Papers by Yue Ma

Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)

Challenge:	Existing approaches to detect mental manipulations are limited due to complexity of detecting subtle, covert tactics in conversations.
Approach:	They propose an approach to detect mental manipulations using large language models using intent-aware prompting by capturing the intents of participants.
Outcome:	The proposed approach significantly reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases.

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Sashuai Zhou, Qiang Zhou, Jijin Hu, Hanqing Yang, Yue Cao, Junpeng Ma, Yinchao Ma, Jun Song, Tiezheng Ge, Cheng Yu, Bo Zheng, Zhou Zhao

POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)

Challenge:	generative models struggle with logic-intensive instruction following, exposing a persistent reasoning–execution gap.
Approach:	They propose a task-agnostic reasoning architecture for general image generation . they propose pixel-level feedback to ground the Thinker's policy in pixel feedback .
Outcome:	The proposed system significantly improves image reasoning and generation quality.

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Yichen Xu, Liangyu Chen, Liang Zhang, Zihao Yue, Jianzhe Ma, Wenxuan Wang, Qin Jin

Multilinguality

Challenge:	Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences.
Approach:	They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse.
Outcome:	The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages.

ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)

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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase.
Approach:	They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift.
Outcome:	The proposed model outperforms baselines while reducing token consumption.

Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (2026.acl-long)

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Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang

Challenge:	Current approaches to writing effective rebuttals are limited by the direct-to-text generation problem . authors must accurately decipher reviewer intent while ensuring every response is firmly anchored in verifiable manuscript details.
Approach:	They propose a framework that reframes rebuttal generation as an evidence-centric planning task.
Outcome:	The proposed framework outperforms baselines in coverage, faithfulness, and strategic coherence.

VRoPE: Rotary Position Embedding for Video Large Language Models (2025.emnlp-main)

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Zikang Liu, Longteng Guo, Yepeng Tang, Tongtian Yue, Junxian Cai, Kai Ma, Qingbin Liu, Xi Chen, Jing Liu

Semantic Text Processing

Challenge:	Existing versions of Large Language Models (LLMs) lack a positional encoding strategy for video.
Approach:	They propose a new positional encoding method tailored for Video-LLMs that mitigates positional biases and ensures a more uniform distribution of spatial focus.
Outcome:	The proposed method outperforms existing versions of RoPE in video understanding and reasoning tasks.

Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)

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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi

From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)

Challenge:	Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions.
Approach:	They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them.
Outcome:	The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios.

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Yongqiang Ma, Lizhi Qing, Jiawei Liu, Yangyang Kang, Yue Zhang, Wei Lu, Xiaozhong Liu, Qikai Cheng

Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs (2025.emnlp-main)

Challenge:	Existing evaluation metrics for large language models yield numerical scores that ignore user experience.
Approach:	They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts .
Outcome:	The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score.

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Yue Zhang, Tianyi Ma, Zun Wang, Yanyuan Qiao, Parisa Kordjamshidi

Efficient Learned Data Compression via Dual-Stream Feature Decoupling (2026.acl-long)

Challenge:	Existing zero-shot LLM-based Vision-and-Language Navigation agents either encode images as textual scene descriptions, potentially oversimplifying visual details, or process raw image inputs, which can fail to capture abstract semantics required for high-level reasoning.
Approach:	They propose to integrate large language models into embodied AI models by incorporating textual descriptions that facilitate analogical reasoning across images from multiple perspectives.
Outcome:	The proposed approach improves the agent’s contextual understanding on the R2R dataset, showing that it can make better decisions based on the LLMs.

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Huidong Ma, Xinyan Shi, Sun Hui, Xiaofei Yue, Xiaoguang Liu, Gang Wang, Wentong Cai

Green & Sustainable NLP

Challenge:	Learned data compression has achieved superior compression ratios, but balancing precise probability modeling with system efficiency remains challenging.
Approach:	They propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams.
Outcome:	The proposed method achieves state-of-the-art performance in both compression ratio and throughput while maintaining the lowest latency and memory usage.

SciVQR: A Multidisciplinary Multimodal Benchmark for Advanced Scientific Reasoning Evaluation (2026.findings-acl)

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Longteng Guo, Xuanxu Lin, Dongze Hao, Tongtian Yue, Pengkang Huo, Jiatong Ma, Yuchen Liu, Jing Liu

LongVideoAgent: Multi-Agent Reasoning with Long Videos (2026.acl-long)

Challenge:	Existing benchmarks for multimodal large language models fail to capture complexity and traceability of reasoning processes . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning.
Approach:	They propose a multimodal benchmark for scientific reasoning covering 54 subfields . SciVQR includes domain-specific visuals and challenges models to combine visual comprehension with reasoning .
Outcome:	SciVQR evaluates 54 subfields in mathematics, physics, chemistry, geography, astronomy, and biology . the results highlight the need for improved multi-step reasoning and integration of interdisciplinary knowledge .

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Runtao Liu, Ziyi Liu, Jiaqi Tang, Yue Ma, Renjie Pi, Jipeng Zhang, Qifeng Chen

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	a key emerging challenge is robust long video understanding, authors say . current methods compress content into lossy summaries or rely on limited toolsets .
Approach:	They propose a multi-agent framework where a master LLM coordinates a grounding agent and a vision agent to extract targeted textual observations.
Outcome:	The proposed model outperforms strong non-agent baselines on episode-level datasets . the proposed model significantly outperformed existing models on other datasets.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

Semantic Text Processing

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)

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Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings.
Approach:	They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints.
Outcome:	The proposed pipeline can be extended to other modules and is not limited to structured output but can be used in industrial applications.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

Diagnosing Hidden Instabilities in Model Editing via Uncertainty Quantification (2026.acl-long)

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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Zihan Gu, TianYi Zhang, Xinyan Zhang, Zhiyuan Wang, Han Zhang, Yuhao Wei, Jiacheng Lu, Tianyi Ma, Xingsheng Zhang, Hua Zhang, Yue Hu

Challenge:	Existing methods to update large language models (LLMs) without expensive retraining are fragile under single-edit evaluation protocols.
Approach:	They propose a framework that characterizes activation-based editing as a constrained intervention on intermediate representations.
Outcome:	The proposed method reveals local knowledge conflicts invisible to existing benchmarks.

