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Introduces SciDraw-Bench, a benchmark for evaluating scientific figure generation by text-to-image and multimodal models, with a four-dimensional evaluation protocol. Findings show domain-specific systems outperform general-purpose models, with text fidelity remaining the hardest challenge.
AQuaUI is a training-free inference-time token reduction method for GUI agent models that uses adaptive quadtrees to reduce spatial redundancy in screenshots, achieving up to 13.22% speedup and 29.52% fewer visual tokens while retaining 99.06% of performance.
The Visual Aesthetic Benchmark (VAB) evaluates multimodal models' ability to judge aesthetics through comparative selection, revealing significant gaps versus human experts and showing that fine-tuning on expert examples improves accuracy.
This paper introduces UNO, an Understanding-Oriented Post-Training framework that uses comprehension tasks as supervisory signals to enhance image generation and editing in unified multimodal models.
Researchers introduce GSI-Bench, the first benchmark to quantify generative spatial intelligence in multimodal models by evaluating 3D spatial constraint compliance during image generation. Fine-tuning on their synthetic dataset boosts both spatial editing fidelity and downstream spatial understanding, showing generative training can strengthen spatial reasoning.