

This paper cites World Models.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction World Models

Reference 14

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Observation b6c6053b-d93f-4d82-a187-7dda275cde1f · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Dream to Control: Learning Behaviors by Latent Imagination

Reference 15

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source=pdf_text observed=2026-08-10T04:59:29.945091Z digest=sha256:71a4546d57a166f296fa369368e7e9d175faa78141d0886ab626f05b09100102

Observation 51dbb2c8-847d-467c-ae8a-962a3d5465ea · outbound

This paper cites Learning latent dynamics for planning from pixels.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Learning latent dynamics for planning from pixels

Reference 16

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 7c28e615-7198-4377-8890-398d25d81af6 · outbound

This paper cites Mastering Atari with Discrete World Models.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Mastering Atari with Discrete World Models

Reference 17

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source=pdf_text observed=2026-08-10T04:59:29.953241Z digest=sha256:1c13895283ee25d1ecbea63e8c947ad507119ecaa568c95e8fed566831d9aa19

Observation 7274d489-584c-40c0-bfbd-c42f85c0de6a · outbound

This paper cites Mastering diverse control tasks through world models.Nature, pages 1–7, 2025.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Mastering diverse control tasks through world models.Nature, pages 1–7, 2025

Reference 18

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source=pdf_text observed=2026-08-10T04:59:29.958235Z digest=sha256:bafad2735a249242e63aefd19cada92e4d03f3c0d535192cf309a6eb84cbf685

Observation ebd9d996-4f3e-46b1-92c9-ddf6cc0476fb · outbound

This paper cites Temporal Difference Learning for Model Predictive Control.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Temporal Difference Learning for Model Predictive Control

Reference 19

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source=pdf_text observed=2026-08-10T04:59:29.962056Z digest=sha256:5e09287619fab48cf9186358287d8e46b68bd32e24e861c58a8af3a17c2b8969

Observation 72721181-b42e-46c8-99ea-441da93f5856 · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 20

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source=pdf_text observed=2026-08-10T04:59:29.965750Z digest=sha256:b166b88f803069fe64901e093779eb31be9bb87e7af30918541ee4f063d6b104

Observation abff9003-dd0a-4bde-a219-9f6b7aa5e784 · outbound

This paper cites Neural motion simulator pushing the limit of world models in reinforcement learning.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Neural motion simulator pushing the limit of world models in reinforcement learning

Reference 21

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9d8d26ea-6baf-49e8-8124-620a905db362 · outbound

This paper cites Model-Based Planning with Discrete and Continuous Actions.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Model-Based Planning with Discrete and Continuous Actions

Reference 22

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Observation 7f691b9c-6c79-4130-946d-43ddc1ee227f · outbound

This paper cites Efros, and Sergey Levine.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Efros, and Sergey Levine

Reference 23

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Observation 9c4fb1de-afc7-41ea-8918-e250a9ddf26f · outbound

This paper cites How Far is Video Generation from World Model: A Physical Law Perspective.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction How Far is Video Generation from World Model: A Physical Law Perspective

Reference 24

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source=pdf_text observed=2026-08-10T04:59:29.983053Z digest=sha256:34ca2065273b3ff164aa20e8eb0b8ed0c4fefec19d85c29c1119c5bc7fb374a5

Observation 6b7357ab-847c-43cf-8f44-66073d45d155 · outbound

This paper cites A path towards autonomous machine intelligence version 0.9.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction A path towards autonomous machine intelligence version 0.9

Reference 25

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source=pdf_text observed=2026-08-10T04:59:29.994748Z digest=sha256:cd3b44ddcedb31cf32831f00cd529c3f46bb00b1b249df234335a553ff4e7967

Observation 23986565-173e-4354-ba80-9534289887bd · outbound

This paper cites Smallworlds: Assessing dynamics understanding of world models in isolated environments, 2025.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Smallworlds: Assessing dynamics understanding of world models in isolated environments, 2025

Reference 26

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source=pdf_text observed=2026-08-10T04:59:30.006373Z digest=sha256:0aba141a428b185ce85f7c04d6fc3752dfa9ce8c910140af07918acc0e9cdee0

Observation ed6be7a9-5a88-49ed-9cb5-05cfc077e36e · outbound

This paper cites Any-step dynamics model improves future predictions for online and offline reinforcement learning.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Any-step dynamics model improves future predictions for online and offline reinforcement learning

Reference 27

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source=pdf_text observed=2026-08-10T04:59:30.025690Z digest=sha256:8e3cd4534458d6a730fce81464071b2aec7c401f7608bee5ed916837e681066f

Observation 2ec7ce6e-adeb-48f1-924e-acc3c9fe2900 · outbound

This paper cites Adm-v2: Pursuing full-horizon roll-out in dynamics models for offline policy learning and evaluation.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Adm-v2: Pursuing full-horizon roll-out in dynamics models for offline policy learning and evaluation

