OneLatent: Single-Token Compression for Visual Latent Reasoning

Understand

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 \textbf{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 $2.21\%$ average accuracy drop relative to textual CoT, while improving output token contribution (OTC) by $6.8\times$.