1 citations · 1 across the 8 of their papers we have counts for
8 papers
Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposing
Can Li, Ting Zhang, Junbo Zhao +1
Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor. However, current frameworks suffer from severe bottlen…
DiagramBank: A Quality-Audited Dataset of Scientific Schematic Diagrams with Multi-Level Document Context
Ling Yue, Tingwen Zhang, Jiaying Wang +2
Scientific papers use schematic diagrams to communicate methods, workflows, and system structure, yet existing scientific-figure corpora often mix them with plots, screenshots, and…
Non-Parametric Structural Priors for Geometry Theorem Prediction
Junbo Zhao, Ting Zhang, Can Li +3
Multi-step theorem prediction is a central challenge in geometry problem solving. Existing neural-symbolic approaches rely heavily on supervised parametric models, which exhibit li…
GeoLoom: High-quality Geometric Diagram Generation from Textual Input
Xiaojing Wei, Ting Zhang, Wei He +2
High-quality geometric diagram generation presents both a challenge and an opportunity: it demands strict spatial accuracy while offering well-defined constraints to guide generati…
Pi-GPS: Enhancing Geometry Problem Solving by Unleashing the Power of Diagrammatic Information
Junbo Zhao, Ting Zhang, Jiayu Sun +2
Geometry problem solving has garnered increasing attention due to its potential applications in intelligent education field. Inspired by the observation that text often introduces…
GNN-Coder: Boosting Semantic Code Retrieval with Combined GNNs and Transformer
Yufan Ye, Pu Pang, Ting Zhang +1
Code retrieval is a crucial component in modern software development, particularly in large-scale projects. However, existing approaches relying on sequence-based models often fail…
MagicGeo: Training-Free Text-Guided Geometric Diagram Generation
Junxiao Wang, Ting Zhang, Heng Yu +2
Geometric diagrams are critical in conveying mathematical and scientific concepts, yet traditional diagram generation methods are often manual and resource-intensive. While text-to…
Process-Supervised Reinforcement Learning for Code Generation
Yufan Ye, Ting Zhang, Wenbin Jiang +1
Existing reinforcement learning strategies based on outcome supervision have proven effective in enhancing the performance of large language models(LLMs) for code generation. While…