UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning

Hugging Face Daily Papers Papers
unified-multimodal-models coordination-path-diversity adaptive-coordination multimodal-reasoning role-aligned-trajectories path-conditioned-executor lightweight-planner

Summary

UniPath proposes a framework for adaptive coordination of understanding and generation in unified multimodal models, leveraging coordination-path diversity to improve performance over fixed strategies.

Unified multimodal models (UMMs) aim to integrate understanding and generation within a single architecture. However, it remains underexplored how to effectively coordinate these two capabilities for more effective and efficient reasoning. Existing coordination approaches either perform coupling during training, without explicit inference-time coordination, or impose a fixed coordination pattern for all inputs. In this work, we show that multimodal tasks exhibit substantial coordination-path diversity: different inputs favor different coordination paths. This suggests that exploiting such diversity is key to improving performance. We propose UniPath, a framework for adaptively modeling and exploiting coordination-path diversity. Instead of enforcing a single coordination pattern, we represent task solving as the selection and execution of a path, ranging from direct answering to textual inference, visual-thought construction, and hypothesis-based exploration. We construct role-aligned trajectories to train a path-conditioned executor and introduce a lightweight planner mechanism to enable input-dependent path selection. Experiments show that leveraging coordination-path diversity improves performance over fixed coordination strategies while providing interpretable intermediate behaviors. The code is available at:https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/unipath.
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Paper page - UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning

Source: https://huggingface.co/papers/2605.11400

Abstract

Unified multimodal models can improve performance by adaptively selecting coordination paths rather than using fixed patterns, enabling diverse reasoning strategies for different inputs.

Unified multimodal models(UMMs) aim to integrate understanding and generation within a single architecture. However, it remains underexplored how to effectively coordinate these two capabilities for more effective and efficient reasoning. Existing coordination approaches either perform coupling during training, without explicit inference-time coordination, or impose a fixed coordination pattern for all inputs. In this work, we show that multimodal tasks exhibit substantialcoordination-path diversity: different inputs favor different coordination paths. This suggests that exploiting such diversity is key to improving performance. We propose UniPath, a framework for adaptively modeling and exploitingcoordination-path diversity. Instead of enforcing a single coordination pattern, we represent task solving as the selection and execution of a path, ranging from direct answering to textual inference, visual-thought construction, and hypothesis-based exploration. We constructrole-aligned trajectoriesto train apath-conditioned executorand introduce alightweight plannermechanism to enable input-dependent path selection. Experiments show that leveragingcoordination-path diversityimproves performance over fixed coordination strategies while providing interpretable intermediate behaviors. The code is available at:https://github.com/AIFrontierLab/TorchUMM/tree/main/src/umm/post_training/unipath.

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