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Paper Citation Record · LEDGER

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2607.23602.

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

pith.paper-citation-record.v1
2607.23602 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T17:53:37.024253Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

19 of 19 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier4
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f33b0dcd-8f67-4e8a-a9a4-a336fe4f6359 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 5

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no resolver link, observed 2026-07-30T17:53:36.941633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T17:53:36.941633Z digest=sha256:6b0a527a58792d3c68008e890bde28b686b90258026a8ab04a54657966e2c9ce

Observation f0dbc7ce-85a7-43ad-acac-5533bc6b8548 · outbound

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

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning

Reference 7

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source=pdf_text observed=2026-07-30T17:53:36.951134Z digest=sha256:3dc4f634b2e5af6ed60f481b9cd776ee492ed934284fa97a6f20bfa9edfb0585

Observation ebfaa93b-0e97-4499-bb20-e106320544cc · outbound

This paper cites Mastering Diverse Domains through World Models.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Mastering Diverse Domains through World Models

Reference 10

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source=pdf_text observed=2026-07-30T17:53:36.966377Z digest=sha256:dfe83f7474f72d85747fb79a18b32de695776054ff5d5bbd55998ed87bc85a8e

Observation 59cc62fc-d440-4b63-b43c-70f4f8446f16 · outbound

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

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Nathan Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra

Reference 13

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source=pdf_text observed=2026-07-30T17:53:36.978831Z digest=sha256:c6f8b727c3672f5eded3f8d10f2c7d3ca71d4a363b0f5a5a8d64c4c16dfcfef2

Observation 558f84b6-443a-42c9-a09e-b25fb81dcf0e · outbound

This paper cites Vivek Myers, Bill Chunyuan Zheng, Benjamin Eysenbach, andSergeyLevine.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Vivek Myers, Bill Chunyuan Zheng, Benjamin Eysenbach, andSergeyLevine

Reference 15

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source=pdf_text observed=2026-07-30T17:53:36.987628Z digest=sha256:d2df700e06e8fb8c6e9ef14444ead99cafa2ae8f919d1424c08bad3834a85b61

Observation dc080992-ec57-4d70-9d2d-b5d554fe735b · outbound

This paper cites OGBench: Benchmarking Offline Goal-Conditioned RL.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models OGBench: Benchmarking Offline Goal-Conditioned RL

Reference 17

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source=pdf_text observed=2026-07-30T17:53:36.995307Z digest=sha256:a1fa66794b93d59840202ad48e3559a5ea400785d9c6b0fc7cb567d293b87bad

Observation 47b7adb7-260c-49c9-acb3-3259ce652961 · outbound

This paper cites Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, and Georg Martius.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, and Georg Martius

Reference 18

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source=pdf_text observed=2026-07-30T17:53:36.998994Z digest=sha256:262607892b874b5d265f2b87d5da7c38a3a898e064591b1a1a23ac7ea53b1b9d

Observation 9a737fef-9670-4c79-a2c4-3a8ccf199a87 · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 24

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source=pdf_text observed=2026-07-30T17:53:37.024253Z digest=sha256:de15de29eb515f17fe666ef9fc53d3adacd9191204d05fe16477867783bedba7

Observation 26c3e6ac-74b7-490e-92eb-06d9e201092e · outbound

This paper cites Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine

Reference 1963

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no resolver link, observed 2026-07-30T17:53:36.974642Z

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source=pdf_text observed=2026-07-30T17:53:36.974642Z digest=sha256:d28ff7ecc78883eb1af73562b11ea5f24b16b28c82a0fff839bbd734ba158eaf

Observation 6c07d988-da87-48aa-9982-9b674a73c710 · outbound

This paper cites 25 YuhaiWang,JiaweiXia,RongxuanZhou,XiaoHu,Yongliang Shi, Jing Du, and Yang Ye.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models 25 YuhaiWang,JiaweiXia,RongxuanZhou,XiaoHu,Yongliang Shi, Jing Du, and Yang Ye

Reference 2006

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source=pdf_text observed=2026-07-30T17:53:37.016172Z digest=sha256:decc3337b3d885e91289e4ee94e3749722383ed08b541b49ea8ef8bd71507053

Observation ea0aba75-447f-4368-b8c0-764fdedc077b · outbound

This paper cites an unresolved cited work.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Unresolved cited work

Reference 2017

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no resolver link, observed 2026-07-30T17:53:37.020066Z

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source=pdf_text observed=2026-07-30T17:53:37.020066Z digest=sha256:6a82662cfb6374e58407251e4b965f8f147a153114da19b7a7fac6e16d6ffed1

Observation 48dc730f-a830-40cf-9751-30069e7d9dbb · outbound

This paper cites Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Reference 2018

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source=pdf_text observed=2026-07-30T17:53:36.946650Z digest=sha256:c2da58a3c4abae360e1e8ce19390099ef3e94ad838a1dc5fde7c7e163bb1afc9

Observation 561f9d40-10f3-4a9c-976a-1de9f0a3dd31 · outbound

This paper cites Search on the Replay Buffer: Bridging Planning and Reinforcement Learning.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Reference 2019

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source=pdf_text observed=2026-07-30T17:53:36.955824Z digest=sha256:e9e0bc932df65e929f1eec9d424ba877d53aa6dcb70ae468e81e14a9ea706788

Observation 63c07340-ea9b-413a-8080-f0e02fd592a9 · outbound

This paper cites Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics

Reference 2020

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source=pdf_text observed=2026-07-30T17:53:36.983691Z digest=sha256:c5bccc6dd5e39fef0a7d5cdc332fd28ebfecfed2e5d010b4c068f4de8f5acea8

Observation 8ca8ea04-c621-495c-aa30-8bf3705b57a6 · outbound

This paper cites UniZero: Generalized and Efficient Planning with Scalable Latent World Models.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models UniZero: Generalized and Efficient Planning with Scalable Latent World Models

Reference 2021

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source=pdf_text observed=2026-07-30T17:53:37.004079Z digest=sha256:9d334d1340c94c96666e295e26e393573d3814caac7ea22cb3844f1e710d80fd

Observation f6e22c49-a04d-4c08-8f03-07d1586a45cc · outbound

This paper cites Nicklas Hansen, Hao Su, and Xiaolong Wang.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Nicklas Hansen, Hao Su, and Xiaolong Wang

Reference 2022

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source=pdf_text observed=2026-07-30T17:53:36.970044Z digest=sha256:26564527f5218583bf3cf4920f9c4251d15e91329c11c46638425743810828fa

Observation d2c29f64-e791-4a0a-a998-6939adb1345b · outbound

This paper cites Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning

Reference 2023

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source=pdf_text observed=2026-07-30T17:53:37.012368Z digest=sha256:12c47f6c47b99a70c05fec898238f397bb479fc18289538b475f9c1327a64eda

Observation eaf8a61c-ca52-469e-b790-80e328a99196 · outbound

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

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2025

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source=pdf_text observed=2026-07-30T17:53:36.928883Z digest=sha256:47b716b6dc7a828aa05e5319afdb2f693e2a72cc371f416fb949f272bdf6de99

Observation fbdef916-b3c5-457a-82b3-49cc6fd30ddd · outbound

This paper cites SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning.

Action from Adjacent Set in Physical Space Outperforms the Best Prediction in World Models SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning

Reference 2026

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source=pdf_text observed=2026-07-30T17:53:36.937914Z digest=sha256:74cf68a4e1749b6ce7b341dc986135781a2bfcd9bb2d1ca8a3ca27ebbf22979f

Pith citing papers

No inbound Pith citation observations are available.