arXiv subjects

Chuyue Li

Publications and source records attributed to Chuyue Li.

4 recordsLinked to original sources

arXiv2026

AVA-Encoder: Towards Agent-Native Video Representation Learning

Video creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence of a structured video representation that is both faithful to film content and directly usable for agentic reasoning and manipulation. To address the challenge, we propose the Agentic Video Auto-Encoder (AVA-Encoder), a novel auto-encoding framework driven by agentic self-evolution to learn agent-native video representations. AVA-Encoder transforms a video into a Film Knowledge Graph (KG) representation and then reconstructs it back into video. This Film KG representation explicitly captures entities, events, assets, and their multimodal relationships in a structured form that can be easily understood, queried, and manipulated by agents. The reconstruction residual drives a dual-loop textual-gradient optimization framework that jointly improves the Film KG representation and the Agentic Video Encoder. Extensive experiments show that AVA-Encoder achieves a 20.7-percentage-point absolute gain, or a 73.1% relative improvement, over the strongest external baseline. In the controlled policy-only setting, its pseudo-trained Agentic Video Encoder policy also outperforms a carefully human-tuned policy while using 74.3% fewer shot-level and 70.1% fewer keyframe-level system-prompt tokens. We release the complete AVA-Encoder framework, a reliable agentic video reconstruction benchmark, and the first dataset of high-quality Film KG representations.

cs.CV↗

arXiv2026

What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems

Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only interactions, leaving image-creation conversations underexplored. In image-creation tasks, useful follow-up edit suggestions must reflect user preferences, offer diverse directions, and remain executable on the current image. We collected 100,000 real multi-turn image-creation conversation samples from Qwen App and found that 80.1% are image-dependent, underscoring the need for multimodal recommendation. We address this setting with a three-stage framework. In Stage 1, we use real online data to build a human-reviewed table of appropriate follow-up editing intents, then create SFT targets and fine-tune a multimodal policy. In Stage 2, to align rule-guided SFT suggestions with actual user choices, we use user click feedback to optimize the policy through multi-objective reinforcement learning. In Stage 3, to reduce visual inconsistencies between suggested edits and the current image, we introduce a visual verifier as additional training supervision. Extensive experiments demonstrate that our framework significantly outperforms baselines on both automatic and human evaluations. In a live user-randomized A/B test with millions of users, our final framework reduces visual inconsistency from 3.7% to 0.9%. Furthermore, it significantly improves recommendation CTR by 32.70%, image take-away rate by 16.32%, and average conversation turns per user by 39.90% (all p<0.05). Project page: https://what-to-edit-next.github.io/

cs.CV↗

arXiv2026

PyMETA: A Benchmark Dataset for Hierarchical Student Code Error Classification with Python-Interpreter-Based Labels

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.

cs.SE↗

arXiv2026

VisionCreator: A Native Visual-Generation Agentic Model with Understanding, Thinking, Planning and Creation

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.

cs.CV↗