EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling | Paper Citation Record

Observation 4df121f6-b5c0-4ea7-a8ce-6a5308aa4084 · outbound

This paper cites Recurrent world models facilitate policy evolution.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Recurrent world models facilitate policy evolution

Reference 1

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Observation 0decbfd3-2741-4452-aa3c-df6721d4089a · outbound

This paper cites Mastering Diverse Domains through World Models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mastering Diverse Domains through World Models

Reference 2

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Observation 83231a6d-9685-41ee-8843-d8369a015480 · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model.Nature, 2020.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mastering atari, go, chess and shogi by planning with a learned model.Nature, 2020

Reference 3

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This paper cites Mastering atari games with limited data.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mastering atari games with limited data

Reference 4

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Observation eb50185c-8934-4c62-a3c5-f1b28966b818 · outbound

This paper cites Day- dreamer: World models for physical robot learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Day- dreamer: World models for physical robot learning

Reference 5

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Observation 74699c8f-cecc-46cd-8b21-9e2df6465bb5 · outbound

This paper cites Dream to control: Learning behaviors by latent imagination.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Dream to control: Learning behaviors by latent imagination

Reference 6

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Observation 0cc5a143-e862-4360-aeb9-e3b3375a471f · outbound

This paper cites Mastering atari with discrete world models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mastering atari with discrete world models

Reference 7

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Observation 3fd1d411-1b04-40fe-8d20-09f090ad447f · outbound

This paper cites Diffu- sion for world modeling: Visual details matter in atari.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Diffu- sion for world modeling: Visual details matter in atari

Reference 8

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Observation 6ff22636-4643-4456-903b-952f9619da2f · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Efficiently modeling long sequences with structured state spaces

Reference 9

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Observation 756fff41-9c54-4ae1-84c4-f2abfc0ebb88 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling On the parameterization and initialization of diagonal state space models

Reference 10

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Observation 2017cfae-cb54-4e17-862b-a06779242f84 · outbound

This paper cites Smith, Andrew Warrington, and Scott Linderman.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Smith, Andrew Warrington, and Scott Linderman

Reference 11

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Observation d3a908ba-db65-4624-aba8-72eed72139fd · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mamba: Linear-time sequence modeling with selective state spaces

Reference 12

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Observation 132e978a-b594-463e-996b-708c9c8d3b33 · outbound

This paper cites Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Transformers are ssms: Generalized models and efficient algorithms through structured state space duality

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:05.051849Z

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Observation 274787c8-5086-4420-b86d-6c9a1a1d1447 · outbound

This paper cites Mas- tering memory tasks with world models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Mas- tering memory tasks with world models

Reference 14

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Observation 32f7d157-a061-4a80-a13e-1d0390ee7fc3 · outbound

This paper cites Model based reinforcement learning for atari.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Model based reinforcement learning for atari

Reference 15

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Observation d004d802-1126-413d-bf85-fb69c6fe182a · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks

Reference 16

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Observation 4b9a6c6c-3d33-41f8-9fb1-45228a89be04 · outbound

This paper cites Benchmarking the spectrum of agent capabilities.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Benchmarking the spectrum of agent capabilities

Reference 17

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Observation a1e3ca45-420a-4508-84b3-1c009bd287d0 · outbound

This paper cites ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

Reference 18

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Observation 2ffb24b7-54e1-4ee5-99d6-6e46e080d628 · outbound

This paper cites Denoising diffusion probabilistic models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Denoising diffusion probabilistic models

Reference 19

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This paper cites Denoising diffusion implicit models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Denoising diffusion implicit models

Reference 20

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Observation d8571c75-ca07-427e-9390-80a3c199b390 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Generative modeling by estimating gradients of the data distribution

Reference 21

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Observation 2e311af2-d58b-46cb-986d-c7018aa49872 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Score-based generative modeling through stochastic differential equations

Reference 22

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Observation 52134006-ab7b-49f3-9e90-4d9cad9f8e67 · outbound

This paper cites Tenenbaum, Sander Dieleman, and et al.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Tenenbaum, Sander Dieleman, and et al

