🤖 AI Summary
Multimodal large language models often suffer from inefficiency, verbosity, and hallucination due to their reliance on end-to-end generation or explicit linguistic reasoning chains. To address these limitations, this work proposes HIVE, a novel framework that recursively extends Transformer modules within an aligned latent space, enabling multi-step implicit “slow thinking” reasoning without requiring explicit textual justifications. HIVE injects hierarchical visual cues—from global scenes to fine-grained regions—into the latent reasoning process, thereby performing grounded, iterative inference while eschewing dependence on superficial language explanations. Experimental results demonstrate that incorporating hierarchical visual knowledge at test time significantly enhances complex scene understanding and overall model performance.
📝 Abstract
The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often remains a"fast thinking"paradigm, reliant on end-to-end generation or explicit, language-centric chains of thought (CoT), which can be inefficient, verbose, and prone to hallucination. This work posits that robust reasoning should evolve within a latent space, integrating multimodal signals seamlessly. We propose multimodal latent reasoning via HIerarchical Visual cuEs injection (\emph{HIVE}), a novel framework that instills deliberate,"slow thinking"without depending on superficial textual rationales. Our method recursively extends transformer blocks, creating an internal loop for iterative reasoning refinement. Crucially, it injectively grounds this process with hierarchical visual cues from global scene context to fine-grained regional details directly into the model's latent representations. This enables the model to perform grounded, multi-step inference entirely in the aligned latent space. Extensive evaluations demonstrate that test-time scaling is effective when incorporating vision knowledge, and that integrating hierarchical information significantly enhances the model's understanding of complex scenes.