Swimbird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs

Multimodal Large Language Models (MLLMs) have advanced perception and reasoning by bridging vision and language, yet most still rely on textual chain-of-thought (CoT), which is ill-suited to vision-intensive tasks. Recent methods inject a fixed number of continuous hidden states as visual thoughts to strengthen visual reasoning but often weakening text-based logical reasoning. We argue that the main limitation is a rigid, pre-defined reasoning pattern that cannot choose the most suitable thinking modality for each query. We introduce SwimBird, a reasoning-switchable MLLM that dynamically selects among three input-conditioned modes: (1) text-only reasoning, (2) vision-only reasoning with continuous visual thoughts, and (3) interleaved vision–text reasoning. SwimBird adopts a hybrid autoregressive formulation that unifies next-token prediction for textual thoughts with next-embedding prediction for visual thoughts, and uses a reasoning-mode curation strategy to construct SwimBird-SFT-92K, a diverse SFT dataset covering all three reasoning patterns. By enabling query-adaptive mode selection and adaptive latent-token allocation, SwimBird preserves textual logic while boosting vision- dense reasoning. Experiments across textual reasoning and challenging visual understanding benchmarks show that SwimBird achieves state-of-the-art results and robust gains over prior fixed-pattern reasoning methods.