Adaptively coordination of generation and understanding in Unified Multimodal Models for better reasoning.</p>\n","updatedAt":"2026-05-13T12:59:19.401Z","author":{"_id":"6204cc0d522e40b4a18d86e2","avatarUrl":"/avatars/18daf2de5671e711dc745388dd60569d.svg","fullname":"Jindong Wang","name":"jindongwang","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8870878219604492},"editors":["jindongwang"],"editorAvatarUrls":["/avatars/18daf2de5671e711dc745388dd60569d.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.11400","authors":[{"_id":"6a047564e94247db1a5a9dfb","name":"Hayes Bai","hidden":false},{"_id":"6a047564e94247db1a5a9dfc","name":"Yinyi Luo","hidden":false},{"_id":"6a047564e94247db1a5a9dfd","name":"Wenwen Wang","hidden":false},{"_id":"6a047564e94247db1a5a9dfe","name":"Qingsong Wen","hidden":false},{"_id":"6a047564e94247db1a5a9dff","name":"Jindong Wang","hidden":false}],"publishedAt":"2026-05-12T00:00:00.000Z","submittedOnDailyAt":"2026-05-13T00:00:00.000Z","title":"UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning","submittedOnDailyBy":{"_id":"6204cc0d522e40b4a18d86e2","avatarUrl":"/avatars/18daf2de5671e711dc745388dd60569d.svg","isPro":false,"fullname":"Jindong Wang","user":"jindongwang","type":"user","name":"jindongwang"},"summary":"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.","upvotes":2,"discussionId":"6a047564e94247db1a5a9e00","projectPage":"https://github.com/AIFrontierLab/TorchUMM","ai_summary":"Unified multimodal models can improve performance by adaptively selecting coordination paths rather than using fixed patterns, enabling diverse reasoning strategies for different inputs.","ai_keywords":["unified multimodal models","coordination-path diversity","path-conditioned executor","lightweight planner","role-aligned trajectories"],"organization":{"_id":"6359ced0d21ba0962c876d02","name":"williammary","fullname":"The College of William & Mary","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1666830023094-6359947160e2f140f44d58ad.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6204cc0d522e40b4a18d86e2","avatarUrl":"/avatars/18daf2de5671e711dc745388dd60569d.svg","isPro":false,"fullname":"Jindong Wang","user":"jindongwang","type":"user"},{"_id":"60d596784cf0297c143fcd33","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60d596784cf0297c143fcd33/phknQ4Z2VuUj3akhcoxLC.png","isPro":false,"fullname":"Yiqiao Jin","user":"Ahren09","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"6359ced0d21ba0962c876d02","name":"williammary","fullname":"The College of William & Mary","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/1666830023094-6359947160e2f140f44d58ad.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.11400.md"}">
UniPath: Adaptive Coordination of Understanding and Generation for Unified Multimodal Reasoning
Abstract
Unified multimodal models can improve performance by adaptively selecting coordination paths rather than using fixed patterns, enabling diverse reasoning strategies for different inputs.
AI-generated summary
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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Adaptively coordination of generation and understanding in Unified Multimodal Models for better reasoning.
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Cite arxiv.org/abs/2605.11400 in a model README.md to link it from this page.
Cite arxiv.org/abs/2605.11400 in a dataset README.md to link it from this page.
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