Paper Citation Record · LEDGER

Pre-Trained Video Generative Models as World Simulators

As of 14 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 17 inbound Pith citation observations for arXiv:2502.07825.

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

pith.paper-citation-record.v1
2502.07825 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:14:22.689279Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:03:15.907426Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

32 of 32 outbound references displayed

verified exact0
verified fuzzy5
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ee870583-4950-4112-96ea-2811eb2cbfba · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

Pre-Trained Video Generative Models as World Simulators Leveraging procedural generation to benchmark reinforcement learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:14:23.290690Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 90e4d933-041a-468f-ba3a-8af6d39daee1 · outbound

This paper cites an unresolved cited work.

Pre-Trained Video Generative Models as World Simulators Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:14:23.030767Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9663e929-9b0a-4cd9-83b4-1766451ae060 · outbound

This paper cites Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning.

Pre-Trained Video Generative Models as World Simulators Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.320381Z

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Observation 72503797-2ce4-4cca-ac27-8733d13e0d42 · outbound

This paper cites MaskViT: Masked Visual Pre-Training for Video Prediction.

Pre-Trained Video Generative Models as World Simulators MaskViT: Masked Visual Pre-Training for Video Prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.326566Z

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Observation 8bfdd34f-6ae0-47a7-b38c-cc55a8d82e67 · outbound

This paper cites Open-Sora Plan: Open-Source Large Video Generation Model.

Pre-Trained Video Generative Models as World Simulators Open-Sora Plan: Open-Source Large Video Generation Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.335380Z

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Observation d56149ff-b739-429b-ac47-ffd5932096d4 · outbound

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

Pre-Trained Video Generative Models as World Simulators Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.338761Z

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Observation a6b98859-75b8-48a6-b8b8-3ac1bec75311 · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

Pre-Trained Video Generative Models as World Simulators Latte: Latent Diffusion Transformer for Video Generation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.341684Z

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Observation 40edee54-cc02-41f5-ba4e-fc10ed38d4b8 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Pre-Trained Video Generative Models as World Simulators Playing Atari with Deep Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.344838Z

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Observation 5ab366d8-1b72-45cc-9dbe-5f16d7401add · outbound

This paper cites AVID: Adapting Video Diffusion Models to World Models.

Pre-Trained Video Generative Models as World Simulators AVID: Adapting Video Diffusion Models to World Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.384783Z

Source-reported events for the cited work

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Observation 680939e0-8980-497a-ba13-e3c4320eaaee · outbound

This paper cites Proximal Policy Optimization Algorithms.

Pre-Trained Video Generative Models as World Simulators Proximal Policy Optimization Algorithms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.421466Z

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Observation 5a1808cd-0788-4f09-8c14-6d73e8bb92f1 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Pre-Trained Video Generative Models as World Simulators Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.445941Z

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Observation dd921605-37e4-42f0-89ed-946fb3b1e98c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Pre-Trained Video Generative Models as World Simulators LLaMA: Open and Efficient Foundation Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.451787Z

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Observation 77c9f3c2-7c61-47be-90f1-3185a557a1cd · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

Pre-Trained Video Generative Models as World Simulators Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.454216Z

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Observation fa2a7b8e-940e-45cc-baf2-8041ae8be735 · outbound

This paper cites Pandora: Towards General World Model with Natural Language Actions and Video States.

Pre-Trained Video Generative Models as World Simulators Pandora: Towards General World Model with Natural Language Actions and Video States

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.461361Z

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Observation 332417a7-d18f-4d7d-bc51-6eea2aa96452 · outbound

This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Pre-Trained Video Generative Models as World Simulators VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.464157Z

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Observation baa8a1ec-19ac-41a4-b215-4e9d6c1662b8 · outbound

This paper cites Playable Game Generation.

Pre-Trained Video Generative Models as World Simulators Playable Game Generation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.470515Z

Source-reported events for the cited work

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Observation 60746027-81d9-4eb2-a7d1-62ecc94c5fc5 · outbound

This paper cites Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models.

