# Synthetic Perception: Can Generated Images Unlock Latent Visual Prior for Text-Centric Reasoning?

arXiv: https://arxiv.org/abs/2506.17623 · HTML: https://mlsanity.com/paper/2506.17623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Placed in

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

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

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021)
- [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://mlsanity.com/paper/2402.13929/) (2024)
- [MetaMorph: Multimodal Understanding and Generation via Instruction Tuning](https://mlsanity.com/paper/2412.14164/) (2024)
- [Caption Anything: Interactive Image Description with Diverse Multimodal Controls](https://mlsanity.com/paper/2305.02677/) (2023)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Diffusion Model for Planning: A Systematic Literature Review](https://mlsanity.com/paper/2408.10266/) (2024)
- [TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models](https://mlsanity.com/paper/2304.08821/) (2023)
- [DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning](https://mlsanity.com/paper/2408.14584/) (2024)

## Abstract (arXiv)

A significant ``modality gap" exists between the abundance of text-only data and the increasing power of multimodal models. This work systematically investigates whether images generated on-the-fly by Text-to-Image (T2I) models can serve as a mechanism to unlock latent visual priors for text-centric reasoning. Through a comprehensive evaluation framework on text classification, we analyze the impact of critical variables, including T2I model quality (e.g., Flux.1, SDXL), prompt engineering strategies, and multimodal fusion architectures. Our findings demonstrate that this ``synthetic perception" can yield significant performance gains by effectively projecting text into a visual semantic space, even when augmenting strong large language model baselines like Llama-3 and Qwen-2.5. We show that this approach serves as a form of cross-modal probing, mitigating the sensory deprivation inherent in pure text training. However, the effectiveness is highly conditional, depending on the semantic alignment between text and the generated image, the task's visual groundability, and the generative fidelity of the T2I model. Our work establishes a rigorous benchmark for this paradigm, demonstrating its viability as a pathway to enrich language understanding in traditionally unimodal scenarios.