Reference 28

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source=pdf_text observed=2026-08-10T04:59:30.035363Z digest=sha256:95e11ef1df56b4978bd8f0d66e41cbde0f3f5724329e913d3e0c9852f8fce801

Observation 040b0d5a-f667-4ab8-bf43-19d833ca7ea7 · outbound

This paper cites From kepler to newton: Inductive biases guide learned world models in transformers.ArXiv, abs/2602.06923, 2026.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction From kepler to newton: Inductive biases guide learned world models in transformers.ArXiv, abs/2602.06923, 2026

Reference 29

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source=pdf_text observed=2026-08-10T04:59:30.044258Z digest=sha256:0e2dfa1d5b6609c4cbde81e49927a85224fc9925f409e22fb96ed835cea0cea6

Observation cabdb706-c5ba-4a3b-8755-0ef54ca2c5f3 · outbound

This paper cites Temporal Abstraction in Reinforcement Learning with the Successor Representation.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Temporal Abstraction in Reinforcement Learning with the Successor Representation

Reference 30

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Observation 1b3a1c66-b37e-4734-9a9c-7773efe6e0c6 · outbound

This paper cites Transformers are sample-efficient world models.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Transformers are sample-efficient world models

Reference 31

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source=pdf_text observed=2026-08-10T04:59:30.053548Z digest=sha256:d1811c35c20b23709959f546d7593f978c05d01c213172286dbafe11dc469f47

Observation ba1f3809-9950-45a9-9ba8-fbd27856e298 · outbound

This paper cites On the difficulty of training recurrent neural networks.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction On the difficulty of training recurrent neural networks

Reference 32

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source=pdf_text observed=2026-08-10T04:59:30.057316Z digest=sha256:fbd3399fe7a5b438d9f1900b1f6ae75936f68c9bbbd0cd5204b9684683390824

Observation 70d19c9a-ffe3-4741-9a06-b413dd1958bb · outbound

This paper cites FiLM: Visual Reasoning with a General Conditioning Layer.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction FiLM: Visual Reasoning with a General Conditioning Layer

Reference 33

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source=pdf_text observed=2026-08-10T04:59:30.061059Z digest=sha256:dbd8576039b6af0c4b86b22132c66d3eca33d172dcbb8933f0a4134b3920ff75

Observation 7da57128-7daf-4edd-841f-644a5b705db7 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 34

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source=pdf_text observed=2026-08-10T04:59:30.067738Z digest=sha256:1b93a66b400be36457b77c2fc187f2efbe651fc4e3e62456ce7fafa335cf52bb

Observation 514c3c40-f568-47a9-9d94-a69c2936babe · outbound

This paper cites Dyna, an integrated architecture for learning, planning, and reacting.ACM Sigart Bulletin, 2(4):160–163, 1991.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Dyna, an integrated architecture for learning, planning, and reacting.ACM Sigart Bulletin, 2(4):160–163, 1991

Reference 35

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source=pdf_text observed=2026-08-10T04:59:30.071735Z digest=sha256:1abdc3c1a84f6f3055214ccd35f8ffcab815ea84df5c7b9c3e31baf30bd614a4

Observation 1cefa4cd-3179-4da4-b099-35c19afcc944 · outbound

This paper cites Self-Correcting Models for Model-Based Reinforcement Learning.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Self-Correcting Models for Model-Based Reinforcement Learning

Reference 36

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This paper cites dm_control: Software and tasks for continuous control.Software Impacts, 6:100022, 2020.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction dm_control: Software and tasks for continuous control.Software Impacts, 6:100022, 2020

Reference 37

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This paper cites Chang, Ashesh Rambachan, and Sendhil Mullainathan.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Chang, Ashesh Rambachan, and Sendhil Mullainathan

Reference 38

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This paper cites Attention Is All You Need.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Attention Is All You Need

Reference 39

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Observation 00e1ab39-4f77-49cf-88fc-d7169a220c7d · outbound

This paper cites Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models

Reference 40

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Observation 808b15e9-03b9-4a16-a966-7f99c3c59e1a · outbound

This paper cites Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

Reference 41

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This paper cites Hierarchical Planning with Latent World Models.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Hierarchical Planning with Latent World Models

Reference 42

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Observation 402418ff-d869-4e2e-a0c2-589f9658b771 · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 43

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Observation 9064f369-0960-4a8a-aac1-34655adb0efa · outbound

This paper cites doi: 10.1613/jair.3912.

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction doi: 10.1613/jair.3912

Reference 2013

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Observation 3ca24311-a5b0-4c36-874a-e56b581dd810 · outbound

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Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction Unresolved cited work

Reference 2025

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Pith citing papers

No inbound Pith citation observations are available.