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Paper Citation Record · LEDGER

MiniWorld: Democratizing the Training of Video World Models from Scratch

As of 20 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.01127.

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

pith.paper-citation-record.v1
2608.01127 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:31:25.374457Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cbc58aa8-0141-4ff4-b2c4-895a31a29248 · outbound

This paper cites Happyoyster: Real-time interactive world model, 2026.

MiniWorld: Democratizing the Training of Video World Models from Scratch Happyoyster: Real-time interactive world model, 2026

Reference 1

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T00:31:23.910817Z digest=sha256:c978089cdd8059fc5f942b294ff9c1f604f0af3b744a072b5aa401bfb3e00962

Observation f0d50057-9631-49b1-879a-f3b6a27c53da · outbound

This paper cites Diffusion forcing: Next-token prediction meets full-sequence diffusion.

MiniWorld: Democratizing the Training of Video World Models from Scratch Diffusion forcing: Next-token prediction meets full-sequence diffusion

Reference 2

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T00:31:23.987597Z digest=sha256:bbce0e1f4043787b49e5c4753c2718194fd8cf14f9248a16c06c52eb16103ecc

Observation 731e9231-1287-4b31-b08b-968fdf50857f · outbound

This paper cites Dreamx-world 1.0: A general-purpose interactive world model, 2026.

MiniWorld: Democratizing the Training of Video World Models from Scratch Dreamx-world 1.0: A general-purpose interactive world model, 2026

Reference 3

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source=pdf_text observed=2026-08-06T00:31:24.067488Z digest=sha256:0e977760a7a68e0011ae77b5a457c6554feab2345886204b1b5e0b4374b04623

Observation 058eb863-9531-4dc7-84a8-dac84f07e640 · outbound

This paper cites Mirage 2: Ai-native ugc game engine powered by real-time world models, 2025.

MiniWorld: Democratizing the Training of Video World Models from Scratch Mirage 2: Ai-native ugc game engine powered by real-time world models, 2025

Reference 4

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source=pdf_text observed=2026-08-06T00:31:24.152832Z digest=sha256:a651bcd7a77e530ed3fed0f9277ebedc678215bd6066cf207c2e6d06870f7e45

Observation 21b8cbac-3fe3-4e03-934e-8ee40257cc3e · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

MiniWorld: Democratizing the Training of Video World Models from Scratch Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 5

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source=pdf_text observed=2026-08-06T00:31:24.173735Z digest=sha256:bcf45a9ab95c182d09deaaaccfa95f64ba12e2ff7935f174f0f278bc21f406d2

Observation b5138675-3957-4e47-ba3f-3a01b3ffe7a2 · outbound

This paper cites Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing.

MiniWorld: Democratizing the Training of Video World Models from Scratch Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing

Reference 6

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source=pdf_text observed=2026-08-06T00:31:24.245292Z digest=sha256:2ba76b92636eb45c44ab07fa492ce78605957c7e4094322ac71fe17ea72e0e5f

Observation 61dcc170-0bc2-40ec-8404-2bfa77f1a582 · outbound

This paper cites Dreamdojo: A generalist robot world model from large-scale human videos,.

MiniWorld: Democratizing the Training of Video World Models from Scratch Dreamdojo: A generalist robot world model from large-scale human videos,

Reference 7

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source=pdf_text observed=2026-08-06T00:31:24.406621Z digest=sha256:8df1cdc74374d1ac00b6e9779d4691e3063454d20f75ab03792053fa985c6299

Observation c40dc53d-42b7-42a5-b055-a18489c0c6b5 · outbound

This paper cites Infinite Worlds with Versatile Interactions.

MiniWorld: Democratizing the Training of Video World Models from Scratch Infinite Worlds with Versatile Interactions

Reference 8

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source=pdf_text observed=2026-08-06T00:31:24.672823Z digest=sha256:5a8a65b02d375f0be9f87e749939ec6d22c490afbcdd636e7f3a57ed0efec24f

Observation b465b94a-3ff4-47b7-b412-e3957b150445 · outbound

This paper cites Genie 3: A new frontier for world models, 2025.

