# Reasoning in the Dark: Interleaved Vision-Text Reasoning in Latent Space

arXiv: https://arxiv.org/abs/2510.12603 · HTML: https://mlsanity.com/paper/2510.12603/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 18 of 18)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024)
- [DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning](https://mlsanity.com/paper/2505.14362/) (2025)
- [From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step](https://mlsanity.com/paper/2405.14838/) (2024)
- [CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation](https://mlsanity.com/paper/2502.21074/) (2025)
- [Imagine while Reasoning in Space: Multimodal Visualization-of-Thought](https://mlsanity.com/paper/2501.07542/) (2025)
- [Compressed Chain of Thought: Efficient Reasoning Through Dense Representations](https://mlsanity.com/paper/2412.13171/) (2024)
- [Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens](https://mlsanity.com/paper/2506.17218/) (2025)
- [PromptCap: Prompt-Guided Task-Aware Image Captioning](https://mlsanity.com/paper/2211.09699/) (2022)
- [Latent Visual Reasoning](https://mlsanity.com/paper/2509.24251/) (2025)
- [Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs](https://mlsanity.com/paper/2505.15436/) (2025)
- [Visual Planning: Let's Think Only with Images](https://mlsanity.com/paper/2505.11409/) (2025)
- [Thinking with Generated Images](https://mlsanity.com/paper/2505.22525/) (2025)
- [Multimodal Chain of Continuous Thought for Latent-Space Reasoning in Vision-Language Models](https://mlsanity.com/paper/2508.12587/) (2025)
- [Thinking with Visual Abstract: Enhancing Multimodal Reasoning via Visual Abstraction](https://mlsanity.com/paper/2505.20164/) (2025)

## Built on by (most cited first, 23 of 23)

- [The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook](https://mlsanity.com/paper/2604.02029/) (2026)
- [Reasoning Within the Mind: Dynamic Multimodal Interleaving in Latent Space](https://mlsanity.com/paper/2512.12623/) (2025)
- [DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models](https://mlsanity.com/paper/2512.24165/) (2025)
- [Forest Before Trees: Latent Superposition for Efficient Visual Reasoning](https://mlsanity.com/paper/2601.06803/) (2026)
- [MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution](https://mlsanity.com/paper/2604.26283/) (2026)
- [What's Holding Back Latent Visual Reasoning?](https://mlsanity.com/paper/2605.18445/) (2026)
- [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://mlsanity.com/paper/2608.26105/) (2026)
- [Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do](https://mlsanity.com/paper/2606.22565/) (2026)
- [What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis](https://mlsanity.com/paper/2606.20075/) (2026)
- [ReGuLaR: Relation-Grounded Latent Reasoning for Large Vision-Language Models](https://mlsanity.com/paper/2605.30587/) (2026)
- [Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning](https://mlsanity.com/paper/2606.13106/) (2026)
- [IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation](https://mlsanity.com/paper/2606.24849/) (2026)
- [LUT: Latent Utility Training for Visual Reasoning](https://mlsanity.com/paper/2608.00743/) (2026)
- [Scaffolding Minds: Optimizing Latent Visual Target Representations for Multimodal Reasoning](https://mlsanity.com/paper/2608.19669/) (2026)
- [Q-Tacit: Image Quality Assessment via Latent Visual Reasoning](https://mlsanity.com/paper/2603.22641/) (2026)
- [LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning](https://mlsanity.com/paper/2603.29165/) (2026)
- [Position Rebinding Cache Reuse: Replay-Free Visual Revisiting for Interleaved Multimodal Reasoning](https://mlsanity.com/paper/2606.26631/) (2026)
- [Model Guides You How to Draw: Adaptive Visual Gating for Unified Multimodal Reasoning](https://mlsanity.com/paper/2607.10004/) (2026)
- [Uni-LaDiR: Latent Diffusion Unifies Multimodal Reasoning](https://mlsanity.com/paper/2609.19878/) (2026)
- [Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence](https://mlsanity.com/paper/2609.34563/) (2026)
- [Soft Spatial Reasoning](https://mlsanity.com/paper/2609.38717/) (2026)
- [ReMAP: Restoring the Perceptual Cycle with Reasoning-Time Latent Visual Memory](https://mlsanity.com/paper/2610.05097/) (2026)
- [SLVR: Structured Latent Visual Reasoning via Human-like Reasoning Flows](https://mlsanity.com/paper/2610.10563/) (2026)

## Abstract (arXiv)

Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. To address these issues, we introduce multimodal latent reasoning with the advantages of multimodal representation, reduced annotation, and inference efficiency. To facilitate it, we propose Interleaved Vision-Text Latent Reasoning (IVT-LR), which injects both visual and textual information in the reasoning process within the latent space. Specifically, IVT-LR represents each reasoning step by combining two implicit parts: latent text (the hidden states from the previous step) and latent vision (a set of selected image embeddings). We further introduce a progressive multi-stage training strategy to enable MLLMs to perform the above multimodal latent reasoning steps. Experiments on M3CoT and ScienceQA demonstrate that our IVT-LR method achieves an average performance increase of 5.45% in accuracy, while simultaneously achieving a speed increase of over 5 times compared to existing approaches.