Papers by Jian Jiang

Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian

MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)

Challenge:	LLM-based agents for machine learning engineering rely on tree search to rank candidates.
Approach:	They propose an LLM-based agent that operationalizes gradient-based optimization.
Outcome:	The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU.

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Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu

Representation Learning

Challenge:	Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest.
Approach:	They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest.
Outcome:	The proposed approach significantly outperforms existing state-of-the-art methods on the MIND news recommendation benchmark.

VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)

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Zhanpeng Chen, Chengjin Xu, Yiyan Qi, Xuhui Jiang, Jian Guo

Context as a Tool: Context Management for Long-Horizon SWE-Agents (2026.findings-acl)

Challenge:	a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness.
Approach:	They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability.
Outcome:	The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images.

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Shukai Liu, Bo Jiang, Jian Yang, Yizhi LI, Jinyang Guo, Xianglong Liu, Bryan Dai

Reasoning

Challenge:	Existing large language models rely on append-only context maintenance or passively triggered compression heuristics, leading to context explosion, semantic drift, and degraded reasoning in long-running interactions.
Approach:	They propose a new context management paradigm that elevates context maintenance to a callable tool . they propose 'cat' framework that injects context-management actions into complete interaction trajectories .
Outcome:	The proposed model outperforms ReAct-based agents and static compression baselines on SWE-Verified tests.

Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)

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Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Daxin Jiang

MTRec: Multi-Task Learning over BERT for News Recommendation (2022.findings-acl)

Challenge:	Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks.
Approach:	They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages.
Outcome:	The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data.

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Qiwei Bi, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Hanfang Yang

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference (2026.acl-long)

Challenge:	Existing news recommendation methods learn news representations solely based on news titles. Existing methods only utilize title information and neglect other valuable news information such as categories and entities.
Approach:	They propose a multi-task method to incorporate multi-field information into BERT, which improves its news encoding capability.
Outcome:	Extensive experiments on the MIND news recommendation benchmark show the proposed method is effective.

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Zhiyuan Shi, Qibo Qiu, null Xuefeng, Zhonglin Jiang, Li Yu, Jian Jiang, Xiaofei He, Wenxiao Wang

Information Retrieval

Challenge:	Existing static compression methods suffer from coarse-grained caching and high I/O overhead.
Approach:	They propose a training-free dynamic compression framework that uses a sparse attention mechanism to categorize attention heads based on stability and similarity.
Outcome:	The proposed framework achieves state-of-the-art performance on long-context benchmarks and accelerates decoding by up to 3 compared to the original model with a 224K context.

Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs (2026.findings-eacl)

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Honghao Liu, Xuhui Jiang, Chengjin Xu, Cehao Yang, Yiran Cheng, Lionel Ni, Jian Guo

HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)

Challenge:	Existing methods to protect PII from training on small corpora are difficult to implement in real-world applications.
Approach:	They propose an entity-based framework that synthesizes encrypted training data to protect PII.
Outcome:	The proposed framework outperforms base models and ensures PII security on limited-scale datasets while exhibiting a modest performance gap compared to models trained on unencrypted synthetic data.

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Songtao Jiang, Yan Zhang, Yeying Jin, Zhihang Tang, Yangyang Wu, Yang Feng, Jian Wu, Zuozhu Liu

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings (2022.coling-1)

Challenge:	Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability .
Approach:	They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses .
Outcome:	The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries.

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Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Controlled Text Generation Using Dictionary Prior in Variational Autoencoders (2022.findings-acl)

Challenge:	Existing knowledge graph embedding methods ignore semantic similarity between related entities and entity-relation couples in different triples .
Approach:	They propose a contrastive learning framework for tensor decomposition based (TDB) KGE that can shorten the semantic distance of related entities and entity-relation couples in different triples and thus improve the performance of KGE.
Outcome:	The proposed method achieves 51.2% MRR, 46.8% Hits@1 on three standard KGE datasets, 37.8% MRR and 28.6% Hits @1 on FB15k-237 datasets and 59.1% MRR .