MiniWorld: Democratizing the Training of Video World Models from Scratch Genie 3: A new frontier for world models, 2025

Reference 9

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source=pdf_text observed=2026-08-06T00:31:24.800715Z digest=sha256:a91c612a01568e988c4a5518dbb2e5bff33a909756303e57f759fa4da39cb7a9

Observation bca26e8d-ee06-41e7-941c-674314856bdb · outbound

This paper cites Recurrent world models facilitate policy evolution.

MiniWorld: Democratizing the Training of Video World Models from Scratch Recurrent world models facilitate policy evolution

Reference 10

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source=pdf_text observed=2026-08-06T00:31:24.868353Z digest=sha256:5c87eb44b6afafe2054bd48939ae8797c1704f4051a121ab0d43665071d68b80

Observation fbf3d8b3-e956-4eed-bac8-9479e4737a04 · outbound

This paper cites Mastering Diverse Domains through World Models.

MiniWorld: Democratizing the Training of Video World Models from Scratch Mastering Diverse Domains through World Models

Reference 11

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source=pdf_text observed=2026-08-06T00:31:24.880506Z digest=sha256:cb44dca9176f4c6919e230b811d59c286d7d633ecebe3231328bd90a088950bd

Observation d7155246-518b-42bc-badb-4349bc5b93ac · outbound

This paper cites Matrix-game 2.0: An open-source real-time and streaming interactive world model.

MiniWorld: Democratizing the Training of Video World Models from Scratch Matrix-game 2.0: An open-source real-time and streaming interactive world model

Reference 12

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source=pdf_text observed=2026-08-06T00:31:25.042552Z digest=sha256:73d6bb5ccaf06c17f166607e50f0a5d3a861358d97af2e462dfbc647a867b0b6

Observation 5d4966a0-ae5c-4fa6-8f0f-ef66d9c39f86 · outbound

This paper cites Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion.

MiniWorld: Democratizing the Training of Video World Models from Scratch Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Reference 13

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source=pdf_text observed=2026-08-06T00:31:25.062899Z digest=sha256:233e0b01bb7d68af5fac4daa08b30f9e42948421764651f4edd327e0f69c6d39

Observation f415b161-0b1f-46c3-aef6-06928f006bbc · outbound

This paper cites Muon: An optimizer for the hidden layers of neural networks, 2024.

MiniWorld: Democratizing the Training of Video World Models from Scratch Muon: An optimizer for the hidden layers of neural networks, 2024

Reference 14

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source=pdf_text observed=2026-08-06T00:31:25.150868Z digest=sha256:a0d96c83f1001ef9087fa93dc4dc96865b8e4ac8c0c6faacc57c28e78c89be2d

Observation 617c3131-951e-4e46-b37c-cdad903f316a · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

MiniWorld: Democratizing the Training of Video World Models from Scratch DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 15

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source=pdf_text observed=2026-08-06T00:31:25.206611Z digest=sha256:edbdd3b7eeea9bc01c8732e581f8ac45e5df3a4ac479310ff1ea065c234f49ef

Observation cbad3fdb-c154-48a9-afcf-51ff4576d6a3 · outbound

This paper cites Advancing Open-source World Models.

MiniWorld: Democratizing the Training of Video World Models from Scratch Advancing Open-source World Models

Reference 16

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source=pdf_text observed=2026-08-06T00:31:25.241964Z digest=sha256:a8995c858c743bf6947f09437ff21cf98c0686df0b2d063d634600a54d42e2bc

Observation e9ef80b9-54ec-4183-88ac-6f02fc4b855c · outbound

This paper cites an unresolved cited work.

MiniWorld: Democratizing the Training of Video World Models from Scratch Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-06T00:31:25.258298Z digest=sha256:d3d226098df3f436a6080eea71aa76845d2273245874bf1642998ba7c8100d7e

Observation b77df3a8-658a-4c8e-bb64-d01a20c88621 · outbound

This paper cites Rolling Forcing: Autoregressive Long Video Diffusion in Real Time.

