Papers by Junjie Liu

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

Challenge:	Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach:	They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome:	The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

Semantic Text Processing

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

Formally Specifying the Intended Behavior of the Program: LLM-Driven Neuro-Symbolic Program Specification Synthesis (2026.acl-demo)

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Cheng Wen, Hu Junjie, YiKun Hu, Jie Su, Bin Yu, Dugang Liu, Zhiwu Xu, Weidi Sun, Shengchao Qin, Cong Tian

DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)

Challenge:	Formal verification typically requires developers to write detailed formal specifications . a formal verification system that generates candidate specifications is costly and error-prone .
Approach:	They propose an LLM-driven neuro-symbolic demonstration system that reframes specification writing as constrained structured synthesis.
Outcome:	The proposed system reduces hallucinations and produces proof-ready annotations.

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Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang

Challenge:	Existing methods for training reward models are vulnerable to context neglect and degraded accuracy.
Approach:	They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response.
Outcome:	The proposed model improves performance in RLHF and improves accuracy in other settings.

TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)

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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang

Dialogue Systems & Conversational Agents

Challenge:	Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots.
Approach:	They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios.
Outcome:	The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning .

JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs (2025.acl-long)

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Junjie Chu, Yugeng Liu, Ziqing Yang, Xinyue Shen, Michael Backes, Yang Zhang

Robustness in NLP

Challenge:	Large language models (LLMs) have been used to mitigate misuse and to align with human values.
Approach:	They propose to use large-scale evaluations of various jailbreak attacks to identify key patterns and test them under eight advanced defenses.
Outcome:	The proposed attacks achieve high success rates but are easy to mitigate by defenses.

ArchiDocGen: Multi-Agent Framework for Expository Document Generation in the Architectural Industry (2025.acl-industry)

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Junjie Jiang, Haodong Wu, Yongqi Zhang, Songyue Guo, Bingcen Liu, Caleb Chen Cao, Ruizhe Shao, Chao Guan, Peng Xu, Lei Chen

Text Generation

Challenge:	drafting method statements is labor-intensive and time-consuming . traditional methods involve using static templates filled in manually by engineers .
Approach:	They propose a framework that automates method statement generation by using multi-agent collaboration.
Outcome:	The proposed framework achieves 4.38 ContentScore, excelling in specialization, completeness, organization, and clarity.

A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)

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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang

MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages.
Approach:	They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies.
Outcome:	The proposed model can be used to understand and generate human natural languages.

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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian

VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval (2024.acl-long)

Challenge:	despite the growing demand for multimodal retrieval, there is a lack of training data.
Approach:	They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data.
Outcome:	The proposed method outperforms baseline models on 70 more datasets and can scale up.

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Junjie Zhou, Zheng Liu, Shitao Xiao, Bo Zhao, Yongping Xiong

Semantic Text Processing

Challenge:	Existing methods for multimodal retrieval are mostly text-oriented, which lack the capability to process visual information.
Approach:	They propose a multi-modal multi-text embedding model VISTA which extends a powerful text encoder with the image understanding capability by introducing visual token embedds.
Outcome:	The proposed model achieves superior performance across a variety of multi-modal retrieval tasks in zero-shot and supervised settings.

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)

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Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

QFinZero: A Unified Financial Toolchain for LLM-Based Trading Agents (2026.acl-demo)

Challenge:	Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs.
Approach:	They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.
Outcome:	The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.

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Haochen Luo, Yifan LI, Ho Tin Ko, An Binh Minh, Junjie Xu, Tang Pok Hin, Wang Chak Wong, Gao Yuan, Zhengzhao Lai, Yuan Zhang, Chen Liu

Information Retrieval

Challenge:	Existing trading systems rely on fragmented and task-specific APIs, resulting in inconsistent schemas and limited reproducibility.
Approach:	They propose a unified trading environment for large language model (LLM) agents that standardizes three core capabilities . they argue that such a standardized trading environment is essential for scalable research on LLM-based financial agents.
Outcome:	The proposed trading environment reduces engineering overhead and supports reproducible evaluation through comprehensive logging and deterministic replay.

