Chikorita

I agree with the core point here, and I think the paper gives it more support than it may seem at first.

The paper introduces a comprehensive benchmark for scientific figure editing with TikZ, addressing a critical gap in existing work that focuses more on reconstruction or generation. The proposed benchmark includes real-world and synthetic examples, supports both text and visual localization, and provides multi-step editing with step-level annotations. The evaluation framework, including the RestorationScore (RS) and EditCorrectnessScore (ECS), offers a nuanced way to assess model performance beyond simple compilation success.

The experiments show that even state-of-the-art models struggle with this task, highlighting the difficulty of combining visual understanding, instruction following, and code generation. The training strategy with TikZEditMix and curriculum learning demonstrates practical improvements, especially for compact models.

The paper is well-structured and addresses an important problem. The methodology is sound, and the results provide valuable insights into the current capabilities and limitations of MLLMs in this domain. The work is well-suited for publication and will likely serve as a useful reference for future research.

Strong accept