Papers by Kaitao Song

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)

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Dingyao Yu, Kaitao Song, Peiling Lu, Tianyu He, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian

Challenge:	MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements.
Approach:	a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests .
Outcome:	the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements.

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang

Semantic Text Processing

Challenge:	Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems.
Approach:	They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings.
Outcome:	The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks .

Towards Understanding Omission in Dialogue Summarization (2023.acl-long)

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Yicheng Zou, Kaitao Song, Xu Tan, Zhongkai Fu, Qi Zhang, Dongsheng Li, Tao Gui

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem .
Approach:	They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances .
Outcome:	The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available .

Foresight Optimization for Strategic Reasoning in Large Language Models (2026.acl-long)

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Jessie Wang, Jiawen Duan, Jian Wang, Kaitao Song, Chunpu Xu, Johnny K. W. Ho, YU Fenggang, Johan F. Hoorn, Wenjie Li

Language Models

Challenge:	Existing reasoning enhancement methods do not capture foresight in LLMs.
Approach:	They propose to integrate opponent modeling principles into policy optimization to enhance strategic reasoning in LLMs by integrating opponent modeling into policy.
Outcome:	The proposed method outperforms existing reasoning-based LLMs in out-of-domain scenarios and shows that it significantly enhances strategic reasoning across LLM of varying sizes and origins.

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (2021.acl-long)

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Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu

Language Models

Challenge:	Existing systems for rap generation focus on rhyming lyrics but ignore rhythmic beats . rap lyrics need to be semantically meaningful and fashionable to convey interesting stories .
Approach:	They develop a Transformer-based rap generation system that can model both rhymes and rhythms.
Outcome:	The proposed system generates high-quality raps with rhymes and rhythms . it is based on a Transformer-based language model .

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Information Retrieval

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

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Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

Double Path Networks for Sequence to Sequence Learning (C18-1)

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Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu

Language Models

Challenge:	Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular .
Approach:	They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning.
Outcome:	The proposed method significantly improves performance over state-of-the-art systems.

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction (2025.naacl-long)

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, Deqing Yang

Semantic Text Processing

Challenge:	EASYTOOL combines tools from diverse tool documentation into a single tool instruction.
Approach:	They propose a framework that transforms tool documentation into a unified tool instruction.
Outcome:	EASYTOOL combines extensive tool documentation into a concise tool instruction . it reduces token consumption and improves performance of LLM-based agents .

Improving Large Language Models in Event Relation Logical Prediction (2024.acl-long)

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Meiqi Chen, Yubo Ma, Kaitao Song, Yixin Cao, Yan Zhang, Dongsheng Li

Language Models

Challenge:	Event relation extraction tasks require rigorous logical reasoning and semantic comprehension, a challenge for narrative understanding and reasoning.
Approach:	They propose three approaches to endow LLMs with event relation logic to generate more coherent answers across different scenarios.
Outcome:	The proposed approach improves on a set of ERE tasks and provides insights for future work.

Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human–Agent Interaction (2026.acl-long)

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Zisu Huang, Muzhao Tian, Xiaohua Wang, Jingwen Xu, Zhengkang Guo, Qi Qian, Kaitao Song, Jiakang Yuan, Changze Lv, Xiaoqing Zheng

Challenge:	Existing systems that use memory as an "all-or-nothing" approach to memory usage are often static and rely on experience-following tendencies.
Approach:	They propose a framework that allows users to dynamically regulate memory reliance by adding context into the model's prompt.
Outcome:	The proposed model outperforms prompting and memory masking strategies in multiple scenarios.