Text-to-image (T2I) generative models have achieved remarkable progress, demonstrating exceptional capability in synthesizing high-quality images from textual prompts. While existing research and benchmarks have extensively evaluated the ability of T2I models to follow the literal meaning of prompts, their ability to reason over prompts with domain knowledge to uncover implicit meaning and contextual nuances remains underexplored. To bridge this gap, we introduce T2I-ReasonBench, a novel benchmark designed to explore the knowledge-driven reasoning capabilities of T2I models.T2I-ReasonBench comprises 800 meticulously designed prompts organized into four dimensions: (1) Idiom Interpretation, (2) Textual Image Design, (3) Entity Reasoning, and (4) Scientific Reasoning. These dimensions challenge models to integrate domain knowledge, infer implicit meaning, and resolve contextual ambiguities. To quantify the performance, we introduce a two-stage evaluation framework: a large language model (LLM) generates prompt-specific question-criterion pairs that evaluate if the image includes the essential elements resulting from correct reasoning; a multimodal LLM (MLLM) then scores the generated image against these criteria. Our comprehensive study across 16 state-of-the-art diffusion and unified multimodal models (UMMs) reveal two primary bottlenecks. First, many models lack the foundational reasoning ability to fully comprehend complex prompts. Second, even models with stronger reasoning modules exhibit a persistent gap between their internal understanding and the final generated image. This highlights an urgent need for the next generation of T2I systems to not only improve their reasoning capability but also to enhance integration between reasoning and synthesis.

Recent reinforcement learning (RL) approaches, such as outcome-supervised GRPO, have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs (MLLMs) remains underexplored. Progress has been further limited by the lack of evaluation settings that jointly test perception and reasoning under controlled generalization challenges. To enable such analysis, we present **SEED-Bench-R1**, a structured testbed featuring real-world video tasks and hierarchical evaluation across in-distribution, cross-environment, and cross-environment-task scenarios. Our analysis reveals that standard outcome-supervised GRPO often yields "logical incoherence"—achieving correct answers through flawed reasoning—due to its exclusive focus on final-answer rewards and rigid KL penalties. To address this, we propose **GRPO-CARE**, a consistency-aware RL framework that eliminates KL penalties while introducing a two-tiered reward system: a base reward for accuracy and an adaptive bonus for consistency. This bonus, derived from a slowly evolving reference model through group-relative likelihood calibration, rewards reasoning paths that logically support the final answer without requiring expensive process supervision. Experiments on SEED-Bench-R1 show that GRPO-CARE consistently outperforms standard GRPO, achieving a 6.7% gain on the hardest evaluation level and a 24.5% increase in reasoning consistency. Moreover, models trained with GRPO-CARE transfer effectively to diverse video understanding and even language-only reasoning benchmarks, validating its robustness and generality.