A Mathematics Question Diagram Generation Strategy Defined with Codes and Empowered by Reasoning Large Language Models
Why this work is in the frame
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Bibliographic record
Abstract
Current multimodal large language models (LLMs) can perform more varied and precious generation for various tasks. It has the potential to support AI-generated assignment contents. However, diagrams directly generated by multimodal LLMs (from text to image) still lack semantic accuracy and quality. Based on the Q-Buddy app, a more stable and controllable method defined with codes (DWC) has been developed to generate structured diagrams for mathematical questions by providing the function alongside the prompt to guide the generation process. Based on initial comparison results, it can be observed that there is a significant improvement in the quality of generated diagrams for mathematics quiz items. The strategy may provide some inspiration on integrating existing code features and libraries into human-LLM collaboration in educational settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it