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Paper Citation Record · LEDGER

QQWorld: Quantile-Quantile Matching for World Model Regularization

As of 9 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.28415.

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

pith.paper-citation-record.v1
2607.28415 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T08:06:20.001606Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

16 of 16 outbound references displayed

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  • verified fuzzy0
  • unresolved16
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e7008ecc-2af8-4835-aef4-c9d0c60ede3b · outbound

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

QQWorld: Quantile-Quantile Matching for World Model Regularization V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 1

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no resolver link, observed 2026-07-31T08:06:18.493167Z

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source=pdf_text observed=2026-07-31T08:06:18.493167Z digest=sha256:569dd0ff53890048f4dad04888f72180e2a7bcd89653a66a88553a55113ef135

Observation 1c3a2854-c4ba-41c0-8032-98d622e65e3c · outbound

This paper cites Sensorimotor World Models: Perception for Action via Inverse Dynamics.

QQWorld: Quantile-Quantile Matching for World Model Regularization Sensorimotor World Models: Perception for Action via Inverse Dynamics

Reference 8

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source=pdf_text observed=2026-07-31T08:06:19.191076Z digest=sha256:f1275221d88a8ea8c691398d21e7acd7871e5f8b0834eaeba74a3c9d509aa342

Observation 958f9f49-83eb-45b6-9fc1-928a6f421206 · outbound

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

QQWorld: Quantile-Quantile Matching for World Model Regularization A path towards autonomous machine intelligence version 0.9

Reference 9

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source=pdf_text observed=2026-07-31T08:06:19.286906Z digest=sha256:0dd341617a7cce7ec8cf2154bcdd5f1b04cbb6adf0520bf0168e1ffae4a83d78

Observation c96f3c4b-79f1-4f67-845c-6cb9d2ea0fec · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

QQWorld: Quantile-Quantile Matching for World Model Regularization LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 10

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source=pdf_text observed=2026-07-31T08:06:19.395732Z digest=sha256:e38fd8c3ba898083790f14045e89bce9969e697cd011f94ebb86fdc201e7ef54

Observation 3af8f9f5-46fa-4c36-b5b5-057e4672dfac · outbound

This paper cites FF-JEPA: Long-Horizon Planning in World Models with Latent Planners.

QQWorld: Quantile-Quantile Matching for World Model Regularization FF-JEPA: Long-Horizon Planning in World Models with Latent Planners

Reference 11

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no resolver link, observed 2026-07-31T08:06:19.496426Z

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source=pdf_text observed=2026-07-31T08:06:19.496426Z digest=sha256:b3eea0eab2c1d2c86372e3fbc5d8ce69cc7732fa9577f2c809123ac11a28c4b5

Observation 53744ce5-e826-46d3-9661-f73b9777a09b · outbound

This paper cites Latent Geometry Beyond Search: Amortizing Planning in World Models.

QQWorld: Quantile-Quantile Matching for World Model Regularization Latent Geometry Beyond Search: Amortizing Planning in World Models

Reference 12

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no resolver link, observed 2026-07-31T08:06:19.545210Z

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source=pdf_text observed=2026-07-31T08:06:19.545210Z digest=sha256:907d13f39f3f497664963faad1a789b8ab1b9d34d30916a11c8d60f61fbb375c

Observation 3ffed40e-77f8-49a4-bbea-54dc11c9c6d9 · outbound

This paper cites DeepMind Control Suite.

QQWorld: Quantile-Quantile Matching for World Model Regularization DeepMind Control Suite

Reference 13

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source=pdf_text observed=2026-07-31T08:06:19.625277Z digest=sha256:fa9d0efe04c1c83ff2c626b6b2824dffac747517aac30ab36158191f401411fe

Observation 4caf142a-5fb4-4b9c-a399-27f95db6114e · outbound

This paper cites Hierarchical Planning with Latent World Models.

