TL;DR
Render-of-Thought (RoT) transforms textual reasoning chains into images to make latent reasoning explicit, improving efficiency and traceability in large language model reasoning tasks.
Contribution
RoT is the first framework to render reasoning steps as images, enabling explicit, traceable reasoning without additional pre-training overhead.
Findings
Achieves 3-4x token compression compared to explicit CoT
Provides substantial inference acceleration
Maintains competitive reasoning performance
Abstract
Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although CoT prompting enhances reasoning, its verbosity imposes substantial computational overhead. Recent works often focus exclusively on outcome alignment and lack supervision on the intermediate reasoning process. These deficiencies obscure the analyzability of the latent reasoning chain. To address these challenges, we introduce Render-of-Thought (RoT), the first framework to reify the reasoning chain by rendering textual steps into images, making the latent rationale explicit and traceable. Specifically, we leverage the vision encoders of existing Vision Language Models (VLMs) as semantic anchors to align the vision embeddings with the textual space. This design ensures plug-and-play implementation without incurring additional pre-training…
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