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Worcester Polytechnic Institute

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Selected work

Representative Papers

Improved Upper Bounds for Slicing the Hypercube

Feb 06, 2026arXiv.org

This study addresses the problem of determining the minimum number of hyperplanes, denoted $S(n)$, required to slice all edges of an $n$-dimensional hypercube. By integrating reasoning large language models with CPro1—a tool featuring automated hyperparameter tuning—we design an efficient search algorithm that yields novel constructive solutions, including an 8-hyperplane slicing scheme for the 10-dimensional hypercube ($Q_{10}$). Our main contributions are a significant improvement of the upper bound on $S(n)$ from $\lceil 5n/6 \rceil$ to $\lceil 4n/5 \rceil$, with a refined bound of $4n/5 + 1$ when $n$ is an odd multiple of 5, and the first non-trivial lower bound on the number of edges that can be sliced by fewer than $n$ hyperplanes. These results substantially advance the theoretical understanding of this classical problem in combinatorial geometry.

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DrawEduMath: Evaluating Vision Language Models with Expert-Annotated Students' Hand-Drawn Math Images

Jan 24, 2025

Existing vision-language models (VLMs) exhibit insufficient capability in parsing cluttered, real-world handwritten mathematics assignments within authentic K–12 educational settings. Method: We introduce the first K–12 handwritten mathematics image benchmark—comprising 2,030 real student assignment images, 11,661 expert teacher-annotated question-answer (QA) pairs, and 44,362 high-fidelity synthetic QA pairs generated by large language models (LLMs). We propose an education-driven evaluation paradigm featuring teacher-expert free-form descriptions and strategic annotation protocols. Contribution/Results: Our analysis confirms that synthetic QA pairs reliably substitute human annotations (Spearman’s ρ = 0.92 for model ranking correlation). Multi-granularity evaluation reveals a substantial performance gap between state-of-the-art VLMs and human teachers. The benchmark is publicly released to advance robustness and trustworthiness research in educational AI.

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