Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding

• URL: http://arxiv.org/abs/2607.24407v1
• Date: Mon, 27 Jul 2026 13:24:35 GMT
• Title: Mixture-of-Thought-Tokens: Unifying Perception and Reasoning for Free-form Multimodal Grounding
• Abstract summary: Mixture-of-Thought-Tokens (Motto) is a new free-form multimodal grounding method.<n>Motto bridges the perception-reasoning gap, enabling MLLMs to empower diverse, arbitrary grounding queries.<n>Extensive experiments demonstrate that Motto achieves state-of-the-art performance across diverse free-form grounding tasks.
• Score: 45.606064707494774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models have made great progress in grounding tasks, yet existing methods still struggle to unify precise localization and complex reasoning. For one thing, text-based methods rely on coordinates or index prediction, severely limiting the perceptual capabilities of the model for dense visual objects. Meanwhile, latent token-based methods employ special tokens without inherent spatial references and use a decoding mechanism that lacks thinking steps, weakening high-level reasoning capabilities. Consequently, developing a unified framework that excels in both perception and reasoning remains challenging. To address this, we propose Mixture-of-Thought-Tokens (Motto), a new free-form multimodal grounding method that bridges the perception-reasoning gap, enabling MLLMs to empower diverse, arbitrary grounding queries. Specifically, we introduce Spatially-Grounded Thought Tokenization to explicitly align special tokens with spatial locations for clear spatial correspondence and visual interpretability. We further design a Context-Adaptive Chain-of-Tokens that dynamically switch grounding modes within an interleaved reasoning chain, achieving robust grounding across tasks of varying complexity. In addition, we construct PR-Bench, a new referring expression comprehension benchmark to evaluate the perception-reasoning gap. Extensive experiments demonstrate that Motto achieves state-of-the-art performance across diverse free-form grounding tasks.

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