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Paper Citation Record · LEDGER

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models

As of 19 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.11174.

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

pith.paper-citation-record.v1
2608.11174 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:21:38.556930Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a57f1e14-4cd6-4f4a-a0f0-4dbb4fea5c7e · outbound

This paper cites Predictive but Not Plannable: RC-aux for Latent World Models.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Predictive but Not Plannable: RC-aux for Latent World Models

Reference 6

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source=pdf_text observed=2026-08-15T14:21:38.488213Z digest=sha256:11f9276cd284f92aca42528ac460cd2a93d9cc4a01c29899fefaac468647dca2

Observation 3bc18ef5-eff9-45f0-ab0a-88f5c0c433e4 · outbound

This paper cites Decoupled Weight Decay Regularization.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Decoupled Weight Decay Regularization

Reference 7

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source=pdf_text observed=2026-08-15T14:21:38.493405Z digest=sha256:828683f1af64fd65f3586c1bcec176c039b01c059613a5397d1569cb4edad1a8

Observation 0d421f97-e9e9-4df2-bd5a-abfcf2dff805 · outbound

This paper cites INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models

Reference 11

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source=pdf_text observed=2026-08-15T14:21:38.513191Z digest=sha256:73d5ea818390817f6959fbacf44da852225c2b3839c4a2909139ceffca7b755f

Observation 040df959-a203-4fe6-ae47-0e1f0e25c738 · outbound

This paper cites DeepMind Control Suite.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models DeepMind Control Suite

Reference 12

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source=pdf_text observed=2026-08-15T14:21:38.518178Z digest=sha256:003da59c17d85ffdb937344a7161a1adf92ef3493ff24f2a57ee6e03b599fcda

Observation cd6c41bf-6bf5-40c9-94ac-7c6f022e2c17 · outbound

This paper cites Efficient Empowerment Estimation for Unsupervised Stabilization.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Efficient Empowerment Estimation for Unsupervised Stabilization

Reference 16

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source=pdf_text observed=2026-08-15T14:21:38.539043Z digest=sha256:d2645ced268da3b2f7dcb2691a3bb56f89a8114efd15159a0bbc16fd5b3f30b9

Observation 6e557c02-704a-4cd1-aefe-6f7d05861ce9 · outbound

This paper cites We first provide the training details and the SSL observations that motivate the regularization intervention.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models We first provide the training details and the SSL observations that motivate the regularization intervention

Reference 17

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source=pdf_text observed=2026-08-15T14:21:38.544221Z digest=sha256:0bd8c0bdae9c7468d18e8d5d639ad0d95e7a027c872395014edeb46279d6dd75

Observation 16539edc-9bce-49c9-8f18-d8afb0c09395 · outbound

This paper cites We train ViT-S/8 from scratch for 400epochs and compare with SWD (Bon- neel et al., 2015), DINO (Caron et al., 2021), VICReg (Bardes et al., 2022), and SIGReg.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models We train ViT-S/8 from scratch for 400epochs and compare with SWD (Bon- neel et al., 2015), DINO (Caron et al., 2021), VICReg (Bardes et al., 2022), and SIGReg

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:21:38.549761Z digest=sha256:7449f66914c443fe0444d5215a6d6a28c5950530610780b234bbeeba3fbeadac

Observation e524ec11-094d-4653-9397-121a4cc35ac7 · outbound

This paper cites The frozen mapping is fit on the three supported development tasks only.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models The frozen mapping is fit on the three supported development tasks only

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T14:21:38.556930Z digest=sha256:e8c7a39d91e0dc5123927f13616ac9f42b18d3a39c30ac666543d13b67f6f077

Observation 5c9a7f41-73b2-4fa7-9dd0-c56f8b1eb7c9 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 1983

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source=pdf_text observed=2026-08-15T14:21:38.467644Z digest=sha256:84d9bcb4dd5887979cce00e258a9a4fde2800fa27ef574e432161ac4fb3e9b44

