OneLatent: Single-Token Compression for Visual Latent Reasoning

Chain-of-thought (CoT) prompting improves reasoning but often increases inference cost by one to two orders of magnitude. To address these challenges, we present $\mathbf{\text{OneLatent}}$, a framework that compresses intermediate reasoning into a single latent token via supervision from rendered CoT images and DeepSeek-OCR hidden states. By rendering textual steps into images, we obtain a deterministic supervision signal that can be inspected and audited without requiring the model to output verbose textual rationales. Across benchmarks, OneLatent reduces average output length by $11\times$ with only a $2.21\mathrm{\%}$ average accuracy drop relative to textual CoT, while improving output token contribution (OTC) by $6.8\times$. On long-chain logical reasoning, OneLatent reaches $99.80\mathrm{\%}$ on ProntoQA and $97.80\mathrm{\%}$ on ProsQA with one latent token, with compression up to $87.4\times$, supporting compression-constrained generalization.