Reference 23

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Observation 55157e0c-50d7-485a-9925-e445829c2dbf · outbound

This paper cites Diffusion policies as an expressive policy class for offline reinforcement learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Diffusion policies as an expressive policy class for offline reinforcement learning

Reference 24

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Observation a97155ef-24d1-4e76-bfbc-d9b8bb95f071 · outbound

This paper cites Tenenbaum Joshua, S.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Tenenbaum Joshua, S

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.778065Z

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Observation fea711b7-135d-4cb3-9c14-fefdcfce05dc · outbound

This paper cites Imitating human behaviour with diffusion models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Imitating human behaviour with diffusion models

Reference 26

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Observation 92d222ab-d74a-4223-9077-c673a18a3497 · outbound

This paper cites Tenenbaum Joshua, and Levine Sergey.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Tenenbaum Joshua, and Levine Sergey

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.719651Z

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Observation 6199ab25-5cde-46c3-9576-6d86b4c21dac · outbound

This paper cites Adaptd- iffuser: Diffusion models as adaptive self-evolving planners.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Adaptd- iffuser: Diffusion models as adaptive self-evolving planners

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.677332Z

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Observation 782329b9-5e0a-4edb-a75b-8efdbd00b494 · outbound

This paper cites Extracting reward functions from diffusion models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Extracting reward functions from diffusion models

Reference 29

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Observation 9b6038a6-0c0c-4032-92f5-7bde9980f5ec · outbound

This paper cites Metadiffuser: Diffusion model as conditional planner for offline meta-rl.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Metadiffuser: Diffusion model as conditional planner for offline meta-rl

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.625902Z

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Observation 6cd035e8-9d1d-4343-89dd-d4935bc9228d · outbound

This paper cites Synthetic experience replay.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Synthetic experience replay

Reference 31

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Observation 41152686-902c-44a2-8b4e-93b46d122798 · outbound

This paper cites Transformer-based world models are happy with 100k interactions.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Transformer-based world models are happy with 100k interactions

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.575970Z

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Observation 7dd9ed3a-2df4-4c7b-8cf4-027d01074067 · outbound

This paper cites STORM: Efficient stochas- tic transformer based world models for reinforcement learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling STORM: Efficient stochas- tic transformer based world models for reinforcement learning

Reference 33

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Observation d254397e-c752-4e41-a2b4-1563b0a4eb3c · outbound

This paper cites Attention is all you need.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Attention is all you need

Reference 34

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Observation c80d240c-8c15-4bcd-a2e2-6517f36b2d19 · outbound

This paper cites Transformers are sample-efficient world models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Transformers are sample-efficient world models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.499638Z

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Observation 93e7b57b-766f-45e4-ba02-67d5137d9638 · outbound

This paper cites Learning to Simulate Dynamic Environments with GameGAN.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Learning to Simulate Dynamic Environments with GameGAN

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.469526Z

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This paper cites Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, and Tim Rocktäschel.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, and Tim Rocktäschel

Reference 37

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Observation f67418a1-b8c7-479e-8dd0-b8b3df14621d · outbound

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

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Diffusion Models Are Real-Time Game Engines

Reference 38

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This paper cites Gaia-1: A generative world model for autonomous driving, 2023.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Gaia-1: A generative world model for autonomous driving, 2023

Reference 39

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Observation 7ea2b069-fa0a-4338-bfff-7b27acd73999 · outbound

This paper cites Gaia-2: A controllable multi-view generative world model for autonomous driving, 2025.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Gaia-2: A controllable multi-view generative world model for autonomous driving, 2025

Reference 40

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This paper cites Xing, and Zhiting Hu.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Xing, and Zhiting Hu

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.366528Z

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Observation 4fc811c2-9854-40e8-a315-85d528ffc3b5 · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Hippo: Recurrent memory with optimal polynomial projections

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.341967Z

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Observation 1ef1388d-b81b-46c0-a34b-df87fa21ab01 · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Diagonal state spaces are as effective as structured state spaces

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.315625Z

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Observation 60c57300-b502-4a15-899e-7119f4849896 · outbound

This paper cites Liquid structural state-space models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Liquid structural state-space models