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Xianghong Fang, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Dit-Yan Yeung

LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful Synthetic Data (2025.findings-acl)

Challenge:	Variational autoencoders (VAEs) have been widely applied in text generation tasks, but they suffer from insufficient representation capacity and poor controllability.
Approach:	They propose a data-driven prior that has expressivity and controllability.
Outcome:	The proposed prior enjoys expressivity and controllability and can be used in language modeling and controlled text generation.

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Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Shengjie Ma, Aofan Liu, Hui Xiong, Jian Guo

Reasoning

Challenge:	Long-context processing ability has emerged as a significant challenge for large language models.
Approach:	They propose a pipeline for synthesizing faithful long-context reasoning instruction datasets . they integrate ground truth and citation-based reasoning prompts integrating them .
Outcome:	The proposed pipeline eliminates distractions and improves reasoning chains.

Global Eye: Breaking the “Fixed Thinking Pattern” during the Instruction Expansion Process (2025.acl-long)

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Wenxuan Lu, Wei Liu, Jian Luan, Bin Wang, Songhao Jiang, Tianning Zang

Leveraging Argumentation Knowledge Graph for Interactive Argument Pair Identification (2021.findings-acl)

Challenge:	Existing methods focus on constructing multi-perspective prompts to expand instructions, overlooking the “Fixed Thinking Pattern” issue of Large Language Models.
Approach:	They propose a method that analyzes the statistical characteristics of newly generated instructions and updates the prompts after a fixed number of instruction expansions.
Outcome:	The proposed method surpasses open-source LLMs and GPT3.5 in several metrics.

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Jian Yuan, Zhongyu Wei, Donghua Zhao, Qi Zhang, Changjian Jiang

Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval (2022.findings-emnlp)

Challenge:	Existing researches focus on sentence matching but the interaction of opinions requires reasoning of knowledge, which is beyond textual information.
Approach:	They propose to leverage external knowledge to enhance the identification of interactive argument pairs by analyzing the discussion thread of the target topic in an online forum.
Outcome:	The proposed model achieves state-of-the-art in the benchmark dataset.

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Houxing Ren, Linjun Shou, Jian Pei, Ning Wu, Ming Gong, Daxin Jiang

Reasoning over Entity-Action-Location Graph for Procedural Text Understanding (2021.acl-long)

Challenge:	Recent multilingual pre-trained models perform poorly on multilingual retrieval tasks due to lack of multilingual training data.
Approach:	They propose to mine and generate self-supervised training data based on large-scale unlabeled corpus and introduce query generator to generate more queries in target languages for unlabed passages.
Outcome:	The proposed method performs better than baselines on a Mr. TYDI dataset and an industrial dataset from a commercial search engine.

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Hao Huang, Xiubo Geng, Jian Pei, Guodong Long, Daxin Jiang

Structured Data in NLP

Challenge:	Procedural text understanding aims at tracking the states and locations of entities mentioned in a paragraph.
Approach:	They propose a framework to model entities-entity, action, and location relations using a graph neural network.
Outcome:	The proposed approach outperforms strong baselines on two datasets, ProPara and Recipes.

A Graph Representation of Semi-structured Data for Web Question Answering (2020.coling-main)

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Xingyao Zhang, Linjun Shou, Jian Pei, Ming Gong, Lijie Wen, Daxin Jiang

Structured Data in NLP

Challenge:	Existing studies treat semi-structured data as flat documents with pieces of text . semi-structural data is more effective to represent rich relational information . question answering is an important feature in most search engines .
Approach:	They propose a graph representation of Web tables and lists based on categorization of components and their relations . they also develop reasoning techniques on the graph model for the question answering task .
Outcome:	The proposed graph improves F1 score by 3.90 points over the state-of-the-art baselines on real datasets.

FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios (2026.findings-acl)

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Yutao Hou, Yihan Jiang, Yuhan Xie, Jian Yang, Liwen Zhang, Hailiang Huang, Guanhua Chen, Yun Chen

Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)

Challenge:	Existing large language models (LLMs) are prone to misuse and misinformation, posing serious compliance risks.
Approach:	They propose a bilingual red-teaming benchmark to test an LLM’s refusal of requests that violate financial compliance.
Outcome:	The proposed benchmark is based on real-world financial crime cases and ethical violations and includes 14 subcategories covering financial crimes and ethical breaches.

