Papers by Xinli Chen
MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)
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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng
| Challenge: | Existing approaches to cluster graphs with GNNs are limited due to label scarcity. |
| Approach: | They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals. |
| Outcome: | The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals. |
Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting (2023.emnlp-industry)
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| Challenge: | Recent advances in machine learning and artificial intelligence have opened up numerous opportunities and challenges in financial time series forecasting. |
| Approach: | They propose to use Large Language Models for explainable financial time series forecasting to leverage cross-sequence information and extract insights from text and price time series. |
| Outcome: | The proposed model outperforms ARMA-GARCH and gradient-boosting tree models while underperforming on other models. |
MaDS: Long-Horizon GUI Automation via Synergizing Dual-Layer Memory and Multi-Round Debate (2026.acl-long)
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| Challenge: | Current methods struggle to distinguish targets in low Signal-to-Noise Ratio environments and lack sufficient pre-execution verification to prevent error accumulation. |
| Approach: | They propose a Memory-augmented Debate System to ensure precise grounding across diverse interfaces and handle irreversible errors in extended workflows. |
| Outcome: | The proposed system achieves a 90.23% task success rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey. |
Orchestrating Audio: Multi-Agent Framework for Long-Video Audio Synthesis (2025.emnlp-main)
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| Challenge: | Existing methods for video-to-audio dubbing for long-form content are fragmented and lack dedicated datasets. |
| Approach: | They propose a multi-agent framework that offers a coordinated, multi-component approach to long-video audio generation. |
| Outcome: | The proposed method outperforms state-of-the-art V2A models in audio quality. |
PreGenie: An Agentic Framework for High-quality Visual Presentation Generation (2025.findings-emnlp)
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| Challenge: | Visual presentations are vital for effective communication, but they are limited by their complexity and lack of visual understanding. |
| Approach: | a new framework is proposed to generate high-quality visual presentations using multimodal large language models. |
| Outcome: | The proposed framework outperforms existing models in multimodal understanding and content consistency. |