MiniWorld: Democratizing the Training of Video World Models from Scratch Rolling Forcing: Autoregressive Long Video Diffusion in Real Time

Reference 18

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source=pdf_text observed=2026-08-06T00:31:25.268816Z digest=sha256:7e5bf71dd1d80e18114b3e99e17499ae485afdcaad5b841a5263e74357d5147c

Observation 0aa437d0-6bba-4a90-a02d-677608ea4e76 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

MiniWorld: Democratizing the Training of Video World Models from Scratch Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 19

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source=pdf_text observed=2026-08-06T00:31:25.274373Z digest=sha256:438f19f286a86af39a128ea8e46526639cdd631486694ab767014e614c327b0b

Observation 51fa5e8c-6c08-4220-9cb2-d0df40f01b63 · outbound

This paper cites Yume-1.5: A text-controlled interactive world generation model.

MiniWorld: Democratizing the Training of Video World Models from Scratch Yume-1.5: A text-controlled interactive world generation model

Reference 20

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source=pdf_text observed=2026-08-06T00:31:25.285453Z digest=sha256:ed9c095441c81f1627d4da7c41e2597a5c66570333a49d9ad74c00c3f2852bda

Observation 98e42777-bae2-4b58-9cfd-c48070003c92 · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

MiniWorld: Democratizing the Training of Video World Models from Scratch Cosmos World Foundation Model Platform for Physical AI

Reference 21

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source=pdf_text observed=2026-08-06T00:31:25.296209Z digest=sha256:9df9ba82af84c6bc640ac1a425a7dad57aedfb62ff0118b7b28f6c28469c507b

Observation afa088ad-5dfc-43a0-99bd-6a6a5ba5d862 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

MiniWorld: Democratizing the Training of Video World Models from Scratch Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 22

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source=pdf_text observed=2026-08-06T00:31:25.279925Z digest=sha256:064368aad30e14b4a398b46a1ee96b2b4fcc1f0971255e449308d5108f2fb5fc

Observation 26c7182a-fb66-4545-8ea2-b494c6af9bfc · outbound

This paper cites Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models, 2026.

MiniWorld: Democratizing the Training of Video World Models from Scratch Worldarena: A unified benchmark for evaluating perception and functional utility of embodied world models, 2026

Reference 23

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Observation 73ca17f1-2708-45e3-bdb0-7c9da4006325 · outbound

This paper cites an unresolved cited work.

MiniWorld: Democratizing the Training of Video World Models from Scratch Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-06T00:31:25.290598Z digest=sha256:4bfa7d6804a66049b38650219693e60ded2e3f81576fc1ba0269bff7a000898f

Observation a35aaa46-ac41-445c-a54c-c7a6a565090d · outbound

This paper cites SkyReels-V2: Infinite-length Film Generative Model.

MiniWorld: Democratizing the Training of Video World Models from Scratch SkyReels-V2: Infinite-length Film Generative Model

Reference 25

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source=pdf_text observed=2026-08-06T00:31:25.316697Z digest=sha256:0cf8eeae9a59747aebe983f3a305ea229a07cebd50d2f78b07126c2656d6f9fe

Observation e8f78a9b-be23-4a5d-b1d7-111107f20558 · outbound

This paper cites MAGI-1: Autoregressive Video Generation at Scale.

MiniWorld: Democratizing the Training of Video World Models from Scratch MAGI-1: Autoregressive Video Generation at Scale

Reference 26

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source=pdf_text observed=2026-08-06T00:31:25.300662Z digest=sha256:a25ff9d242f89ed7bf96ecd1070a962d5db3c8999e83cf82e93088b00881d7e1

Observation 35a35884-25d6-47c5-a335-e6342a097fdc · outbound

This paper cites AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion.

MiniWorld: Democratizing the Training of Video World Models from Scratch AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion

Reference 27

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source=pdf_text observed=2026-08-06T00:31:25.329247Z digest=sha256:dd3d53ef091a1130714e9f2ffffc65f61d58b52226717a4876a7a4fc1c6f9572

Observation cd5d4877-5e94-419a-851f-6a94c17bd35b · outbound

This paper cites Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory.

