DMWM: Dual-Mind World Model with Long-Term Imagination

• URL: http://arxiv.org/abs/2502.07591v2
• Date: Thu, 23 Oct 2025 05:56:53 GMT
• Title: DMWM: Dual-Mind World Model with Long-Term Imagination
• Authors: Lingyi Wang, Rashed Shelim, Walid Saad, Naren Ramakrishnan,
• Abstract summary: We propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency.<n>The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite.
• Score: 43.39205414684229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform long-term imagination tasks due to the accumulation of prediction errors. Inspired by the dual-process theory of human cognition, we propose a novel dual-mind world model (DMWM) framework that integrates logical reasoning to enable imagination with logical consistency. DMWM is composed of two components: an RSSM-based System 1 (RSSM-S1) component that handles state transitions in an intuitive manner and a logic-integrated neural network-based System 2 (LINN-S2) component that guides the imagination process through hierarchical deep logical reasoning. The inter-system feedback mechanism is designed to ensure that the imagination process follows the logical rules of the real environment. The proposed framework is evaluated on benchmark tasks that require long-term planning from the DMControl suite. Extensive experimental results demonstrate that the proposed framework yields significant improvements in terms of logical coherence, trial efficiency, data efficiency and long-term imagination over the state-of-the-art world models.

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