Observation 8efaefdd-e6a6-4786-a1dd-be39d5e8849e · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Offline Reinforcement Learning with Implicit Q-Learning

Reference 2005

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source=pdf_text observed=2026-08-15T14:21:38.478780Z digest=sha256:1d2d4bab46ce655277413bbdc17bfcd0631ff44039a879dac8a2051efe2e293f

Observation e2282459-e945-4a39-9bcd-2527e65654d1 · outbound

This paper cites VISReg: Variance-Invariance-Sketching Regularization for JEPA training.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models VISReg: Variance-Invariance-Sketching Regularization for JEPA training

Reference 2017

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source=pdf_text observed=2026-08-15T14:21:38.528203Z digest=sha256:fb5682a828b47d11015f99c16c30fdbd04904de19374716095b6967d51fd7797

Observation f3a8bb3d-9194-4a40-b6f5-42b7ac1860a7 · outbound

This paper cites Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models

Reference 2018

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source=pdf_text observed=2026-08-15T14:21:38.523472Z digest=sha256:e8b0bf83d7e18c0578d4da8552b0d260dc8efe2a6f94728e4f76a91b4c3b8e7a

Observation 134e8d18-6b30-4bc3-b2c3-b33635210f3f · outbound

This paper cites Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo

Reference 2019

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source=pdf_text observed=2026-08-15T14:21:38.473019Z digest=sha256:b056bb1acc1633d11c4936ba59662dd8d19484f721d795c2eec6ec26eccadf01

Observation 301e8efa-aba9-4f0d-9e8a-7c491d3f14ca · outbound

This paper cites Qantara: Bridge-flow training for multi-paradigm jepa control.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Qantara: Bridge-flow training for multi-paradigm jepa control

Reference 2020

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source=pdf_text observed=2026-08-15T14:21:38.503567Z digest=sha256:245a7d5c7802f626db49413233d9c0b1e8f7c25fdf1dfa789e1e3d4d932dd276

Observation e2bc81aa-b043-4496-b7d1-3ef19be4ccc1 · outbound

This paper cites Hierarchical Planning with Latent World Models.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Hierarchical Planning with Latent World Models

Reference 2021

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source=pdf_text observed=2026-08-15T14:21:38.533668Z digest=sha256:9ca31fa62b3ad291e18d01c4077e59ce5e98226811dd6f3a9a0fe16e04f82ade

Observation a6f37a95-2e49-4268-884b-7dd4da3e7961 · outbound

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

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2022

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source=pdf_text observed=2026-08-15T14:21:38.454228Z digest=sha256:40fec9161438883bb33f70ad4da8a2b2af017447c95d48c4a8a539cee8e9ba62

Observation 63f75590-dc8d-4a39-9e35-0d66351546b5 · outbound

This paper cites Foundation Policies with Hilbert Representations.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Foundation Policies with Hilbert Representations

Reference 2023

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source=pdf_text observed=2026-08-15T14:21:38.498034Z digest=sha256:24274a589297763c3355d7d8e5d2dee01b424e28b8dee830d7a50d867d4c0a9b

Observation 2018e79f-c128-4985-8a0f-495f83e0830c · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 2025

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source=pdf_text observed=2026-08-15T14:21:38.461371Z digest=sha256:ab349d9b642d765eb0b70e53e1bbb039a8168ea46b449dbd47d780a4f42ab996

Observation 70f75d6a-9968-4150-8d5d-47a4f365404a · outbound

This paper cites Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control.

VIScore: Diagnosing Planning-Relevant Quality in Latent World Models Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control

Reference 2026

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source=pdf_text observed=2026-08-15T14:21:38.508146Z digest=sha256:ab32e80cf57d91e8df4f46a09f14e014e8cb3b3dd4df76e0037e38af929533eb

Pith citing papers

No inbound Pith citation observations are available.