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Shaohua Zhang

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4 papers

2 author rows

AAAI Conference 2026 System Paper

GeoProblem Factory: A Visual Interaction System for Solvable and Controllable Geometric Problem Generation by Leveraging Symbolic Deduction Engine

Zhuoxuan Jiang
Yanpeng Li
Tianyang Zhang
Jing Chen
Yong Li
Mo Guang
Wen Si
Shaohua Zhang

We propose a novel system, GeoProblem Factory, designed to effectively generate high-quality geometry problems for intelligent education. The system enables to efficiently produce batches of geometry problems for teachers and students, either to save time and manual effort or to support personalized learning. Generating geometry problems is particularly challenging, as it requires ensuring both solvability and controllability from a pedagogical perspective. To address these issues, we adopt a state-of-the-art pipeline method based on a symbolic deduction engine and develop a visual interaction demo. This demo allows users to easily refine the generated problems through visual operations. It provides two modes for inputting controllable information: specifying knowledge points or supplying a reference problem. Moreover, the system can automatically generate a preliminary geometric diagram corresponding to each problem for further refinement. Through human–machine interaction, the system can more efficiently produce high-quality geometry problems than ever.

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AAAI Conference 2025 System Paper

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

Tianyang Zhang
Zhuoxuan Jiang
Haotian Zhang
Lin Lin
Shaohua Zhang

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.

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ICRA Conference 2025 Conference Paper

Robust Robotic Breast Ultrasound Scanning and Real-Time Lesion Localization

Zhiyan Cao
Yiwei Wang 0002
Huan Zhao 0001
Han Ding 0001
Shaohua Zhang

The inherent flexibility and real-time deformation of breast tissue pose significant challenges for achieving full coverage and accurate lesion localization in autonomous breast ultrasound scanning. This paper introduces a robust finite state machine-based framework that mimics the decision-making process of an experienced physician, dynamically transitioning between the global breast scan and the fine lesion scan. An autonomous radial and anti-radial global scan pattern ensures comprehensive breast coverage. To avoid lesion misidentification caused by soft tissue movement, a real-time lesion fine scan method is proposed for lesion detection and localization. Experimental results demonstrate that the system in full coverage tests achieves 7 identified lesions out of 7 existing lesions and maintains a robust localization accuracy of $\mathbf{3. 2 3 ~ m m}$ across phantoms with varying stiffnesses.

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ICLR Conference 2020 Conference Paper

Double Neural Counterfactual Regret Minimization

Hui Li 0061
Kailiang Hu
Shaohua Zhang
Yuan (Alan) Qi
Le Song

Counterfactual regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CFR algorithm only works for discrete states and action spaces, and the resulting strategy is maintained as a tabular representation. Such tabular representation limits the method from being directly applied to large games. In this paper, we propose a double neural representation for the IIGs, where one neural network represents the cumulative regret, and the other represents the average strategy. Such neural representations allow us to avoid manual game abstraction and carry out end-to-end optimization. To make the learning efficient, we also developed several novel techniques including a robust sampling method and a mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) method, which may be of independent interests. Empirically, on games tractable to tabular approaches, neural strategies trained with our algorithm converge comparably to their tabular counterparts, and significantly outperform those based on deep reinforcement learning. On extremely large games with billions of decision nodes, our approach achieved strong performance while using hundreds of times less memory than the tabular CFR. On head-to-head matches of hands-up no-limit texas hold'em, our neural agent beat the strong agent ABS-CFR by $9.8\pm4.1$ chips per game. It's a successful application of neural CFR in large games.

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