Papers by Guanyu Wang
Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
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| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
DKME: Rethinking Coupled Knowledge Memory for Lifelong Model Editing of Large Language Models (2026.findings-acl)
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| Challenge: | Existing memory-based editors suffer from catastrophic forgetting as edits accumulate. |
| Approach: | They propose a method which injects factual updates into large language models without retraining or finetuning into existing memory-based editors. |
| Outcome: | Experiments on HalluEditBench, CKnowEdit, and WikiDatacounterfact show that the proposed model achieves a more favorable trade-off between editing success and locality compared to baselines while maintaining more stable performance as the edit scale increases. |
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. |
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. |
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. |
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. |
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them (2025.findings-emnlp)
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Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu, Chujie Zhao, Ying Fang, Tianren Zhang, Feng Chen
| Challenge: | Existing large language models can perform abstract reasoning tasks but are they actually engaging in rule-based reasoning beyond mere memorization? |
| Approach: | They propose a method to examine whether large language models perform abstract reasoning . they fine-tune the model to learn those contradictory rules and assess its generalization ability . |
| Outcome: | The proposed approach examines whether large language models perform abstract reasoning by altering their original understanding of fundamental rules. |