Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity
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Authors: name: Jiarui Zhang, name: Ollie Liu, name: Tianyu Yu, name: Jinyi Hu, name: Willie Neiswanger

Multimodal large language models (MLLMs) have made rapid progress in recent
years, yet continue to struggle with low-level visual perception (LLVP) --
particularly the ability to accurately describe the geometric details of an
image. This capability is crucial for applications in areas such as robotics,
medical image analysis, and manufacturing. In this paper, we first introduce
Geoperception, a benchmark designed to evaluate an MLLM's ability to accurately
transcribe 2D geometric information from an image. Using this benchmark, we
demonstrate the limitations of leading MLLMs, and then conduct a comprehensive
empirical study to explore strategies for improving their performance on
geometric tasks. Our findings highlight the benefits of certain model
architectures, training techniques, and data strategies, including the use of
high-fidelity synthetic data and multi-stage training with a data curriculum.
Notably, we find that a data curriculum enables models to learn challenging
geometry understanding tasks which they fail to learn from scratch. Leveraging
these insights, we develop Euclid, a family of models specifically optimized
for strong low-level geometric perception. Although purely trained on synthetic
multimodal data, Euclid shows strong generalization ability to novel geometry
shapes. For instance, Euclid outperforms the best closed-source model,
Gemini-1.5-Pro, by up to 58.56% on certain Geoperception benchmark tasks and
10.65% on average across all tasks.