All Information is Valuable: Question Matching over Full Information Transmission Network (2022.findings-naacl)

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Le Qi, Yu Zhang, Qingyu Yin, Guidong Zheng, Wen Junjie, Jinlong Li, Ting Liu

Learning to Detect Noisy Labels Using Model-Based Features (2022.findings-emnlp)

Challenge:	Existing methods for question matching only transmit one kind of information while failing to utilize both kinds of information simultaneously.
Approach:	They propose a question matching network that can transmit both representation and interactive information together in a simultaneous fashion.
Outcome:	The proposed approach outperforms strong baseline models on two standard benchmarks.

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Zhihao Wang, Zongyu Lin, Junjie Wen, Xianxin Chen, Peiqi Liu, Guidong Zheng, Yujun Chen, Zhilin Yang

CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges (2025.acl-long)

Challenge:	Existing approaches to reduce label noise rely on heuristics and sample losses.
Approach:	They propose a method that transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features.
Outcome:	Empirically, the proposed approach improves over strong baselines on a wide range of tasks including text classification and speech recognition.

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Haitao Li, Junjie Chen, Qingyao Ai, Zhumin Chu, Yujia Zhou, Qian Dong, Yiqun Liu

Robustness in NLP

Challenge:	Empirical evaluations of large language models demonstrate that they improve performance in a wide range of tasks.
Approach:	They propose a label-free method for mitigating selection bias during inference by reformulating debiasing as an optimization task.
Outcome:	The proposed method mitigates selection bias and improves performance compared to existing methods.

Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)

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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu

LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)

Challenge:	Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks.
Approach:	They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability.
Outcome:	The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios.

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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng

The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)

Challenge:	E-commerce search relevance is a critical component of retrieval systems.
Approach:	They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies.
Outcome:	The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain.

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Mo Yu, Lemao Liu, Junjie Wu, Tsz Ting Chung, Shunchi Zhang, Jiangnan Li, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution.
Approach:	They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena.
Outcome:	The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena.

GUICourse: From General Vision Language Model to Versatile GUI Agent (2025.acl-long)

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Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun

Visual Data in NLP

Challenge:	Graphical User Interfaces (GUIs) are a pivotal medium for human-computer interaction.
Approach:	They propose a series of datasets for training visual-based GUI agents using general VLMs.
Outcome:	The proposed GUICourse datasets show that even a small-sized GUI agent performs better on GUI tasks.

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

Understanding LLMs’ Fluid Intelligence Deficiency: An Analysis of the ARC Task (2025.naacl-long)

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Junjie Wu, Mo Yu, Lemao Liu, Dit-Yan Yeung, Jie Zhou

Challenge:	Recent research on fluid intelligence assessments has highlighted significant deficiencies in LLMs’ abilities.
Approach:	They analyze the challenges LLMs face in demonstrating fluid intelligence through controlled experiments using the most representative ARC task as an example.
Outcome:	The proposed model shows that it lacks the ability to combine skill composition and abstract input formats and lacks left-to-right decoding.

XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021.emnlp-main)

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Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson

Multilinguality

Challenge:	Recent advances in multilingual natural language processing have improved performance on benchmarks such as XTREME and XGLUE by 13 points . however, improvements have been easier to achieve in some tasks than others .
Approach:	They extend XTREME to XTRAME-R, which includes ten natural language understanding tasks and covers 50 typologically diverse languages.
Outcome:	The proposed framework improves the performance on the XTREME multilingual benchmark by 13 points compared to human-level performance on English transfer learning.

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

ERCThinker: Fast-Slow Thinking for Emotion Recognition in Conversation (2026.acl-long)

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

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Yumeng Fu, Weitao Huang, Junjie Wu, Hao Teng, Shouduo Shang, Meishan Zhang, Bingquan Liu

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

Challenge:	Existing methods for ERC lack interpretability and shallow semantics capture deep semantics.
Approach:	They propose a Fast-Slow thinking framework for Emotion Recognition in Conversation . they use fine-grained emotion reasoning chains to capture deep semantics .
Outcome:	The proposed framework achieves state-of-the-art in explanation and judgment on a benchmark dataset.