Pre-Trained Video Generative Models as World Simulators Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.473732Z

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Observation aaf386df-6f94-4e3d-ad18-43773697db6c · outbound

This paper cites Decentralized Multi-Robot Line-of-Sight Connectivity Maintenance under Uncertainty.

Pre-Trained Video Generative Models as World Simulators Decentralized Multi-Robot Line-of-Sight Connectivity Maintenance under Uncertainty

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.476911Z

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Observation 88fc553c-18e6-4734-b55f-578407f7e3af · outbound

This paper cites an unresolved cited work.

Pre-Trained Video Generative Models as World Simulators Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:14:23.209385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3eee1721-3506-40a8-ab07-51f925605047 · outbound

This paper cites an unresolved cited work.

Pre-Trained Video Generative Models as World Simulators Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:14:23.189692Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f38cf5cd-2132-4fdd-a127-95682bdefd78 · outbound

This paper cites For our implementation of DWS, we utilized Open-Sora version 1.2 as our base model.

Pre-Trained Video Generative Models as World Simulators For our implementation of DWS, we utilized Open-Sora version 1.2 as our base model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:14:23.094037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fdba8374-4b4b-4d50-82b1-1538230ed792 · outbound

This paper cites In terms of the hyperparameter of DQN, we followed the default setting provided at https://github.com/ vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py.

Pre-Trained Video Generative Models as World Simulators In terms of the hyperparameter of DQN, we followed the default setting provided at https://github.com/ vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:14:23.200014Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 939260c3-2479-44fa-b1c5-9df8e07854eb · outbound

This paper cites DeepMind Control Suite.

Pre-Trained Video Generative Models as World Simulators DeepMind Control Suite

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.448687Z

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Observation c1ecb83b-8d3f-4f2e-b945-bf911d90bf4c · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Pre-Trained Video Generative Models as World Simulators Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.257646Z

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Observation 6d765c23-c578-4461-8bd4-e6a6e6b9c902 · outbound

This paper cites The Matrix: Infinite-Horizon World Generation with Real-Time Moving Control.

Pre-Trained Video Generative Models as World Simulators The Matrix: Infinite-Horizon World Generation with Real-Time Moving Control

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.323651Z

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Observation c5bae560-1813-43c0-b9a8-a13fbb97552c · outbound

This paper cites Diffusion Models Are Real-Time Game Engines.

Pre-Trained Video Generative Models as World Simulators Diffusion Models Are Real-Time Game Engines

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.457055Z

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Observation 43d21c00-7ba7-49f9-8db7-404214769029 · outbound

This paper cites Mastering Atari with Discrete World Models.

Pre-Trained Video Generative Models as World Simulators Mastering Atari with Discrete World Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.329614Z

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Observation 3707742c-cdc3-4651-bb08-faee1c8943ea · outbound

This paper cites Phasic policy gradient.

Pre-Trained Video Generative Models as World Simulators Phasic policy gradient

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:14:23.230309Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8906ac7b-4bc8-4044-a0a4-e84d29743b69 · outbound

This paper cites Learning Interactive Real-World Simulators.

Pre-Trained Video Generative Models as World Simulators Learning Interactive Real-World Simulators

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.467323Z

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Observation 6cb7b33f-d77e-4a22-8de2-168511b246ed · outbound

This paper cites https://iclr-blog-track.github.io/2022/03/25/ppo- implementation-details/.

Pre-Trained Video Generative Models as World Simulators https://iclr-blog-track.github.io/2022/03/25/ppo- implementation-details/

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:14:23.220035Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9856ffde-2b26-4825-8889-93ed73b16072 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Pre-Trained Video Generative Models as World Simulators Movie Gen: A Cast of Media Foundation Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.347722Z

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Observation 6873f18a-cb3b-4ab8-bc41-9480163c524d · outbound

This paper cites Diffusion for World Modeling: Visual Details Matter in Atari.

Pre-Trained Video Generative Models as World Simulators Diffusion for World Modeling: Visual Details Matter in Atari

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T15:14:22.229876Z

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Pith citing papers

Observation 8b281877-bc57-4655-8c14-5222c3132691 · inbound

Long-Context State-Space Video World Models cites this paper.

