Planning with Unified Multimodal Models

• URL: http://arxiv.org/abs/2509.23014v1
• Date: Sat, 27 Sep 2025 00:13:13 GMT
• Title: Planning with Unified Multimodal Models
• Authors: Yihao Sun, Zhilong Zhang, Yang Yu, Pierre-Luc Bacon,
• Abstract summary: We argue that unified multimodal models (UMMs) have greater potential for decision-making by enabling reasoning through generated visual content.<n>Within this framework, a single model simultaneously serves as the policy, dynamics model, and value function.<n>We present a novel approach self-discriminated filtering, where the generative model serves as a self-discriminator to filter out invalid dynamics predictions.
• Score: 27.156039833076324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the powerful reasoning capabilities of large language models (LLMs) and vision-language models (VLMs), many recent works have explored using them for decision-making. However, most of these approaches rely solely on language-based reasoning, which limits their ability to reason and make informed decisions. Recently, a promising new direction has emerged with unified multimodal models (UMMs), which support both multimodal inputs and outputs. We believe such models have greater potential for decision-making by enabling reasoning through generated visual content. To this end, we propose Uni-Plan, a planning framework built on UMMs. Within this framework, a single model simultaneously serves as the policy, dynamics model, and value function. In addition, to avoid hallucinations in dynamics predictions, we present a novel approach self-discriminated filtering, where the generative model serves as a self-discriminator to filter out invalid dynamics predictions. Experiments on long-horizon planning tasks show that Uni-Plan substantially improves success rates compared to VLM-based methods, while also showing strong data scalability, requiring no expert demonstrations and achieving better performance under the same training-data size. This work lays a foundation for future research in reasoning and decision-making with UMMs.

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