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems (2026.acl-long)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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Mengzhuo Chen, Junjie Wang, Fangwen Mu, Yawen Wang, Zhe Liu, Huanxiang Feng, Qing Wang

LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)

Challenge:	Existing benchmarks rely on partially observable traces that capture only agent outputs . lack of full execution traces obscures many failure causes, authors argue .
Approach:	They propose a benchmark that allows attribution under full execution observability . they find full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart .
Outcome:	The proposed benchmark improves attribution accuracy by up to 76.5% over a partial-observation counterpart.

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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu

Dialogue Systems & Conversational Agents

Challenge:	Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations.
Approach:	They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings.
Outcome:	The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality.

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)

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Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang

Robustness in NLP

Challenge:	Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries.
Approach:	They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.
Outcome:	The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements.

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection (2026.findings-acl)

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Tao Liu, Taiqiang Wu, Runming Yang, Shaoning Sun, Junjie Wang, Yujiu Yang

Challenge:	Traditional fine-tuning ignores one-to-many nature of language, leading to overfitting . authors propose a method to fine- tune LLMs by leveraging tokens.
Approach:	They propose a method to fine-tune Large Language Models by leveraging tokens to mask low-probability tokens.
Outcome:	The proposed method outperforms baselines on general reasoning and mathematical benchmarks.

Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)

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Junjie Zhang, Rushuai Yang, Shunyu Liu, Ting-En Lin, Fei Huang, Yi Chen, Yongbin Li, Dacheng Tao

Towards General Error Diagnosis via Behavioral Testing in Machine Translation (2023.findings-emnlp)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis.
Approach:	They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement.
Outcome:	The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks.

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Junjie Wu, Lemao Liu, Dit-Yan Yeung

Multilinguality

Challenge:	Existing behavioral testing approaches only evaluate translation quality without references, restricting diagnosis to specific types of errors.
Approach:	They propose a bilingual translation pair generation based behavior testing framework that auto-generates test cases and pseudo-references to facilitate general error diagnosis.
Outcome:	The proposed framework can provide comprehensive and accurate behavioral testing results for general error diagnosis on machine translation systems.

Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups (2026.findings-acl)

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Geng Liu, Li Feng, Junjie Mu, Mengxiao Zhu, Francesco Pierri

StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

Challenge:	Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they reflect and amplify social biases.
Approach:	They propose a Mandarin-specific evaluation framework to examine social identity biases in Chinese LLMs using Mandarin-based prompts.
Outcome:	The proposed framework compares ingroup (“We”) and outgroup (“They”) framings across 240 social groups salient in the Chinese context, using a two-tiered measurement framework that assesses both sentiment and toxicity.

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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang

Programming Languages in NLP

Challenge:	Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
Approach:	They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality.
Outcome:	The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality.

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

LaERC-S: Improving LLM-based Emotion Recognition in Conversation with Speaker Characteristics (2025.coling-main)

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Yumeng Fu, Junjie Wu, Zhongjie Wang, Meishan Zhang, Lili Shan, Yulin Wu, Bingquan Liu

Sentiment Analysis

Challenge:	Emotion recognition in conversation (ERC) is a task of discerning human emotions for each utterance within a conversation.
Approach:	They propose a framework that uses large language models to analyze speaker characteristics . they use two-stage learning to make the models reason speaker characteristics and track emotion of the speaker .
Outcome:	The proposed framework outperforms existing methods on three benchmark datasets.

AndroidGen: Building an Android Language Agent under Data Scarcity (2025.acl-long)

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Hanyu Lai, Junjie Gao, Xiao Liu, Yifan Xu, Shudan Zhang, Yuxiao Dong, Jie Tang

JoPR: Joint Emotion Perception and Reasoning for Conversational Emotion Recognition (2026.acl-long)

Challenge:	Existing LLMs lack high-quality data sources and lack robust data filtration strategies.
Approach:	They develop a framework to enhance the capabilities of LLM-based agents under data scarcity.
Outcome:	The proposed framework improves the capabilities of LLM-based agents under data scarcity.

