Enhancing Memory and Imagination Consistency in Diffusion-based World Models via Linear-Time Sequence Modeling

• URL: http://arxiv.org/abs/2502.00466v1
• Date: Sat, 01 Feb 2025 15:49:59 GMT
• Title: Enhancing Memory and Imagination Consistency in Diffusion-based World Models via Linear-Time Sequence Modeling
• Authors: Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun, Chun-Yi Lee,
• Abstract summary: We introduce EDELINE, a novel framework that integrates diffusion models with linear-time state space models to enhance memory retention and temporal consistency. Our results across multiple benchmarks demonstrate EDELINE's superiority and robustness over prior baselines in long-horizon tasks.
• Score: 8.250616459360684
• License:
• Abstract: World models are crucial for enabling agents to simulate and plan within environments, yet existing approaches struggle with long-term dependencies and inconsistent predictions. We introduce EDELINE, a novel framework that integrates diffusion models with linear-time state space modelsto enhance memory retention and temporal consistency. EDELINE employs a recurrent embedding module based on Mamba SSMs for processing unbounded sequences, a unified architecture for joint reward and termination prediction, and dynamic loss harmonization to balance multi-task learning. Our results across multiple benchmarks demonstrate EDELINE's superiority and robustness over prior baselines in long-horizon tasks.

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