Papers by Jiang Gui
Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)
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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data. |
| Approach: | They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse. |
| Outcome: | The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)
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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting. |
| Approach: | LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data . |
| Outcome: | LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm . |
See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers (2026.acl-long)
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| Challenge: | External Human-Machine Interfaces (eHMIs) are emerging as promising solutions to address this communication gap. |
| Approach: | They propose a framework that uses vision-language models (VLMs) for perceptual evaluation as automated visual feedback to improve an LLM-based eHMI action designer. |
| Outcome: | The proposed framework outperforms prompt-only LLM designers and manually specified baselines in three eHMI modalities and multiple LLM model sizes. |
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding (2025.findings-naacl)
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Xingjian Diao, Chunhui Zhang, Weiyi Wu, Zhongyu Ouyang, Peijun Qing, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Multimodal foundation models have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. |
| Approach: | They propose a specialized cognitive module, temporal working memory, which selectively retains task-relevant information across temporal dimensions. |
| Outcome: | The module retains task-relevant information across temporal dimensions, ensuring that critical details are preserved throughout the processing of video and audio content. |
Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)
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| Challenge: | Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples. |
| Approach: | They propose a sparse learning framework specifically designed for Music AVQA to address these challenges. |
| Outcome: | The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets. |
Music Audio-Visual Question Answering Requires Specialized Multimodal Designs (2026.findings-acl)
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Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Tingxuan Wu, Ming Cheng, Soroush Vosoughi, Jiang Gui
| Challenge: | Music audio-visual question answering presents unique challenges with dense audio-visual content, intricate temporal dynamics, and the need for domain-specific knowledge. |
| Approach: | They analyze Music AVQA datasets and analyze their results to identify key design patterns . they propose concrete future directions for incorporating musical priors . |
| Outcome: | The proposed architectures are critical for success in Music AVQA, the authors argue . they suggest concrete future directions for incorporating musical priors . |
Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization (2026.findings-acl)
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Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Weiyi Wu, Keyi Kong, Lin Shi, Kaize Ding, Soroush Vosoughi, Jiang Gui
| Challenge: | Prior work has attempted to mitigate this issue by using adaptive reasoning strategies, but these methods overlook a fundamental bottleneck: visual perception failures. |
| Approach: | They propose a meta-reasoning controller that dynamically routes computation among three decision paths at each generation step. |
| Outcome: | The proposed method outperforms slow-thinking methods while producing shorter responses. |
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)
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| Challenge: | Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations. |
| Approach: | They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts . |
| Outcome: | The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current medical benchmarks have limitations in question design, data sources and evaluation methods. |
| Approach: | They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records . |
| Outcome: | The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. |
Learning Musical Representations for Music Performance Question Answering (2024.findings-emnlp)
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| Challenge: | Existing methods for audio-visual learning fail to consider the distinctive characteristics of instruments and music. |
| Approach: | They propose to integrate multimodal interactions within the context of music data and annotate and release rhythmic and music sources in the current music datasets to enable the model to learn music characteristics. |
| Outcome: | The proposed model can learn music characteristics from the current music datasets and align its predictions with the temporal dimension. |
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models (2025.emnlp-main)
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Xingjian Diao, Chunhui Zhang, Keyi Kong, Weiyi Wu, Chiyu Ma, Zhongyu Ouyang, Peijun Qing, Soroush Vosoughi, Jiang Gui
| Challenge: | Recent large language models have demonstrated impressive reasoning abilities, but their extension to the audio modality remains underexplored. |
| Approach: | They propose a rule-based reinforcement learning algorithm to equip LALMs with robust reasoning capabilities. |
| Outcome: | The proposed algorithm improves on the SoundMind benchmark. |
Assessing and Mitigating Medical Knowledge Drift and Conflicts in Large Language Models (2025.findings-emnlp)
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| Challenge: | Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice. |
| Approach: | They propose to evaluate how large language models manage knowledge conflicts in clinical guidelines. |
| Outcome: | The proposed benchmark evaluates how LLMs manage varied knowledge conflicts in clinical guidelines. |
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)
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| Challenge: | Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify. |
| Approach: | They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant. |
| Outcome: | The proposed framework yields faithful, fine-grained explanations while maintaining competitive accuracy. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments (2026.findings-acl)
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Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, Jiecao Chen
| Challenge: | Currently, there are no efficient reinforcement learning (RL) frameworks specifically designed for tool use. |
| Approach: | They propose an automated environment construction pipeline that incorporates scenario decomposition, document generation, function integration, complexity scaling, and localized deployment to enable high-quality training environments without external tools. |
| Outcome: | The proposed framework significantly improves the models’ tool-use performance without degrading their general capabilities. |
From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)
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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination. |
| Approach: | They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning. |
| Outcome: | The proposed model significantly improves fine-grained speech quality discrimination. |
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)
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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy . |
| Approach: | They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy. |
| Outcome: | The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training. |