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Paper Citation Record · LEDGER

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

As of 20 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2608.08558.

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

pith.paper-citation-record.v1
2608.08558 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:35:47.635198Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

22 of 22 outbound references displayed

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  • verified fuzzy5
  • unresolved14
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d25b082d-807e-4971-b145-4d6bde3eaffb · outbound

This paper cites Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models

Reference 6

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verified exact
local_arxiv, observed 2026-08-14T04:35:48.684757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:46.944761Z digest=sha256:393aba2ef8997d79d1ce9eb7727576cad9b5df23d3a8b1dfa946f97ca84c1137

Observation 0d65bcc2-e65a-40ec-8855-772f2b997fb3 · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 7

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:46.987762Z digest=sha256:7cca17cc736e927472ab8ef0b39860bdcbe3bf839d4b9de534616e4912c9d6c9

Observation e5e9978d-a57e-4e5a-821f-12288fc83b7f · outbound

This paper cites InProceedings of the 2023 Conference on Robot Learning.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InProceedings of the 2023 Conference on Robot Learning

Reference 8

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verified fuzzy
raw_fallback, observed 2026-08-14T04:35:49.513897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:47.035932Z digest=sha256:7e71d6c49302f9f74b815649f7127470bd7fd7f9c495b59844b0e2b67ed7d026

Observation df82bc25-b919-465b-b40c-c55849dbe0fe · outbound

This paper cites InProceedings of the 42nd International Conference on Machine Learning, volume 267 ofProceed- ings of Machine Learning Research, 24328–24346.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InProceedings of the 42nd International Conference on Machine Learning, volume 267 ofProceed- ings of Machine Learning Research, 24328–24346

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-14T04:35:49.401591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:47.127965Z digest=sha256:05dbd79ed9a1b7b897a0fd335b1b91ca2277e256f5cd3715b4e84a271459ae1f

Observation eda0f05b-7908-4d4f-95ee-cd59726a1bd9 · outbound

This paper cites CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.192925Z digest=sha256:4c2fe194edb71e3eda1095136ccb5ecc875a94aa4b283a15d294eada9ae19aac

Observation 14c61720-e429-41ab-b43c-48284de52987 · outbound

This paper cites InProceed- ings of the 8th Conference on Robot Learning, volume 270 ofProceedings of Machine Learning Research, 2679–2713.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InProceed- ings of the 8th Conference on Robot Learning, volume 270 ofProceedings of Machine Learning Research, 2679–2713

Reference 12

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.231781Z digest=sha256:376a5aa44626fb87b052c435436a37eadfabdac7c94fde1bb9c3adea6dd27319

Observation 4e0e25da-c894-43b5-a5da-901dcdbebec0 · outbound

This paper cites Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.274753Z digest=sha256:0dbf6527f500cd7fd0c4bbda3719a666b31e9cc078c39ac90c9f0a09ef691a94

Observation 0d77d707-218d-46b0-b97f-68db6d4d8e8e · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 14

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source=pdf_text observed=2026-08-14T04:35:47.324853Z digest=sha256:fe362ed3dff7c6ce685b3c15ecfd391bbe3a712078ef8ae0226f1e3375fb50b9

Observation 44cbcfe5-b7b5-4fc1-89ab-fc2dd862eca1 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Wan: Open and Advanced Large-Scale Video Generative Models

Reference 15

Resolution
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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.384766Z digest=sha256:85ff1f2e533cf83299713b4dfe0c6789d7b2c227210b8cd55a6da5a599dc2d08

Observation ae3b2025-f94e-44c0-af9a-857545898b41 · outbound

This paper cites arXiv:2603.16195.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models arXiv:2603.16195

Reference 16

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no resolver link, observed 2026-08-14T04:35:47.424750Z

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source=pdf_text observed=2026-08-14T04:35:47.424750Z digest=sha256:322599fcb741855e9cdc864cb535b4d7a1d23e533584b1308521911d2b40cd8f

Observation def13912-140e-4fff-a2da-b52ef96155a3 · outbound

This paper cites World Action Models are Zero-shot Policies.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models World Action Models are Zero-shot Policies

Reference 17

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no resolver link, observed 2026-08-14T04:35:47.464753Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.464753Z digest=sha256:db9ad686bf5c5be835ab14d277415c5323a85fd94fdc9d7e3cfbcf0c48bc5ed5

