GENIUS: Generative Fluid Intelligence Evaluation Suite

Unified Multimodal Models (UMMs) have shown remarkable progress in visual generation. Yet, existing benchmarks predominantly assess textitCrystallized Intelligence, which relies on recalling accumulated knowledge and learned schemas. This focus overlooks textitGenerative Fluid Intelligence (GFI): the capacity to induce patterns, reason through constraints, and adapt to novel scenarios on the fly. To rigorously assess this capability, we introduce GENIUS (GEN Fluid Intelligence EvalUation Suite). We formalize textitGFI as a synthesis of three primitives. These include textitInducing Implicit Patterns (e.g., inferring personalized visual preferences), textitExecuting Ad-hoc Constraints (e.g., visualizing abstract metaphors), and textitAdapting to Contextual Knowledge (e.g., simulating counter-intuitive physics). Collectively, these primitives challenge models to solve problems grounded entirely in the immediate context. Our systematic evaluation of 12 representative models reveals significant performance deficits in these tasks. Crucially, our diagnostic analysis disentangles these failure modes. It demonstrates that deficits stem from limited context comprehension rather than insufficient intrinsic generative capability. To bridge this gap, we propose a training-free attention intervention strategy. Ultimately, GENIUS establishes a rigorous standard for textitGFI, guiding the field beyond knowledge utilization toward dynamic, general-purpose reasoning. Our dataset and code will be released at: hrefhttps://github.com/arctanxarc/GENIUShttps://github.com/arctanxarc/GENIUS.