QQWorld: Quantile-Quantile Matching for World Model Regularization Hierarchical Planning with Latent World Models

Reference 16

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source=pdf_text observed=2026-07-31T08:06:20.001606Z digest=sha256:31e3062e649593269f873b674e33afc1e7d2fb64e852d3b8993bb2423548dce0

Observation a7e6dd31-9e90-4c0e-bce1-6b8d08afd18e · outbound

This paper cites A Generalization Theory for JEPA-Based World Models.

QQWorld: Quantile-Quantile Matching for World Model Regularization A Generalization Theory for JEPA-Based World Models

Reference 1936

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no resolver link, observed 2026-07-31T08:06:18.610888Z

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source=pdf_text observed=2026-07-31T08:06:18.610888Z digest=sha256:ce38162b888964ff0ff65a31c76b430cd588b49ac3a953767991713b5d0d5e64

Observation a344c8ac-eb3e-49c9-b92f-3c408a8332dd · outbound

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

QQWorld: Quantile-Quantile Matching for World Model Regularization VISReg: Variance-Invariance-Sketching Regularization for JEPA training

Reference 1968

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no resolver link, observed 2026-07-31T08:06:19.879794Z

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source=pdf_text observed=2026-07-31T08:06:19.879794Z digest=sha256:851238e5efac7c536cee4493aa3cffb69f219c7c541e3deb242e30825246e108

Observation 66bac504-c335-4eee-9636-fd7078b3d7f0 · outbound

This paper cites IMWM: Intuition Models Complement World Models for Latent Planning.

QQWorld: Quantile-Quantile Matching for World Model Regularization IMWM: Intuition Models Complement World Models for Latent Planning

Reference 1983

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no resolver link, observed 2026-07-31T08:06:18.696355Z

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source=pdf_text observed=2026-07-31T08:06:18.696355Z digest=sha256:fe2312db03e5ef084640f50034976ae84ffc2211b27b7b9549967961a359ee22

Observation db9bd900-5a98-4f76-b356-de14ad11ef2b · outbound

This paper cites World Models.

QQWorld: Quantile-Quantile Matching for World Model Regularization World Models

Reference 2012

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source=pdf_text observed=2026-07-31T08:06:19.082048Z digest=sha256:3c21ce95a743665e2c8edc4950d2985d0471127c4b09667b9bd2e122800ac64f

Observation f2622116-7e26-4672-a311-ff18224528f3 · outbound

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

QQWorld: Quantile-Quantile Matching for World Model Regularization Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models

Reference 2018

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source=pdf_text observed=2026-07-31T08:06:19.744743Z digest=sha256:d4e44d2f2e96b3d86116806808519cdc20cc712b5b62e336b4fc49d7788ad285

Observation b21ce97c-1638-4396-bc39-6db3a2bb6d75 · outbound

This paper cites Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv:2606.15768,.

QQWorld: Quantile-Quantile Matching for World Model Regularization Lawam: Latent world action models for efficient dynamics-aware robot policies.arXiv:2606.15768,

Reference 2022

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source=pdf_text observed=2026-07-31T08:06:18.542788Z digest=sha256:53a2b1715097a7ce224f0258442e63b70b96fa558bef64045f10e71cd4a220f9

Observation e3930869-f486-4ff4-a57b-a6d674ba8480 · outbound

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

QQWorld: Quantile-Quantile Matching for World Model Regularization LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 2025

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source=pdf_text observed=2026-07-31T08:06:18.539680Z digest=sha256:cea340f46b156978798e34a257a47d1863509ecd1b0add8cec4f0282cf0c196c

Observation 270e552c-f95e-4fa6-b91d-95a15ac82571 · outbound

This paper cites Fast LeWorldModel.

QQWorld: Quantile-Quantile Matching for World Model Regularization Fast LeWorldModel

Reference 2026

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source=pdf_text observed=2026-07-31T08:06:18.930079Z digest=sha256:da41fa87723003ba6a08f36fc964e36e27987aa8c423f9cc4bd6635acd12e1c2

Pith citing papers

No inbound Pith citation observations are available.