MiniWorld: Democratizing the Training of Video World Models from Scratch Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory

Reference 28

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source=pdf_text observed=2026-08-06T00:31:25.310303Z digest=sha256:9bb7ed5d4df6d60a21ec4ec18b2bbb6f8b048198bf576c25f1bad3fb8f48276c

Observation 8a377a66-b2d5-4a64-86da-15046b744e36 · outbound

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

MiniWorld: Democratizing the Training of Video World Models from Scratch Wan: Open and Advanced Large-Scale Video Generative Models

Reference 29

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source=pdf_text observed=2026-08-06T00:31:25.343155Z digest=sha256:d3d2b0257408b1b29c78aa2a5d2300bdb2b4e0a068938f4b97448c855f0ed50b

Observation b175f980-3f52-4bbb-8de1-9a94c510d53f · outbound

This paper cites History-Guided Video Diffusion.

MiniWorld: Democratizing the Training of Video World Models from Scratch History-Guided Video Diffusion

Reference 30

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source=pdf_text observed=2026-08-06T00:31:25.322462Z digest=sha256:4741633aba1be246c0ea5f25d95aa100135cf55496c221e79f6451e1d1d72f43

Observation 3dbc3706-8dfc-4be6-805d-30bf7458f545 · outbound

This paper cites Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling.

MiniWorld: Democratizing the Training of Video World Models from Scratch Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling

Reference 31

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source=pdf_text observed=2026-08-06T00:31:25.354409Z digest=sha256:3e3ba7e573e1a5be57f26f01043ad44f9b3b1a08c29a9610e012c8b74690f501

Observation cace3272-9a9e-4afc-ac42-96e4be56eec0 · outbound

This paper cites Hy-world 1.5: A systematic framework for interactive world modeling with real-time latency and geometric consistency, 2025.

MiniWorld: Democratizing the Training of Video World Models from Scratch Hy-world 1.5: A systematic framework for interactive world modeling with real-time latency and geometric consistency, 2025

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T00:31:25.337557Z digest=sha256:45129b13b95e02fd4ee35e78184805f5b2150a874ab8bd70e255fe9f913ccafe

Observation 5cb37f35-81c7-4ce6-847e-d9cf44d1ac8c · outbound

This paper cites The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.

MiniWorld: Democratizing the Training of Video World Models from Scratch The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Reference 33

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source=pdf_text observed=2026-08-06T00:31:25.363220Z digest=sha256:f41012804c20a4f95b1ea1f745415a67d7e497ba490f91b3f0e0d474c3c114f9

Observation 1d335ff7-24bc-44c3-8429-e029d9b41fe8 · outbound

This paper cites Bovik, Hamid R.

MiniWorld: Democratizing the Training of Video World Models from Scratch Bovik, Hamid R

Reference 34

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-06T00:31:25.349077Z digest=sha256:51d739d58f09955bc21a33fa96975717a5e4cec0b07b250450f74c2e6308321d

Observation bb376b70-c506-4d02-ab64-c9fab1b8aa34 · outbound

This paper cites Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation.

MiniWorld: Democratizing the Training of Video World Models from Scratch Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation

Reference 35

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source=pdf_text observed=2026-08-06T00:31:25.374457Z digest=sha256:397c3250d494cf374ebb261c85fe24556c616bb5412ee052c0f259894da56104

Observation 9a82f494-c5e3-450e-8345-ed8d8bedd4a8 · outbound

This paper cites From slow bidirectional to fast autoregressive video diffusion models.

MiniWorld: Democratizing the Training of Video World Models from Scratch From slow bidirectional to fast autoregressive video diffusion models

Reference 36

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Observation 560dc427-5a15-4cb9-bf95-854c993fb53a · outbound

This paper cites Stereo Magnification: Learning View Synthesis using Multiplane Images.

MiniWorld: Democratizing the Training of Video World Models from Scratch Stereo Magnification: Learning View Synthesis using Multiplane Images

Reference 38

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Observation 571b2f16-c7f0-4004-900a-8bd92034bbcc · outbound

This paper cites Flow Matching for Generative Modeling.

MiniWorld: Democratizing the Training of Video World Models from Scratch Flow Matching for Generative Modeling

Reference 2023

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Observation b661d539-58d7-4a8c-88c8-84d554eb127a · outbound

This paper cites DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos.

MiniWorld: Democratizing the Training of Video World Models from Scratch DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos

Reference 2026

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