Chuyue Li

Jinxiang Lai, Zexin Lu, Jiajun He et al.

Visual content creation tasks demand a nuanced understanding of design conventions and creative workflows-capabilities challenging for general models, while workflow-based agents lack specialized knowledge for autonomous creative planning. To overcome these challenges, we propose VisionCreator, a native visual-generation agentic model that unifies Understanding, Thinking, Planning, and Creation (UTPC) capabilities within an end-to-end learnable framework. Our work introduces four key contributions: (i) VisGenData-4k and its construction methodology using metacognition-based VisionAgent to generate high-quality creation trajectories with explicit UTPC structures; (ii) The VisionCreator agentic model, optimized through Progressive Specialization Training (PST) and Virtual Reinforcement Learning (VRL) within a high-fidelity simulated environment, enabling stable and efficient acquisition of UTPC capabilities for complex creation tasks; (iii) VisGenBench, a comprehensive benchmark featuring 1.2k test samples across diverse scenarios for standardized evaluation of multi-step visual creation capabilities; (iv) Remarkably, our VisionCreator-8B/32B models demonstrate superior performance over larger closed-source models across multiple evaluation dimensions. Overall, this work provides a foundation for future research in visual-generation agentic systems.

Chuyue Li, Ziqi Tang, Jingyi Wang et al.

With the advancement of Large Language Models (LLMs), code error detection has extended beyond traditional IDE diagnostics to context-sensitive debugging in educational scenarios. However, existing approaches lack large-scale datasets, multi-error analysis, and unified error taxonomies. To address this, we introduce PyMETA, a large-scale Python code error classification dataset of 48,646 student submissions, with single-error labels for all samples and a diagnostic subset of 97 expert-annotated multi-error samples. The dataset uses a three-level hierarchical taxonomy, from a binary error/no-error split down to 14 fine-grained error types grounded in Python's official exception hierarchy. We evaluate multi-level classification tasks on two finetuned models and four LLMs with prompting, comparing their classification performance and runtime cost. For multi-error prompting, the best model, Gemini 2.5 Pro, achieves 81.8% macro F1 under the "contains" criterion. We observe that: 1) prompted LLMs still underperform finetuned smaller models; 2) models exhibit significant disparities across error types; 3) most LLMs over-classify code as Logic Error, with GPT-3.5 showing the highest Logic Error Overprediction Rate and Gemini 2.5 Pro the lowest. Our work establishes a data foundation and provides insights for LLM-based code error research.