Papers by Qiang Luo
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction (2025.acl-long)
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Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, Yongbin Li
| Challenge: | Existing methods focus on replicating dialogues in textual form, neglecting the role’s voice traits as a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. |
| Approach: | They propose a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. |
| Outcome: | The proposed model exhibits role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. |
ReCreate: Reasoning and Creating Domain Agents Driven by Experience (2026.acl-long)
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Zhezheng Hao, Hong Wang, Jian Luo, Jianqing Zhang, Yuyan Zhou, Qiang Lin, Can Wang, Hande Dong, Jiawei Chen
| Challenge: | Large Language Model (LLM) agents are reshaping the industrial landscape, but tasks differ widely, making them labor-intensive to build. |
| Approach: | They propose an experience-driven framework for the automatic creation of domain agents . they leverage agent interaction histories to provide rich concrete signals on success or failure . |
| Outcome: | The proposed framework outperforms human-designed agents and existing methods in experiments across diverse domains. |
Guideline Learning for In-Context Information Extraction (2023.emnlp-main)
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| Challenge: | Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters. |
| Approach: | They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines. |
| Outcome: | The proposed framework improves the performance of in-context IE by synthesizing and following guidelines. |
CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision (2025.acl-long)
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YifeiLu YifeiLu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, Feiliang Ren
| Challenge: | Existing approaches to tool invocation are often unnecessarily long and require lengthy reasoning paths. |
| Approach: | They propose a framework for stepwise code generation that improves LLM tool invocation . they incorporate two distinct process rewards: the On-the-spot and the Latent Reward . |
| Outcome: | The proposed framework improves LLM tool invocation by leveraging the concise nature of code. |
Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures (2024.findings-naacl)
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| Challenge: | Applying deep learning to binary code analysis has drawn great attention because of its notable performance. |
| Approach: | They propose to learn cross-architecture instruction embeddings where semantically-similar instructions have close embeddements in a shared space. |
| Outcome: | The proposed approach generates high-quality CAIE with good transferability on four ISAs. |
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends (2026.findings-acl)
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Yihao Ding, Siwen Luo, Yue Dai, Yanbei Jiang, Zechuan Li, Qiang Sun, Geoffrey Martin, Wei Liu, Yifan Peng
| Challenge: | Visually Rich Document Understanding (VRDU) frameworks are a key area of research . early approaches to VRDU relied on manually crafted rules and domain-specific heuristics . conventional deep learning approaches do not integrate the diverse modalities in documents . |
| Approach: | They review recent advances in MLLM-based Visually Rich Document Understanding (VRDU) their findings highlight emerging trends and promising research directions . |
| Outcome: | The proposed frameworks are scalable, reliable, and adaptable, the authors argue . their findings highlight emerging trends and promising research directions . |
DocSpiral: A Platform for Integrated Assistive Document Annotation through Human-in-the-Spiral (2025.acl-demo)
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| Challenge: | Documents that are image-based are difficult to extract because of document variability. |
| Approach: | They propose a human-in-the-spiral assistive document annotation platform to extract structured data from document collections. |
| Outcome: | The proposed framework reduces annotation time by at least 41% while showing consistent performance gains over three iterations. |
OpenOmni: A Collaborative Open Source Tool for Building Future-Ready Multimodal Conversational Agents (2024.emnlp-demo)
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| Challenge: | OpenOmni is an open-source, end-to-end pipeline benchmarking tool for multimodal conversational agents. |
| Approach: | They developed an open-source, end-to-end pipeline benchmarking tool to help solve these issues. |
| Outcome: | OpenOmni integrates speech-to-text, emotion detection, and large language models with the ability to integrate customized models. |
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution (2025.findings-acl)
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| Challenge: | Existing fine-grained attribution methods rely on model-internal similarity metrics but lack a fine-grain representation of the data. |
| Approach: | They propose to use model-internal similarity metrics to validate RAG-generated content . they aggregate token-wise evidence through set union operations and integrate dependency parsing to enrich the semantic completeness of target spans. |
| Outcome: | The proposed method outperforms all prior works in the validation of RAG-generated content. |
Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective (2026.acl-long)
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Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo, Jiarui Yu, Hande Dong, Qiang Lin, Can Wang, Jiawei Chen
| Challenge: | Large Language Models (LLMs) have remarkable reasoning capabilities in complex tasks such as mathematics and coding. |
| Approach: | They propose an entropy-modulation method that adaptively reweighs tokens based on theoretically-estimated entropic variations. |
| Outcome: | The proposed method outperforms state-of-the-art methods in six mathematical reasoning and three coding benchmarks. |
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)
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Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou
| Challenge: | Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training. |
| Approach: | They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable. |
| Outcome: | Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines. |
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture (2025.findings-emnlp)
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| Challenge: | Using deep learning to detect malware has attracted great attention due to its notable performance. |
| Approach: | a new approach uses Neural Machine Translation and Normalizing Flows to apply deep learning to malware detection. |
| Outcome: | The proposed approach reduces the burden of data collection by enabling malware detection across multiple ISAs. |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
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Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |