Evaluating Reasoning Fidelity in Visual Text Generation

2026-06-03

AI Summary

This study addresses the unresolved question of whether current text-to-image (T2I) models genuinely possess logical reasoning capabilities when generating visual content involving complex inferential processes. The authors propose the first comprehensive evaluation framework focused on reasoning fidelity, systematically assessing mainstream T2I models through tasks encompassing long-text rendering, factual knowledge probing, contextual comprehension, and multi-step reasoning. Their analysis reveals that, despite producing visually legible text, these models frequently exhibit semantic inaccuracies, logical inconsistencies, and erroneous intermediate reasoning steps. Notably, their reasoning performance falls substantially short of that achieved by purely textual language models. This work exposes fundamental limitations in the deep reasoning capacities of existing T2I systems and establishes a benchmark to guide future advancements in this direction.

Abstract

Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generation and slide generation. However, it remains unclear whether such systems faithfully preserve reasoning ability when complex solutions must be expressed directly through rendered text, or whether they merely imitate surface-level patterns. We investigate this question by evaluating reasoning fidelity in visual text generation, where models must express complete reasoning processes as images. Our evaluation includes long text rendering, factual knowledge probing, context understanding, and multi-step reasoning. Across these settings, we find that current T2I models frequently produce semantic errors, logical inconsistencies, and incorrect intermediate steps, even when the rendered text appears visually clear. These failures contrast with the strong reasoning performance of text-only models on the same tasks. Our findings reveal a substantial gap between visual text generation and procedural reasoning, motivating more reliable visual text reasoning.

Problem

• reasoning fidelity
• visual text generation
• text-to-image models
• procedural reasoning
• semantic errors

Innovation

• reasoning fidelity
• visual text generation
• text-to-image models
• multi-step reasoning
• semantic errors