While Multimodal Large Language Models (MLLMs) have achieved remarkable success in difficult purely textual mathematical reasoning tasks, even advanced closed-source models such as GPT-o3 still struggle with geometric problems. This discrepancy motivates us to investigate the root cause: is the bottleneck of multimodal geometric reasoning rooted in reasoning itself, or in the perception of geometric information from diagrams? To answer this question, we conduct an exploratory experiment and find that providing high-quality captions consistently and substantially boosts performance of MLLMs, empirically validating the visual perception bottleneck in geometric reasoning. However, MLLMs’ capabilities in visual geometric perception remain insufficiently evaluated. To fill the gap, we propose CapGeo-Bench, a benchmark of 4,641 high-quality figure–caption pairs equipped with a fine-grained keypoint-based evaluation metric. Specifically, CapGeo-Bench encompasses three geometric classes and is devided into four difficulty levels according to the complexity of geometric primitives. Captions are scored at a fine-grained level by extracting keypoints along three dimensions-geometric elements, spatial relationships, and numerical attributes. CapGeo-Bench provides a reliable instrument and guideline for evaluating and advancing MLLMs’ geometric perception capabilities. Our code and data are publicly available at: https://github.com/YuYingLi0/CapGeo.

With the advancement of powerful large-scale reasoning models, effectively evaluating the reasoning capabilities of these models has become increasingly important. However, existing benchmarks designed to assess the reasoning abilities of large models tend to be limited in scope and lack the flexibility to adapt their difficulty according to the evolving reasoning capacities of the models. To address this, we propose MorphoBench, a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models. Specifically, we curate the benchmark by selecting and collecting complex reasoning questions from existing benchmarks and sources such as Olympiad-level competitions. Additionally, MorphoBench adaptively modifies the analytical challenge of questions by leveraging key statements generated during the model’s reasoning process. Furthermore, it includes questions generated using simulation software, enabling dynamic adjustment of benchmark difficulty with minimal resource consumption. We have gathered over 1,300 test questions and iteratively adjusted the difficulty of MorphoBench based on the reasoning capabilities of models such as GPT-5 and Gemini-3-Pro. MorphoBench enhances the comprehensiveness and validity of model reasoning evaluation, providing reliable guidance for improving both the reasoning abilities and scientific robustness of large models.

Synthetic math data is widely used to scale LLM training, but its correctness is not guaranteed. We introduce MathDebugger, the first text-only, type-aware benchmark for jointly detecting *and* classifying errors in synthetic mathematical questions and answers. MathDebugger contains 2,000 correct questions, 2,000 erroneous questions (balanced across four fine-grained error types), and 2,000 annotated answers (610 erroneous, across three error types), each annotated with both a correctness label and an error-type label, with per-error-type Fleiss’ 𝜅 ranging from 0.69 to 0.88. On 14 SOTA LLMs (6 closed-source, 8 open-source, 1.5B–72B) and 3 process reward models, even advanced systems such as GPT-o3 and DeepSeek-R1 remain far from saturating the benchmark, particularly on fine-grained error-type detection; we further surface a *solving-vs-verifying* gap that causes math-tuned and long-chain-reasoning models to trail their general-purpose siblings. Error-type annotations also provide actionable supervision for correction, with gains that are significant under paired bootstrap (p<0.01). Code and data: [https://anonymous.4open.science/r/MathDebugger-7C4A/README.md](https://anonymous.4open.science/r/MathDebugger-7C4A/README.md)