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.289110Z

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Observation 2afea137-90bb-4e7e-9df7-cc240d21bba0 · outbound

This paper cites Structured state space models for in-context reinforcement learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Structured state space models for in-context reinforcement learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.243282Z

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Observation d6c9d9ff-fd5d-457c-ba0e-ad9642743ff5 · outbound

This paper cites Decision mamba: Reinforcement learning via hybrid selective sequence modeling.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Decision mamba: Reinforcement learning via hybrid selective sequence modeling

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.215162Z

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This paper cites Decision trans- former: Reinforcement learning via sequence modeling.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Decision trans- former: Reinforcement learning via sequence modeling

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.186813Z

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Observation 1d2e67ba-cde8-41b4-8b94-ff0fb8e09ad8 · outbound

This paper cites Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient

Reference 48

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Observation 3f79d976-cce3-4c7a-9de6-fa6b9190cef8 · outbound

This paper cites Optimal control of markov processes with incomplete state information.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Optimal control of markov processes with incomplete state information

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.161594Z

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Observation 9be24e13-7d4b-443f-8cbd-4a8c81174a54 · outbound

This paper cites an unresolved cited work.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Unresolved cited work

Reference 50

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Observation 7d16d97f-826d-4cd7-b752-d15a240491cb · outbound

This paper cites Improving the Closed-Loop Tracking Performance Using the First-Order Hold Sensing Technique with Experiments.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Improving the Closed-Loop Tracking Performance Using the First-Order Hold Sensing Technique with Experiments

Reference 51

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Observation 5eb44454-6a0a-4977-a0bc-b741d3ea605c · outbound

This paper cites Hochreiter and J.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Hochreiter and J

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:04.109185Z

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This paper cites Chung, C.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Chung, C

Reference 53

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This paper cites Learning semantic- aware normalization for generative adversarial networks.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Learning semantic- aware normalization for generative adversarial networks

Reference 54

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EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Harmonydream: Task harmonization inside world models

Reference 55

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This paper cites Dueling network architectures for deep reinforcement learning.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Dueling network architectures for deep reinforcement learning

Reference 56

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This paper cites Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems (NeurIPS), 2021.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems (NeurIPS), 2021

Reference 57

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This paper cites Towards efficient world models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Towards efficient world models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.949042Z

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This paper cites Deep unsuper- vised learning using nonequilibrium thermodynamics.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Deep unsuper- vised learning using nonequilibrium thermodynamics

Reference 59

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This paper cites Diffusion models beat gans on image synthesis.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Diffusion models beat gans on image synthesis

Reference 60

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This paper cites Improved techniques for training score-based generative models.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Improved techniques for training score-based generative models

Reference 61

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This paper cites Denoising likelihood score matching for con- ditional score-based data generation.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Denoising likelihood score matching for con- ditional score-based data generation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.858324Z

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Observation 6ad4d1f8-b260-42de-bb27-e19a160b54cc · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 63

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This paper cites Multitask learning.Machine Learning, 28:41–75, 1997.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Multitask learning.Machine Learning, 28:41–75, 1997

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.801575Z

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Observation fd0bf9d0-18b8-4fd8-940c-4422a936967c · outbound

This paper cites Improving token-based world models with parallel observation prediction.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Improving token-based world models with parallel observation prediction

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.761918Z

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Observation bb8d215f-4db7-4feb-92b4-aec620109750 · outbound

This paper cites Learning transformer-based world models with contrastive predictive coding.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Learning transformer-based world models with contrastive predictive coding

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.733991Z

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Observation fd0fc18c-5bb4-4033-96db-52e4b6be1b34 · outbound

This paper cites Parallelizing model-based reinforce- ment learning over the sequence length.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Parallelizing model-based reinforce- ment learning over the sequence length

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T18:58:03.679552Z

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Observation 26353fe6-af1c-49fb-8424-2239025a5653 · outbound

This paper cites Sutton and Andrew G.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Sutton and Andrew G

Reference 68

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Observation b90ee21e-02d5-4735-bb06-c5898f3be68b · outbound

This paper cites an unresolved cited work.

EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling Unresolved cited work

Reference 2025

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