Papers by Guanyu Li
MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization. |
| Approach: | They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs. |
| Outcome: | The proposed framework outperforms existing benchmarks in Graph-related tasks. |
Less is More: Knowledge-Aware Compression for Long Legal Judgment Prediction (2026.findings-acl)
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| Challenge: | Recent advances leverage large language models (LLMs) for legal reasoning, but they face high computational costs and information degradation when handling long cases. |
| Approach: | They propose a framework that selectively retains legally relevant information while reducing redundant or less informative content, enabling efficient and accurate long-context reasoning. |
| Outcome: | The proposed framework outperforms existing methods on four real-world datasets spanning multiple jurisdictions and languages. |
Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization (2026.acl-long)
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| Challenge: | Recent studies attempt to obtain optimal or suboptimal arrangements based on statistical results or using dataset-based search, but these methods increase inference overhead while leaving the model’s inherent order bias unresolved. |
| Approach: | They propose Dual Group Advantage Optimization (DGAO) which aims to improve model accuracy and order stability simultaneously. |
| Outcome: | The proposed method improves model accuracy and order stability while penalizing order-sensitive or incorrect responses. |
RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains (2026.acl-long)
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| Challenge: | Current reinforcement learning paradigms rely on outcome-based rewards, overlooking latent logical fallacies in intermediate steps. |
| Approach: | They propose a specialized audit model augmented with external tools to identify local logical ruptures and calibrate reward signals. |
| Outcome: | The proposed framework improves accuracy and logical rigor in high-stakes domains. |
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages (2024.acl-long)
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Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing research focuses on enhancing LLMs capabilities through tool utilization. |
| Approach: | They propose a framework to investigate safety issues in large language models in tool learning . they propose malicious queries and jailbreak attacks in the input stage . |
| Outcome: | The proposed framework investigates six safety scenarios for LLMs in tool learning . the data will be released upon acceptance of the proposed framework . |
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains (2025.findings-acl)
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| Challenge: | Existing Large Language Models (LLMs) generate brief answers without reasoning processes and explanations. |
| Approach: | They propose supervised fine-tuning and tree search to enhance LLMs’ reasoning capabilities on domain tasks. |
| Outcome: | The proposed model improves on stock investment recommendation and legal reasoning QA tasks. |
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios (2025.coling-main)
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Junjie Ye, Guanyu Li, SongYang Gao, Caishuang Huang, Yilong Wu, Sixian Li, Xiaoran Fan, Shihan Dou, Tao Ji, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing evaluations of tool learning focus on validation of tools for large language models with expected outcomes, but this focus ignores the complex capabilities required for LLMs to effectively use tools. |
| Approach: | They propose a fine-grained system for evaluation of large language models’ tool learning capabilities in authentic scenarios. |
| Outcome: | The proposed system examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. |
DefGen-Bench: A Benchmark for Chinese Criminal Defence Opinion Generation in LegalAI (2026.acl-long)
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| Challenge: | Existing LegalAI tasks are descriptive or predictive, requiring the users to translate the information into legal reasoning. |
| Approach: | They propose a task to generate a structured defence opinion conditioned jointly on an indictment and the defendant’s stated opinion, which often present conflicting claims. |
| Outcome: | The proposed approach improves on eight large language models (LLMs) and shows that it is more efficient than previous approaches. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning (2024.emnlp-main)
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Junjie Ye, Yilong Wu, Songyang Gao, Caishuang Huang, Sixian Li, Guanyu Li, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current research emphasizes LLMs’ capacity to utilize tools in well-structured environments while overlooking their stability when confronted with the inevitable noise of the real world. |
| Approach: | They propose a multi-level benchmark to evaluate the robustness of large language models in tool learning by establishing five external environments with varying levels of noise. |
| Outcome: | The proposed model outperforms the GPT-4 model in tool learning in three critical phases: tool selection, parameter identification, and content filling. |
GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors (2025.findings-emnlp)
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Xinrong Chen, Hengyuan Zhang, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Hayden Kwok-Hay So, Ngai Wong
| Challenge: | Low-Rank Adaptation (LoRA) methods are efficient for a large language model with reduced computational costs. |
| Approach: | They propose a layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors. |
| Outcome: | The proposed method achieves superior or comparable performance to all baselines on three backbone models. |
LoRACoE: Improving Large Language Model via Composition-based LoRA Expert (2025.emnlp-main)
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| Challenge: | Recent studies show that the Mixture of Experts architecture improves performance of large language models. |
| Approach: | They propose a method to build static experts using LoRA parameters . they propose to use rank-level parameters to build experts based on rank-based parameters based in LoRA module. |
| Outcome: | The proposed method improves task performance across a broader range of tasks. |