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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li

Conflicts Make Large Reasoning Models Vulnerable to Attacks (2026.findings-acl)

Challenge:	Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions.
Approach:	They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations.
Outcome:	The proposed framework measures the agent's higher-order social cognition in multi-turn conversations.

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Honghao Liu, Chengjin Xu, Xuhui Jiang, Cehao Yang, Shengming Yin, Zhengwu Ma, Lionel Ni, Jian Guo

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning (2026.findings-acl)

Challenge:	Large Reasoning Models have demonstrated outstanding capabilities in solving complex reasoning tasks by incorporating step-by-step chain-of-thought (CoT) reasoning.
Approach:	They evaluate three large reasoning models that perform explicit and coherent reasoning under conflicting objectives and use them to evaluate their performance.
Outcome:	The proposed models perform explicit and coherent reasoning before producing their outputs, improving problem-solving and multi-step decision making.

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Cehao Yang, Xueyuan Lin, Xiaojun Wu, Chengjin Xu, Xuhui Jiang, Honghao Liu, Hui Xiong, Jian Guo

Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding (2022.emnlp-main)

Challenge:	Large reasoning models exhibit human-like behaviors such as exploration, verification, reflection, and correction.
Approach:	They propose a supervised fine-tuning framework for long chain-of-thoughts reasoning . they leverage a difficulty-aware reward model to estimate the learning value of questions .
Outcome:	The proposed framework performs fine-tuning on large reasoning models on 10% of the data selected.

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Shining Liang, Linjun Shou, Jian Pei, Ming Gong, Wanli Zuo, Xianglin Zuo, Daxin Jiang

PromptBERT: Improving BERT Sentence Embeddings with Prompts (2022.emnlp-main)

Challenge:	Existing approaches to translate spoken language understanding into low-resource languages are limited to implicit alignment and disregard the inherent semantic structure in SLU.
Approach:	They propose to model utterance-slot-word structure by a multi-level contrastive learning framework . they also propose a label-aware joint model leveraging label semantics to enhance alignment .
Outcome:	The proposed model improves performance on two zero-shot cross-lingual datasets.

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Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang

GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)

Challenge:	Existing research shows that BERT and RoBERTa are poor in sentence embeddings due to static token embeddable bias and ineffective BERT layers.
Approach:	They propose a novel contrastive learning method for better sentence embeddings by using a template denoising technique.
Outcome:	The proposed method achieves 2.29 and 2.58 points of improvement compared to SimCSE and RoBERTa in the unsupervised setting.

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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan

Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)

Challenge:	Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models.
Approach:	They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.
Outcome:	The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages.

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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu

Robustness in NLP

Challenge:	Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes.
Approach:	They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence.
Outcome:	The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show.

Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)

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Muzhi Li, Cehao Yang, Chengjin Xu, Xuhui Jiang, Yiyan Qi, Jian Guo, Ho-fung Leung, Irwin King

Alleviating Over-smoothing for Unsupervised Sentence Representation (2023.acl-long)

Challenge:	Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities.
Approach:	They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple.
Outcome:	The proposed framework improves on FB15k237 and WN18RR datasets.

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Nuo Chen, Linjun Shou, Jian Pei, Ming Gong, Bowen Cao, Jianhui Chang, Jia Li, Daxin Jiang

Modeling Event-Pair Relations in External Knowledge Graphs for Script Reasoning (2021.findings-acl)

Challenge:	Existing approaches to learn better unsupervised sentence representations have been successful . over-smoothing problem in unsupervised sentences reduces the capacity of powerful PLMs .
Approach:	They propose a method to solve the over-smoothing problem in unsupervised sentence representations by combining negatives from PLMs intermediate layers.
Outcome:	The proposed method improves on different strong baselines on Semantic Textual Similarity and Transfer datasets.