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Yumeng Fu, Weitao Huang, Junjie Wu, Hao Teng, Meishan Zhang, Bingquan Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for ERC lack human-like emotion reasoning and discrimination between similar emotions.
Approach:	They propose a multi-dimension curriculum with long CoT fine-tuning to clone human-like emotion reasoning for conversational emotion recognition.
Outcome:	The proposed model outperforms existing methods on three widely used datasets and shows that it is more intuitive and more accurate.

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)

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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen

Towards Robust Neural Machine Translation (P18-1)

Challenge:	Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data.
Approach:	They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments.
Outcome:	The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments.

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Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, Yang Liu

Text Generation

Challenge:	Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models.
Approach:	They propose adversarial stability training to make encoder and decoder robust to perturbations by enabling them to behave similarly for the original input and its perturbed counterpart.
Outcome:	The proposed approach improves translation quality and robustness over strong models on Chinese-English, English-German and English-French translation tasks.

Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA (2022.findings-emnlp)

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Junjie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, Nan Duan

Semantic Text Processing

Challenge:	Existing methods to retrieve evidences from corpus are difficult due to table-text discrepancy and data sparsity problem.
Approach:	They propose an optimized OpenQA Table-Text Retriever to retrieve tabular and textual evidences from tabular resources.
Outcome:	The proposed OpenQA Table-Text Retriever significantly outperforms existing methods on QA tasks.

Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)

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Junjie Wu, Jiangnan Li, Yuqing Li, Lemao Liu, Liyan Xu, Jiwei Li, Dit-Yan Yeung, Jie Zhou, Mo Yu

Representation Learning

Challenge:	Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context.
Approach:	They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.
Outcome:	The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset.

Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval (2025.acl-long)

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Zheng Liu, Ze Liu, Zhengyang Liang, Junjie Zhou, Shitao Xiao, Chao Gao, Chen Jason Zhang, Defu Lian

Concise and Organized Perception Facilitates Reasoning in Large Language Models (2025.findings-naacl)

Challenge:	Existing multimodal retrieval models are lacking in visual representations of multimodal data.
Approach:	They propose a visualized information retrieval paradigm where multimodal information is represented by a unified visual format called Screenshots for various retrieval applications.
Outcome:	The proposed model is based on a large dataset of screenshots from diverse sources . it is compared with existing models and lays a solid foundation for the new model .

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Junjie Liu, Shaotian Yan, Chen Shen, Zhengdong Xiao, Liang Xie, Wenxiao Wang, Jieping Ye

Modeling Semantic Compositionality with Sememe Knowledge (P19-1)

Challenge:	Extensive experimental results on several popular logical benchmarks (ProofWriter, PrOntoQA, PrONtoQA-OOD, and FOLIO) and mathematical benchmark (DI-GSM) show that COP significantly outperforms previous state-of-the-art methods.
Approach:	They propose a reasoning approach called Concise and Organized Perception (COP) that carefully analyzes the given statements to identify the most pertinent information while eliminating redundancy efficiently.
Outcome:	The proposed approach outperforms state-of-the-art methods on several popular logical benchmarks and mathematical benchmarks.

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Fanchao Qi, Junjie Huang, Chenghao Yang, Zhiyuan Liu, Xiao Chen, Qun Liu, Maosong Sun

OntoGuard: Enforcing Action Admissibility for LLM Agents in Complex Interactive Environments (2026.findings-acl)

Challenge:	Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents.
Approach:	They propose to incorporate sememes into SC models and employ them in learning multiword expressions.
Outcome:	The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge.

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Pengxiang Liu, Tao Ren, Wei Xiong, Tingrui Yang, Junjie Wang, Jun HU

Challenge:	Existing approaches to large language models (LLMs) are limited by their ability to enforce environmental and behavioral admissibility.
Approach:	They propose an ontological framework to guard LLM agents by enforcing environmental and behavioral admissibility.
Outcome:	Experiments on ScienceWorld and VirtualHome show that OntoGuard can enforce environmental and behavioral admissibility while preventing invalid actions.

Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)

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Junjie Xiong, Changjia Zhu, Shuhang Lin, Chong Zhang, Yongfeng Zhang, Yao Liu, Lingyao Li