UReason: Benchmarking the Reasoning Paradox in Unified Multimodal Models

• URL: http://arxiv.org/abs/2602.08336v1
• Date: Mon, 09 Feb 2026 07:17:57 GMT
• Title: UReason: Benchmarking the Reasoning Paradox in Unified Multimodal Models
• Authors: Cheng Yang, Chufan Shi, Bo Shui, Yaokang Wu, Muzi Tao, Huijuan Wang, Ivan Yee Lee, Yong Liu, Xuezhe Ma, Taylor Berg-Kirkpatrick,
• Abstract summary: We present UReason, a diagnostic benchmark for reasoning-driven image generation.<n>We observe a consistent Reasoning Paradox: Reasoning traces generally improve performance over direct generation.<n>Our analysis suggests that the bottleneck lies in contextual interference rather than insufficient reasoning capacity.
• Score: 44.0727449598399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To elicit capabilities for addressing complex and implicit visual requirements, recent unified multimodal models increasingly adopt chain-of-thought reasoning to guide image generation. However, the actual effect of reasoning on visual synthesis remains unclear. We present UReason, a diagnostic benchmark for reasoning-driven image generation that evaluates whether reasoning can be faithfully executed in pixels. UReason contains 2,000 instances across five task families: Code, Arithmetic, Spatial, Attribute, and Text reasoning. To isolate the role of reasoning traces, we introduce an evaluation framework comparing direct generation, reasoning-guided generation, and de-contextualized generation which conditions only on the refined prompt. Across eight open-source unified models, we observe a consistent Reasoning Paradox: Reasoning traces generally improve performance over direct generation, yet retaining intermediate thoughts as conditioning context often hinders visual synthesis, and conditioning only on the refined prompt yields substantial gains. Our analysis suggests that the bottleneck lies in contextual interference rather than insufficient reasoning capacity. UReason provides a principled testbed for studying reasoning in unified models and motivates future methods that effectively integrate reasoning for visual generation while mitigating interference.

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