Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

ACL ARR 2026 January Submission5126 Authors

Keywords: Large Language Model, Vision Language Model, Latent Reasoning

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 overhead.
Extensive experiments on mathematical and logical reasoning benchmarks demonstrate that our method achieves 3-4$\times$ token compression and substantial inference acceleration compared to explicit CoT.

Paper Type: Long

Research Area: LLM Efficiency

Research Area Keywords: Language Modeling

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 5126