Papers by Ziyu Zhao
C2DLM: Causal Concept-Guided Diffusion Large Language Models (2026.findings-acl)
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Kairong Han, Nuanqiao Shan, Ziyu Zhao, Zijing Hu, Xinpeng Dong, Ye Jun Jian, Lujia Pan, Fei Wu, Kun Kuang
| Challenge: | Autoregressive (AR) and diffusion language models (DLMs) suffer from insufficient reasoning capabilities. |
| Approach: | They propose a fully connected Diffusion Language Model that uses a concept-level causal graph to guide attention to learn causal relationships between concepts. |
| Outcome: | The proposed model achieves a 12% improvement and 3.2 training speedup on the COT-OrderPerturb task, along with an average gain of 1.31% across six downstream reasoning tasks. |
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs (2025.findings-emnlp)
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| Challenge: | Sparse Mixture-of-Experts (SMoE) architectures require loading all expert parameters . previous work focused on expert pruning and merging but focused on neuron-level structure . |
| Approach: | They propose a task-agnostic framework for expert pruning and reconstruction . it prunes redundant experts using router statistics, then decomposes them into neuron-level expert segments . |
| Outcome: | The proposed framework reduces the number of experts and memory usage, making it easier to deploy. |
StructuThink: Reasoning with Task Transition Knowledge for Autonomous LLM-Based Agents (2025.findings-emnlp)
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| Challenge: | StructuThink framework enhances LLMs' ability to ground decisions in domain-specific scenarios. |
| Approach: | They propose a knowledge-structured reasoning framework that enhances LLM-based agents with explicit decision constraints. |
| Outcome: | The proposed framework achieves higher task success rates and more efficient action sequences than baseline methods. |
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models (2025.findings-emnlp)
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| Challenge: | Sparse Autoencoders (SAEs) can disentangle complex features into more interpretable components. |
| Approach: | They propose to use Sparse Autoencoders to disentangle LLM features into more interpretable components. |
| Outcome: | The proposed method disentangles complex features into more interpretable components. |
LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild (2024.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) is an effective yet efficient solution for fine-tuning large language models. |
| Approach: | They propose a low-rank Adaptation framework that retrieves and composes multiple LoRAs according to input prompts. |
| Outcome: | Experimental results show that LoraRetriever outperforms baselines in terms of performance and versatility. |
SciMDR: Advancing Scientific Multimodal Document Reasoning (2026.acl-long)
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| Challenge: | Current models struggle to provide reliable assistance in real-world scientific workflows because evidence is distributed across long, multimodal documents. |
| Approach: | They propose a framework for QA Synthesis and document-scale regrounding that generates faithful, isolated QA pairs and reasoning on focused segments. |
| Outcome: | The proposed framework achieves significant improvements across multiple QA benchmarks, particularly in tasks requiring complex document-level reasoning. |
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)
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| Challenge: | Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios. |
| Approach: | They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families. |
| Outcome: | The proposed method is superior to existing methods and can handle low-resource languages. |
Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction (2026.findings-eacl)
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Chong Zhang, Yixi Zhao, Yulu Xie, Chenshu Yuan, Yi Tu, Ya Guo, Mingxu Chai, Ziyu Shen, Yue Zhang, Qi Zhang
| Challenge: | PTLMs have shown remarkable success in multiple information extraction tasks . however, their performance in real-world scenarios falls short of expectations . |
| Approach: | They propose to use an entity-centric dataset to evaluate PTLMs' performance . they find that inadequate annotations in benchmark datasets lead to spurious correlations . |
| Outcome: | The proposed dataset disentangles the falsely-coupled segment and entity annotations that arises from the block-level annotation of FUNSD. |
On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)
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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang
| Challenge: | Adapting general multimodal large language models to specific domains is important for practical applications. |
| Approach: | They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks . |
| Outcome: | The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines. |
H-LegalKI: A Hierarchical Legal Knowledge Integration Framework for Legal Community Question Answering (2024.findings-emnlp)
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| Challenge: | Legal question answering (LQA) aims to bridge the gap between limited availability of legal professionals and the extensive volume of legal issues. |
| Approach: | They propose a legal knowledge retriever and a hierarchical legal knowledge integration framework to address multiple user-specific circumstances. |
| Outcome: | The proposed framework outperforms baselines on the legal community question-answering dataset. |
KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)
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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong
| Challenge: | Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge. |
| Approach: | They propose a benchmark to evaluate domain specialization methods in real-world software development. |
| Outcome: | KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development. |
LRBench and Judge-R1: Principled Evaluation and Training of LLM-Based Judges for Long-Context Reasoning (2026.findings-acl)
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| Challenge: | Existing benchmarks for evaluating large language models (LLMs) under long contexts are underexplored. |
| Approach: | They propose a large-scale benchmark for evaluating large language models (LLMs) that combines reinforcement learning with multi-turn search to enable grounded and principle-aware evaluation. |
| Outcome: | The proposed model outperforms single-turn baselines across domains and principles. |
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer (2024.acl-long)
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| Challenge: | Existing methods for short TST are difficult to implement and can cause content degradation. |
| Approach: | They propose a method to vary the style polarity of text while preserving semantic content. |
| Outcome: | The proposed method improves over baselines and is highly efficient. |
SocioBench: Modeling Human Behavior in Sociological Surveys with Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) lack large-scale, systematically constructed benchmarks for evaluating their alignment with real-world social attitudes. |
| Approach: | They propose a benchmark to assess LLMs' alignment with real-world social attitudes . they find LLM models achieve only 30–40% accuracy when simulating individuals . |
| Outcome: | The proposed benchmark shows that LLMs achieve only 30% accuracy when simulating individuals in complex survey scenarios. |
OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)
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Xueyu Hu, Tao Xiong, Biao Yi, Zishu Wei, Ruixuan Xiao, Yurun Chen, Jiasheng Ye, Meiling Tao, Xiangxin Zhou, Ziyu Zhao, Yuhuai Li, Shengze Xu, Shenzhi Wang, Xinchen Xu, Shuofei Qiao, Zhaokai Wang, Kun Kuang, Tieyong Zeng, Liang Wang, Jiwei Li, Yuchen Eleanor Jiang, Wangchunshu Zhou, Guoyin Wang, Keting Yin, Zhou Zhao, Hongxia Yang, Fan Wu, Shengyu Zhang, Fei Wu
| Challenge: | a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide. |
| Approach: | They propose to build OS Agents that operate within operating systems' GUIs and GUIs . they examine evaluation metrics and benchmarks to identify promising directions . |
| Outcome: | The proposed agents are based on operating systems (OS) and operating systems frameworks. |
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models (2025.emnlp-main)
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| Challenge: | Existing fine-tuning paradigms focus on aligning LLMs with task-specific objectives. |
| Approach: | They propose a pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training. |
| Outcome: | The proposed pipeline achieves an average improvement of 5.76% on the STG dataset and 1.56% on downstream tasks. |