arXiv:2512.16303v1 Announce Type: cross
Abstract: Multi-modal large language models that have image output are emerging. Many image generation benchmarks focus on aesthetics instead of fine-grained generation capabilities. In PixelArena, we propose using semantic segmentation tasks to objectively examine their fine-grained generative intelligence with pixel precision. We find the latest Gemini 3 Pro Image has emergent image generation capabilities that generate semantic masks with high fidelity under zero-shot settings, showcasing visual intelligence unseen before and true generalization in new image generation tasks. We further investigate its results, compare them qualitatively and quantitatively with those of other models, and present failure cases. The findings not only signal exciting progress in the field but also provide insights into future research related to multimodality, reasoning, interpretability and benchmarking.
DiscoverDCP: A Data-Driven Approach for Construction of Disciplined Convex Programs via Symbolic Regression
arXiv:2512.15721v1 Announce Type: cross Abstract: We propose DiscoverDCP, a data-driven framework that integrates symbolic regression with the rule sets of Disciplined Convex Programming (DCP) to