Papers by Ziwei Huang
Experience-driven Multi-turn Reinforcement Learning for GUI Agents (2026.acl-long)
Copied to clipboard
Zhengxi Lu, Jiabo Ye, Fei Tang, Yongliang Shen, Haiyang Xu, Ziwei Zheng, Weiming Lu, Ming Yan, Fei Huang, Jun Xiao, Yueting Zhuang
| Challenge: | GUI agents have demonstrated remarkable progress in automating complex user interface interactions . training such agents for long-horizon tasks remains challenging due to limited rewards and prohibitive costs. |
| Approach: | They propose a method that leverages expert trajectories as environment experiences for on-policy multi-turn training. |
| Outcome: | The proposed method achieves significant gains over the base model with 1K public trajectories as RL experiences . it achieves competitive performance against strong baselines such as UI-TARS-7B and GPT-4o . |
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)
Copied to clipboard
Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
| Challenge: | Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities. |
| Approach: | They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation. |
| Outcome: | The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks. |
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)
Copied to clipboard
Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang
| Challenge: | Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks. |
| Approach: | They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations. |
| Outcome: | The proposed pipeline can be used to study tool use under three scenarios. |
A Bounding Box is Worth One Token - Interleaving Layout and Text in a Large Language Model for Document Understanding (2025.findings-acl)
Copied to clipboard
Jinghui Lu, Haiyang Yu, Yanjie Wang, Yongjie Ye, Jingqun Tang, Ziwei Yang, Binghong Wu, Qi Liu, Hao Feng, Han Wang, Hao Liu, Can Huang
| Challenge: | Existing methods for integrating spatial layouts with text have limitations . existing methods produce overly long text sequences or lack autoregressive traits of LLMs . |
| Approach: | They introduce Interleaving Layout and Text in a Large Language Model (LayTextLLM) they use OCR-derived text and spatial layouts to integrate with LLMs for document understanding . |
| Outcome: | The proposed model shows an increase in performance in KIE and VQA tasks. |
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark (2026.acl-long)
Copied to clipboard
Kai Zou, Ziqi Huang, Yuhao Dong, Shulin Tian, Dian Zheng, Hongbo Liu, Jingwen He, Bin Liu, Yu Qiao, Ziwei Liu
| Challenge: | Existing evaluations treat visual understanding and generation in isolation or overlook tasks that inherently couple them. |
| Approach: | They propose a benchmark that examines the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |
| Outcome: | The proposed model systematically unfolds the bidirectional synergy between generation and understanding across eight reasoning-centric domains. |
Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods rely on rigid pipelines that overlook user needs and provide numerical results without clear explanations. |
| Approach: | They propose an evaluation framework that employs human-like strategies for efficient, dynamic, multi-round evaluations using only a few samples per round. |
| Outcome: | The evaluation agent framework reduces evaluation time to 10% of traditional methods while delivering comparable results. |
From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation (2026.acl-long)
Copied to clipboard
| Challenge: | a naive application of GRPO leads to conflicting gradient signals and a misalignment with the temporal dynamics of the diffusion process. |
| Approach: | They propose a framework that uses synergy-aware reward shaping to penalize conflicted reward signals and amplify synergies to provide a sharper and decisive gradient. |
| Outcome: | The proposed framework outperforms naive GRPO and Time-Aware Dynamic Weighting (TDW) on DreamBench, and achieves a state-of-the-art balance between ID preservation and prompt adherence. |
MGPO: Thinking with Images via Multi-Turn Grounding-Based Reinforcement Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | State-of-the-art large multimodal models face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task. |
| Approach: | They propose a multi-turn grounding-based policy optimization framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images based on model-predicted grounding coordinates within a multiple-turn conversation framework. |
| Outcome: | The proposed framework improves on Qwen2.5-VL-7B with 21K samples and surpasses OpenAI’s o1 and GPT-4o models on the out-of-distribution (OOD) V* Bench. |
A Survey on Open Information Extraction from Rule-based Model to Large Language Model (2024.findings-emnlp)
Copied to clipboard
Liu Pai, Wenyang Gao, Wenjie Dong, Lin Ai, Ziwei Gong, Songfang Huang, Li Zongsheng, Ehsan Hoque, Julia Hirschberg, Yue Zhang
| Challenge: | Open Information Extraction (OpenIE) is a key NLP task aimed at extracting structured information from unstructured text sources. |
| Approach: | They propose to categorize OpenIE into rule-based, neural, and pre-trained large language models and discuss each within a chronological framework. |
| Outcome: | The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. |
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models for visual information extraction suffer from limitations in scale and realism . ReceiptBench is a large-scale, human-annotated benchmark for receipts . |
| Approach: | They propose a large-scale, human-annotated benchmark for visual information extraction . the method organizes information extraction into four hierarchical sub-tasks . |
| Outcome: | The proposed method surpasses proprietary models on complex reasoning tasks. |
An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing (2024.acl-long)
Copied to clipboard
Ziwei Chai, Guoyin Wang, Jing Su, Tianjie Zhang, Xuanwen Huang, Xuwu Wang, Jingjing Xu, Jianbo Yuan, Hongxia Yang, Fei Wu, Yang Yang
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across a wide spectrum of tasks, but performance and reliability in certain specialized domains still fall short of expectations. |
| Approach: | They propose a unified generalist framework that facilitates seamless integration of multiple expert LLMs. |
| Outcome: | The proposed framework outperforms existing multi-LLM collaboration paradigms across six diverse expert domains. |
VChain: Chain-of-Visual-Thought for Reasoning in Video Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent video generation models struggle to synthesize complex dynamics with a coherent chain of consequences. |
| Approach: | They propose a framework that injects visual reasoning signals from multimodal models into video generation. |
| Outcome: | a new framework that leverages multimodal models to generate sparse keyframes significantly improves quality of generated videos. |