Papers by Fangzhi Xu
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model. |
| Approach: | They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences. |
| Outcome: | The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks. |
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents (2024.acl-long)
Copied to clipboard
| Challenge: | Existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e-book). |
| Approach: | They propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate curation of GUI ground data. |
| Outcome: | The proposed agent improves ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. |
Vision-Language Models Can Self-Improve Reasoning via Reflection (2025.naacl-long)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) has been shown to improve the reasoning capability of large language models (LLMs). |
| Approach: | They propose a framework which iteratively enhances the model’s Vision-language Reasoning by Reflecting on CoT Rationales. |
| Outcome: | The proposed framework improves multimodal reasoning on vision-language tasks by 23% to 60% over baselines. |
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document. |
| Approach: | They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts. |
| Outcome: | The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets. |
MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)
Copied to clipboard
Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu
| Challenge: | Existing methods for optimizing reasoning quality are limited by overthinking. |
| Approach: | They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time. |
| Outcome: | The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%. |
TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge. |
| Approach: | They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts. |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
Copied to clipboard
Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)
Copied to clipboard
Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen
| Challenge: | Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected. |
| Approach: | They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans . |
| Outcome: | The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test . |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
Copied to clipboard
Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
Copied to clipboard
Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
| Approach: | They propose a model that integrates symbolic data into LLM training without loss of generality ability. |
| Outcome: | The proposed model performs better on symbol- and NL-centric tasks. |
PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering (2024.acl-long)
Copied to clipboard
| Challenge: | Existing logical reasoning tasks are challenging, especially for large language models. |
| Approach: | They propose a logic reasoning task model that transforms each logical sample into reasoning paths and propose an atom extension strategy supported by equivalent logical formulas to form new reasoning paths. |
| Outcome: | The proposed architecture achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. |
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to fine-tune Large Language Models without human annotations are lacking in the field of natural language training. |
| Approach: | They propose an environment-guided neural-symbolic self-training framework to overcome two main challenges: the scarcity of symbolic data and the limited proficiency of LLMs in processing symbolic language. |
| Outcome: | The proposed framework overcomes two main challenges: the scarcity of symbolic data, and the limited proficiency of LLMs in processing symbolic language. |
OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows (2026.acl-long)
Copied to clipboard
Qiushi Sun, Mukai Li, Zhoumianze Liu, Zhihui Xie, Fangzhi Xu, Zhangyue Yin, Kanzhi Cheng, Zehao Li, Zichen Ding, Qi Liu, Zhiyong Wu, Zhuosheng Zhang, Ben Kao, Lingpeng Kong
| Challenge: | Existing methods for detecting unsafe mobile GUI agents are underexplored. |
| Approach: | They propose a mobile agent safety detection framework that integrates a formal verifier and a VLM-based contextual judge to detect system-level violations. |
| Outcome: | The proposed framework achieves 10%–30% improvements over existing approaches across multiple metrics. |
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing agents lack generalization and specialization capabilities for open-ended tasks . specialized generalists are often underdeveloped in real-world environments . |
| Approach: | They propose a platform to dynamically integrate heterogeneous agents for automating computer tasks . they propose specialized generalist agent MetaAgent with the AgentToken strategy . |
| Outcome: | The proposed platform expands capabilities of existing agents in generalization and specialization . it can be used to automate open-ended tasks in real-world environments . |
𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation. |
| Approach: | They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value. |
| Outcome: | The proposed decoding strategy outperforms strong baselines in performance and efficiency. |