Observation 6001bddb-6b1a-4f95-95d3-934d9a1a1d78 · outbound

This paper cites InThe Thirteenth International Conference on Learning Representations.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InThe Thirteenth International Conference on Learning Representations

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T04:35:49.280828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:47.502085Z digest=sha256:fd411c58a81c6b808281434c412d831696015451d3c5e01337e249a006ea3677

Observation 7872af17-3d4c-4339-90df-678c524fccb6 · outbound

This paper cites Fast-WAM: Do World Action Models Need Test-time Future Imagination?.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Reference 19

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.541078Z digest=sha256:588e73f217a061edece18e10fafd39919fa7b23d84c359d5dff16e3d18345eae

Observation 11c4e4c6-a926-4775-bf56-133ebe0092a6 · outbound

This paper cites Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

Reference 20

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source=pdf_text observed=2026-08-14T04:35:47.584826Z digest=sha256:45e208df3259d598c795b1c6f1aeb22eaa97de0631c8cdd65ac9343bec3187d1

Observation 488ca4a5-ad2b-4c8c-a48c-7c33cbeda78d · outbound

This paper cites InConference on Robot Learning, 2165–2183.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InConference on Robot Learning, 2165–2183

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-14T04:35:49.194730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:47.609891Z digest=sha256:0054a09934de55ffb53ab948e19fe12314124d997f54253d78c32bfe5893bebb

Observation b71b92ca-0430-4660-9aea-300ec1d1886e · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Distilling the Knowledge in a Neural Network

Reference 2015

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no resolver link, observed 2026-08-14T04:35:47.074751Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:47.074751Z digest=sha256:08c5df0914815c662ceeed45f7dbac640bccd8a949de3e8e3767a68b259a1403

Observation a6896f9d-707b-44ee-ac73-33fe30ea8b02 · outbound

This paper cites Teacher exposure.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Teacher exposure

Reference 2016

Resolution
verified exact
raw_fallback, observed 2026-08-14T04:35:47.834747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:47.635198Z digest=sha256:50e6badcd18d2b5cd282e6e87324adbcd9541033200eaf4e662b582ab355be10

Observation 98d16b3a-36dc-4d46-b6b0-08748dba139a · outbound

This paper cites GenAug: Retargeting behaviors to unseen situations via Generative Augmentation.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models GenAug: Retargeting behaviors to unseen situations via Generative Augmentation

Reference 2023

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source=pdf_text observed=2026-08-14T04:35:46.854860Z digest=sha256:4fa78b5aa30fb4f1101a9d5c17addc57ac276b862c5ba70234ca09db8de53ac7

Observation 910d3a9d-cdf8-4dc2-90a8-859071bf9086 · outbound

This paper cites Large Video Planner Enables Generalizable Robot Control.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Large Video Planner Enables Generalizable Robot Control

Reference 2024

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source=pdf_text observed=2026-08-14T04:35:46.804867Z digest=sha256:dcf1d962c49819ff1130988ceb64a76ebd277b4112a23a2e01cb1317a14a54a5

Observation 2934eb91-1efd-4b23-bf60-80a81f1ce694 · outbound

This paper cites Motus: A Unified Latent Action World Model.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Motus: A Unified Latent Action World Model

Reference 2025

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:35:46.764750Z digest=sha256:5007c93ba306bcbdc8fb6a149188b5738fcc301d2ccbb04474bb0ef9fea08e99

Observation df9dc6d3-163d-4414-bedd-edea92f69728 · outbound

This paper cites Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models Supervise What Survives: Geometry-Guided VLA Adaptation from Synthetic Robot Videos

Reference 2026

Resolution
verified exact
local_arxiv, observed 2026-08-14T04:35:48.914997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:46.823017Z digest=sha256:9a5e3c4ddae6f02460f1045800e0b5a87862c73aaaa4acb268764e80bf58dacd

Observation a962c79b-0367-4ec9-b665-2e4528bab755 · outbound

This paper cites InThe Twelfth International Conference on Learning Representations.

Vid2WAM: Distilling Video Diffusion Priors into World Action Models InThe Twelfth International Conference on Learning Representations

Reference 9172

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verified fuzzy
raw_fallback, observed 2026-08-14T04:35:49.620081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T04:35:46.904753Z digest=sha256:6964a14b6cbbd1523af93ea3985270bc92a3432ffebc600f4d07d8f147f00497

Pith citing papers

No inbound Pith citation observations are available.