GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models
This addresses a critical evaluation gap for researchers and developers of multimodal AI systems, though it is incremental as it focuses on benchmarking rather than new model development.
The authors tackled the lack of benchmarks for evaluating generative reasoning in Unified Multimodal Models by introducing GGBench, a benchmark based on geometric construction that measures integrated language comprehension and precise visual generation.
The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image generation separately, failing to measure the integrated cognitive process of generative reasoning. To bridge this gap, we propose that geometric construction provides an ideal testbed as it inherently demands a fusion of language comprehension and precise visual generation. We introduce GGBench, a benchmark designed specifically to evaluate geometric generative reasoning. It provides a comprehensive framework for systematically diagnosing a model's ability to not only understand and reason but to actively construct a solution, thereby setting a more rigorous standard for the next generation of intelligent systems. Project website: https://opendatalab-raiser.github.io/GGBench/.