# Latent Implicit Visual Reasoning

arXiv: https://arxiv.org/abs/2512.21218 · HTML: https://mlsanity.com/paper/2512.21218/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 25 of 25)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [Visual-RFT: Visual Reinforcement Fine-Tuning](https://mlsanity.com/paper/2503.01785/) (2025)
- [DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning](https://mlsanity.com/paper/2505.14362/) (2025)
- [R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization](https://mlsanity.com/paper/2503.12937/) (2025)
- [Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning](https://mlsanity.com/paper/2505.15966/) (2025)
- [Think before you speak: Training Language Models With Pause Tokens](https://mlsanity.com/paper/2310.02226/) (2023)
- [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://mlsanity.com/paper/2509.23661/) (2025)
- [Imagine while Reasoning in Space: Multimodal Visualization-of-Thought](https://mlsanity.com/paper/2501.07542/) (2025)
- [Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search](https://mlsanity.com/paper/2412.18319/) (2024)
- [LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs](https://mlsanity.com/paper/2501.06186/) (2025)
- [SPair-71k: A Large-scale Benchmark for Semantic Correspondence](https://mlsanity.com/paper/1908.10543/) (2019)
- [Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens](https://mlsanity.com/paper/2506.17218/) (2025)
- [SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models](https://mlsanity.com/paper/2412.07755/) (2024)
- [Latent Visual Reasoning](https://mlsanity.com/paper/2509.24251/) (2025)
- [Perception-Aware Policy Optimization for Multimodal Reasoning](https://mlsanity.com/paper/2507.06448/) (2025)
- [VGR: Visual Grounded Reasoning](https://mlsanity.com/paper/2506.11991/) (2025)
- [The ArtBench Dataset: Benchmarking Generative Models with Artworks](https://mlsanity.com/paper/2206.11404/) (2022)
- [There is a Time and Place for Reasoning Beyond the Image](https://mlsanity.com/paper/2203.00758/) (2022)
- [Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization](https://mlsanity.com/paper/2504.18397/) (2025)

## Built on by (most cited first, 18 of 18)

- [The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook](https://mlsanity.com/paper/2604.02029/) (2026)
- [LaST0: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Model](https://mlsanity.com/paper/2601.05248/) (2026)
- [Toward Cognitive Supersensing in Multimodal Large Language Model](https://mlsanity.com/paper/2602.01541/) (2026)
- [LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning](https://mlsanity.com/paper/2605.22012/) (2026)
- [CrystaL: Spontaneous Emergence of Visual Latents in MLLMs](https://mlsanity.com/paper/2602.20980/) (2026)
- [Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2605.12374/) (2026)
- [LUT: Latent Utility Training for Visual Reasoning](https://mlsanity.com/paper/2608.00743/) (2026)
- [Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?](https://mlsanity.com/paper/2605.21642/) (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)
- [Reasoning with Image Generation](https://mlsanity.com/paper/2609.16409/) (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)
- [WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning](https://mlsanity.com/paper/2609.34826/) (2026)
- [Soft Spatial Reasoning](https://mlsanity.com/paper/2609.38717/) (2026)
- [MoLE: Mixture of Latent Experts for Complementary Visual Reasoning](https://mlsanity.com/paper/2610.01917/) (2026)
- [GeoLatent: Geometry-Guided Latent Structuring with Routed Optimization for 3D Reasoning](https://mlsanity.com/paper/2610.02091/) (2026)
- [Fitting Vision Adapters at Frontier Scales](https://mlsanity.com/paper/2610.05897/) (2026)

## Abstract (arXiv)

While Large Multimodal Models (LMMs) have made significant progress, they remain largely text-centric, relying on language as their core reasoning modality. As a result, they are limited in their ability to handle reasoning tasks that are predominantly visual. Recent approaches have sought to address this by supervising intermediate visual steps with helper images, depth maps, or image crops. However, these strategies impose restrictive priors on what "useful" visual abstractions look like, add heavy annotation costs, and struggle to generalize across tasks. To address this critical limitation, we propose Latent Implicit Visual Reasoning (LIVR), a task-agnostic mechanism that trains LMMs to discover and use latent visual reasoning tokens without explicit intermediate supervision. These tokens attend globally and re-encode the image in a task-adaptive way, enabling the model to extract relevant visual information without hand-crafted supervision. LIVR consistently outperforms direct supervised fine-tuning across diverse vision-centric tasks and multiple LMM backbones. In broader comparisons, LIVR remains competitive with or outperforms prior text-based and explicit-visual-intermediate reasoning methods, while requiring no additional intermediate supervision such as helper images, bounding boxes, image crops, depth maps, or chain-of-thought annotations. Our project page can be found here: https://www.chuyishang.com/livr/