Long-Context State-Space Video World Models Pre-Trained Video Generative Models as World Simulators

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:15.907426Z

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Observation 2ec664b1-d70f-464c-b996-844981a4de2a · inbound

Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers cites this paper.

Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers Pre-Trained Video Generative Models as World Simulators

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:15.192689Z

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Observation ccb6ddd4-fa94-48af-b038-c88b46a2c13b · inbound

WorldVLA: Towards Autoregressive Action World Model cites this paper.

WorldVLA: Towards Autoregressive Action World Model Pre-Trained Video Generative Models as World Simulators

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:57:08.130802Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 44bf1fbe-ec70-4b5e-9f81-f11d1edc7221 · inbound

RoboScape: Physics-informed Embodied World Model cites this paper.

RoboScape: Physics-informed Embodied World Model Pre-Trained Video Generative Models as World Simulators

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:11.850755Z

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Observation a72f13bd-15f5-4954-abc2-fc2f681ef209 · inbound

Can Your Model Separate Yolks with a Water Bottle? Benchmarking Physical Commonsense Understanding in Video Generation Models cites this paper.

Can Your Model Separate Yolks with a Water Bottle? Benchmarking Physical Commonsense Understanding in Video Generation Models Pre-Trained Video Generative Models as World Simulators

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:27:01.234367Z

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Observation 9389cae8-231a-48b5-b0be-e3f2ef303831 · inbound

Ctrl-World: A Controllable Generative World Model for Robot Manipulation cites this paper.

Ctrl-World: A Controllable Generative World Model for Robot Manipulation Pre-Trained Video Generative Models as World Simulators

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-16T01:14:10.406401Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ff7b020-b15b-454c-89b4-95c650b2b7f4 · inbound

Co-Evolving Latent Action World Models cites this paper.

Co-Evolving Latent Action World Models Pre-Trained Video Generative Models as World Simulators

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T02:52:21.672604Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9931c735-97cd-4f46-81fc-c2c37a47fca0 · inbound

Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation cites this paper.

Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation Pre-Trained Video Generative Models as World Simulators

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:17:55.157621Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4fd3a81b-c6f1-4e13-9c4e-7d369e8ca2ba · inbound

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling cites this paper.

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling Pre-Trained Video Generative Models as World Simulators

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T15:46:07.784278Z

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Observation b1bd8532-1cd8-4084-aab3-8968e0dece78 · inbound

UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving cites this paper.

UniDrive-WM: Unified Understanding, Planning and Generation World Model for Autonomous Driving Pre-Trained Video Generative Models as World Simulators

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T12:06:26.663155Z

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Observation 6a49fd73-fa0e-47c9-a702-09754c141124 · inbound

Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild cites this paper.

Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild Pre-Trained Video Generative Models as World Simulators

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:24:10.642590Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 451cbc93-4de4-4b54-85ed-481ef0313753 · inbound

Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement cites this paper.

Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement Pre-Trained Video Generative Models as World Simulators

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:51:07.886021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b4dc9c1-d2da-4d6d-8d23-86abc5e4610c · inbound

Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement cites this paper.

Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement Pre-Trained Video Generative Models as World Simulators

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T04:20:58.445345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 18da8698-da7c-4f7c-ba0d-8923f9c8c58a · inbound

DiLA: Disentangled Latent Action World Models cites this paper.

DiLA: Disentangled Latent Action World Models Pre-Trained Video Generative Models as World Simulators

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:38:56.309993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91be3498-76fc-4700-a00a-06b14cba4d1e · inbound

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization cites this paper.

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization Pre-Trained Video Generative Models as World Simulators

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.596078Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1b10620f-daab-4c2f-b89a-4966c21ce3e8 · inbound

World Model Self-Distillation: Training World Models to Solve General Tasks cites this paper.

World Model Self-Distillation: Training World Models to Solve General Tasks Pre-Trained Video Generative Models as World Simulators

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-27T10:20:48.824778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dce3e5b2-6690-4974-ba4b-59cdfde4c4c8 · inbound

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL cites this paper.

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL Pre-Trained Video Generative Models as World Simulators

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T14:50:11.679991Z

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Unavailable: canonical work link unavailable.