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

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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

DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)

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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Haiyang Shen, Hang Yan, Zhongshi Xing, Mugeng Liu, Yue Li, Zhiyang Chen, Yuxiang Wang, Jiuzheng Wang, Yun Ma

Robustness in NLP

Challenge:	Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries.
Approach:	They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance.
Outcome:	The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness.

Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)

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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi

Reasoning

Challenge:	Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable.
Approach:	This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning .
Outcome:	The proposed study examines the safety and security risks of large reasoning models.

DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)

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Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu

FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding.
Approach:	They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects.
Outcome:	The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation.

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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang

Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory (2026.acl-long)

Challenge:	FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Approach:	They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Outcome:	The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download.

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Shiqi He, Yue Cui, Xinyu Ma, Yaliang Li, Bolin Ding, Mosharaf Chowdhury

Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference (2023.acl-long)

Challenge:	Existing methods for embodied reasoning are coarse-grained and expensive . branch-and-browse framework enables fine-grounded, memory-guided, and efficient multi-branch reasoning.
Approach:	They propose a framework that unifies structured reasoning-acting, contextual memory, and efficient execution.
Outcome:	The proposed framework achieves task success rate of 35.8% and reduces execution time by up to 40.4% relative to state-of-the-art methods.

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Junhao Zheng, Qianli Ma, Shengjie Qiu, Yue Wu, Peitian Ma, Junlong Liu, Huawen Feng, Xichen Shang, Haibin Chen

Semantic Text Processing

Challenge:	Existing studies attribute catastrophic forgetting to fine-tuning, and they retain pre-trained knowledge indiscriminately without identifying what knowledge is transferable.
Approach:	They propose a unified objective for fine-tuning to retrieve the causality back from pre-trained data and use it to mitigate negative transfer while preserving knowledge.
Outcome:	The proposed method outperforms state-of-the-art fine-tuning methods on commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models.

Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs (2024.findings-acl)

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Naihao Deng, Zhenjie Sun, Ruiqi He, Aman Sikka, Yulong Chen, Lin Ma, Yue Zhang, Rada Mihalcea

Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents (2026.acl-long)

Challenge:	Recent years have witnessed an explosion of Large Language Models (LLMs), with impressive performance on various NLP tasks.
Approach:	They propose to use image-based representations to compare LLMs' performance on table-related tasks such as question-answering and fact-checking to determine their effectiveness.
Outcome:	The proposed model performs better on image-based representations than on text-based models.

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Tianyi Ma, Yue Zhang, Zehao Wang, Parisa Kordjamshidi

PEAR: Planner-Executor Agent Robustness Benchmark (2026.findings-eacl)

Challenge:	Vision-and-Language Navigation (VLN) is a subfield of embodied AI that integrates natural language understanding, visual perception, and sequential decision-making to allow autonomous agents to navigate and interact within visual environments.
Approach:	They propose a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents.
Outcome:	The proposed framework decomposes navigation into atomic skills handled by a specialized agent.

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Shen Dong, Mingxuan Zhang, Pengfei He, Li Ma, Bhavani Thuraisingham, Hui Liu, Yue Xing

Robustness in NLP

Challenge:	Existing studies examine isolated attack surfaces or specific scenarios, leaving a lack of holistic understanding of MAS vulnerabilities.
Approach:	They propose a benchmark to evaluate the utility and vulnerability of planner–executor MAS.
Outcome:	The proposed benchmark evaluates planner–executor MAS on a widely adopted design.

A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)

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Bowen Ding, Qingkai Min, Shengkun Ma, Yingjie Li, Linyi Yang, Yue Zhang

RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)

Challenge:	Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text.
Approach:	They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation.
Outcome:	The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios.

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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu

KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)

Challenge:	High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized.
Approach:	They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy.
Outcome:	The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped.

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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu

Improving Encoder by Auxiliary Supervision Tasks for Table-to-Text Generation (2021.acl-long)

Challenge:	Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos.
Approach:	They propose a framework that decouples epistemic planning from content generation.
Outcome:	The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage.

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Liang Li, Can Ma, Yinliang Yue, Dayong Hu

Text Generation

Challenge:	Experimental results show that our method not only has a good generalization but also outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.
Approach:	They propose to build an entity graph from the input tables and introduce a reasoning module to perform reasoning on the graph.
Outcome:	The proposed method outperforms previous methods on several metrics: BLEU, Content Selection, Content Ordering.

One2Set + Large Language Model: Best Partners for Keyphrase Generation (2024.emnlp-main)

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Liangying Shao, Liang Zhang, Minlong Peng, Guoqi Ma, Hao Yue, Mingming Sun, Jinsong Su

Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation (2025.acl-long)

Challenge:	Existing selection methods make redundant selections, causing poor recall and accuracy.
Approach:	They propose a framework to generate keyphrases from a one2set-based model and an LLM as selector.
Outcome:	The proposed framework surpasses state-of-the-art models in absent keyphrase prediction.

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Shijie Wang, Wenqi Fan, Yue Feng, Lin Shanru, Xinyu Ma, Shuaiqiang Wang, Dawei Yin