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Yucheng Zhou, Xiubo Geng, Tao Shen, Jian Pei, Wenqiang Zhang, Daxin Jiang

Knowledge Representation

Challenge:	Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events .
Approach:	They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate.
Outcome:	The proposed model is compared with baselines using external KG or not on a script reasoning task.

Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation (2020.coling-main)

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Junhao Liu, Linjun Shou, Jian Pei, Ming Gong, Min Yang, Daxin Jiang

Multilinguality

Challenge:	Cross-lingual Machine Reading Comprehension (CLMRC) is a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese.
Approach:	They propose a novel approach to augment cross-lingual machine reading comprehension by combining knowledge from multiple language branch models into a single model for all target languages.
Outcome:	Extensive experiments on two CLMRC benchmarks show the proposed method is effective and robust to data noises.

Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang

BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2022.naacl-main)

Challenge:	Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications.
Approach:	They propose a framework that incorporates large language models to improve EA.
Outcome:	The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency.

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Yuchen Jiang, Tianyu Liu, Shuming Ma, Dongdong Zhang, Jian Yang, Haoyang Huang, Rico Sennrich, Ryan Cotterell, Mrinmaya Sachan, Ming Zhou

Multilinguality

Challenge:	Standard evaluation metrics, e.g., BLEU, TER and METEOR, focus on the quality of translations at the sentence level and do not consider discourse-level features.
Approach:	They propose to use a metric to take discourse coherence into consideration by categorizing discourse-related spans and calculating the similarity-based F1 measure of categorized spans.
Outcome:	The proposed metric possesses better selectivity and interpretability at the document-level, and is more sensitive to document- level nuances.

Learning Visually-Grounded Semantics from Contrastive Adversarial Samples (C18-1)

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Haoyue Shi, Jiayuan Mao, Tete Xiao, Yuning Jiang, Jian Sun

Robustness in NLP

Challenge:	Existing frameworks for grounding distributional representations of texts on the visual domain are limited . effective and efficient grounding of distributional embeddings remains challenging .
Approach:	They propose to ground distributional representations of texts on the visual domain using visual-semantic embeddings.
Outcome:	The proposed model improves on a diverse set of downstream tasks and defends known-type adversarial attacks.

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Nuo Chen, Linjun Shou, Tengtao Song, Ming Gong, Jian Pei, Jianhui Chang, Daxin Jiang, Jia Li

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

Challenge:	Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks.
Approach:	They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations.
Outcome:	The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings.

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding (2021.emnlp-main)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Yingmei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

Challenge:	Existing approaches to scale out spoken language understanding to low-resource languages are noisy.
Approach:	They propose a method for mitigating noise in augmented data by training models with augmented datasets.
Outcome:	The proposed method outperforms state-of-the-art methods on two benchmark datasets.

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026.findings-acl)

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Zhichao Shi, Xuhui Jiang, Chengjin Xu, Cangli Yao, Shengjie Ma, Yinghan Shen, Zixuan Li, Jian Guo, Yuanzhuo Wang

Challenge:	Current evaluation methods for large language models rely on static benchmarks . limited knowledge coverage and fixed difficulties hinder the targeted optimizations resulting in superficial evaluations of LLMs - a problem that has been addressed by JudgeAgent .
Approach:	They propose a knowledge-driven and dynamic evaluation framework for large language models . judgeAgent leverages LLM agents equipped with context graphs to traverse knowledge structures .
Outcome:	The proposed framework can achieve comprehensive evaluations and facilitate effective model iterations.

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

MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)

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.

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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Huang Zihe, Jian Guo, Yuanzhuo Wang

Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)

Challenge:	Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings.
Approach:	They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment.
Outcome:	The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment.

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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang

Dialogue Systems & Conversational Agents

Challenge:	Recent advances in neural models have shown promising progress on this task, but key challenges remain .
Approach:	They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template .
Outcome:	The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial.

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)

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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo

OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)

Challenge:	Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities.
Approach:	They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training.
Outcome:	The proposed framework achieves an optimal balance between generation efficiency and data quality.

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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu