Papers by Miao Yu
Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models (2026.acl-long)
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Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen
| Challenge: | Existing defenses rely on impractical assumptions about trigger settings to mitigate backdoor attacks . a recent study found that small amounts of training data can systematically induce harmful behaviors in large language models. |
| Approach: | They propose a backdoor defense framework that requires no prior knowledge of trigger settings . they use a two-stage process to aggregate backdoor representations and fine-tune recovery . |
| Outcome: | The proposed defense reduces the average Attack Success Rate to 4.41% across multiple benchmarks . the proposed framework generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios. |
ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)
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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao
| Challenge: | ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge. |
| Approach: | They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks . |
| Outcome: | ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering. |
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection (2021.naacl-main)
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| Challenge: | Existing datasets for sarcasm detection are limited due to the difficulty in acquiring ground-truth annotations. |
| Approach: | They propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. |
| Outcome: | The proposed approach outperforms transfer learning and meta-learning baselines and achieves 10.02% performance gain on the iSarcasm dataset. |
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning (2024.acl-long)
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| Challenge: | Recent research shows that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. |
| Approach: | They propose to use contrastive learning to promote global feature alignment and learning counterfactual clues to improve model performance. |
| Outcome: | The proposed method outperforms the state-of-the-art on out-of distribution (OOD) datasets. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs. |
| Approach: | They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction. |
| Outcome: | The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models. |
DocumentNet: Bridging the Data Gap in Document Pre-training (2023.emnlp-industry)
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| Challenge: | Document understanding tasks are a tedious task that requires extensive training and privacy constraints. |
| Approach: | They propose a method to collect weakly labeled data from the web to benefit VDER training . the collected dataset does not depend on specific document types or entity sets . |
| Outcome: | The proposed method does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. |
AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)
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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang
| Challenge: | Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs. |
| Approach: | They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure. |
| Outcome: | The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic. |
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)
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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing
| Challenge: | Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases. |
| Approach: | They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. |
| Outcome: | The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty. |
SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)
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| Challenge: | Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent. |
| Approach: | They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent . |
| Outcome: | The proposed method improves on three multi-intent datasets on multi-tasks. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems (2025.acl-long)
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| Challenge: | Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, but their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns. |
| Approach: | They propose a topology-guided security lens and treatment for robust LLM-MAS that leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation. |
| Outcome: | Experiments show that the proposed security lens recovers 40% of the performance under various attack strategies and integrates with mainstream MAS with security guarantees. |
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)
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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu
| Challenge: | Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents. |
| Approach: | They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process. |
| Outcome: | The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets . |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition (2024.findings-emnlp)
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Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan
| Challenge: | emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. |
| Approach: | They propose a two-stage compression method tailored for Mixture of Experts to reduce the model size and decrease the computational cost. |
| Outcome: | The proposed method reduces model size and improves inference efficiency while maintaining performance in various zero-shot tasks. |
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)
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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
| Challenge: | Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability. |
| Approach: | They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification. |
| Outcome: | Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
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| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)
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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu
| Challenge: | Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections. |
| Approach: | They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight. |
| Outcome: | The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%. |