Papers by Zhe Pan
SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine (2025.findings-naacl)
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Xiaochen Wang, Junqing He, Liang Chen, Gholamreza Haffari, Yiru Wang, Zhe Yang, Xiangdi Meng, Kunhao Pan, Zhifang Sui
| Challenge: | Multi-hop Question Answering (MHQA) is a challenging task that requires models to answer multiple questions with multiple passages. |
| Approach: | They propose a self-guided prompting finite state machine to improve multi-hop reasoning abilities by iterating over multiple questions and correcting itself to improve accuracy. |
| Outcome: | The proposed approach outperforms baselines on Musique and other datasets. |
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)
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Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
| Challenge: | Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models. |
| Approach: | They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs. |
| Outcome: | The proposed agent performs better than open-source models and the closed-source model, GPT-4o. |
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation (2026.findings-acl)
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| Challenge: | Agentic SQL is a framework for multiturn agent learning, but it is limited to single-turn paradigms. |
| Approach: | They propose a framework that provides a universal two-tiered reward mechanism for credit assignment . they propose 'Aggregated Trajectory Reward' to resolve multi-turn credit assignment. |
| Outcome: | The proposed framework outperforms SOTA Arctic-Text2SQL-R1-7B on BIRD and Spider 2.0 using identical models. |
Hyperbolic Hierarchy-Aware Knowledge Graph Embedding for Link Prediction (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods are built on Euclidean space, which are difficult to handle hierarchical structures. |
| Approach: | They propose a KGE model with extended Poincaré Ball and polar coordinate system to capture hierarchical structures. |
| Outcome: | The proposed model captures hierarchical relationships with extended Poincaré Ball and polar coordinate system in hyperbolic space and achieves state-of-the-art results on part of link prediction tasks. |
SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)
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| Challenge: | Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies. |
| Approach: | They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters. |
| Outcome: | The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems. |