Papers by Dong Fang
Verified Critical Step Optimization for LLM Agents (2026.findings-acl)
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| Challenge: | Critical Step Optimization (CSO) focuses preference learning on verified critical steps where alternative actions demonstrably flip task outcomes from failure to success. |
| Approach: | They propose a method which focuses preference learning on verified critical steps where alternative actions demonstrably flip task outcomes from failure to success. |
| Outcome: | The proposed method outperforms the existing methods on GAIA-Text-103 and XBench-DeepSearch while requiring supervision at only 16% of trajectory steps. |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation (2023.emnlp-main)
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| Challenge: | Existing models require associated image with input sentence, which is difficult to satisfy at inference. |
| Approach: | They propose to use synthetic and authentic images to generate translations using text-to-image generation models. |
| Outcome: | The proposed model achieves state-of-the-art performance on En-De and En-Fr datasets while remaining independent of authentic images during inference. |
IntentCoding: Amplifying User Intent in Code Generation (2026.findings-acl)
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| Challenge: | IntentCoding captures the influence of user intent by masking out the intent, and integrates seamlessly with existing decoding procedures. |
| Approach: | They propose a decoding strategy that captures the influence of user intent by masking out the intent and applies a multi-strength ensemble mechanism to amplify the effect of user intention during generation. |
| Outcome: | The proposed model significantly improves both constraint satisfaction and functional correctness compared to greedy decoding approaches. |
Recall with Reasoning: Chain-of-Thought Distillation for Mamba’s Long-Context Memory and Extrapolation (2025.emnlp-main)
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| Challenge: | Existing long-context memory methods such as Mamba struggle with long-constituency when the length of the processed text exceeds the model's training length. |
| Approach: | They propose a method that uses chain-of-thought summarization to teach Mamba to actively recall and reason over long contexts. |
| Outcome: | Experiments on LONGMEMEVAL and HELMET show that RwR outperforms existing long-term memory methods while preserving short-context capabilities. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment (2026.findings-acl)
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Shipeng Li, Zhiqin Yang, Shikun Li, Xiaobo Xia, Hengyu Liu, Xinghua Zhang, Gaode Chen, Dong Fang, Ying Tai, Zhe Peng
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) is a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck. |
| Approach: | They propose a gradient-alignment-based method which intelligently selects the learnable and representative training reasoning data for RLVR post-training. |
| Outcome: | Experiments on five reasoning benchmarks show that the proposed method significantly reduces training data requirements while improving performance. |
WebRollback: Enhancing Web Agents with Explicit Rollback Mechanisms (2026.eacl-short)
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| Challenge: | Recent studies have adopted a greedy one-way search strategy to deal with dynamic web environments. |
| Approach: | They propose to integrate a rollback mechanism into web agents to allow them to revert back to a previous state in navigation trajectory. |
| Outcome: | The proposed method is able to revert back to a previous state in its navigation trajectory, allowing the models to directly control the search process. |
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization (2025.acl-long)
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Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu
| Challenge: | Existing studies focus on building text-only agents in synthetic environments where the reward signals are clearly defined. |
| Approach: | They propose a multimodal web agent that can autonomously conduct real-world exploration and improve itself after each iteration. |
| Outcome: | The proposed agent improves itself after each iteration, demonstrating strong performance across multiple test sets. |
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)
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Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong
| Challenge: | Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research . |
| Approach: | They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions . |
| Outcome: | The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench. |
AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity (2025.emnlp-main)
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| Challenge: | Existing methods for large language models with extended context lengths face significant computational challenges during the prefill phase. |
| Approach: | They propose a difference-aware, dynamic sparse attention mechanism that efficiently identifies critical attention regions at a finer stripe granularity while adapting to global contextual information. |
| Outcome: | The proposed model achieves a speedup of 1.44 while maintaining higher recall rates. |
Assessing Combinational Generalization of Language Models in Biased Scenarios (2022.aacl-short)
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| Challenge: | Existing work focuses on assessing in-domain knowledge, but shedding light on what pre-trained Language Models learn is important. |
| Approach: | They propose a method to assess a PLM's generalization capacity in biased scenarios by combining component combinations where it could be easy for the PLMs to learn shortcuts from the training corpus. |
| Outcome: | The proposed model can overcome distribution shifts in the training corpus and with sufficient data. |
KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)
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| Challenge: | Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. |
| Approach: | They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism. |
| Outcome: | The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom. |
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization via Multi-LLMs (2025.findings-naacl)
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| Challenge: | Existing jailbreak research exhibits limitations in universality, validity, and efficiency . Existing methods for jailbreaking LLMs have limited validity and effectiveness . |
| Approach: | They propose a black-box approach that uses wordplay-guided mapping rule sampling to create universal adversarial prompts. |
| Outcome: | The proposed method efficiently identifies security vulnerabilities across various LLMs, achieving an average success rate of over 80% with fewer than 10 queries. |
MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)
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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data. |
| Approach: | They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process. |
| Outcome: | Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities. |
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
| 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. |
WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback (2025.findings-emnlp)
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Minda Hu, Tianqing Fang, Jianshu Zhang, Jun-Yu Ma, Zhisong Zhang, Jingyan Zhou, Hongming Zhang, Haitao Mi, Dong Yu, Irwin King
| Challenge: | Web agents powered by Large Language Models lack the ability to perform in uncertain web environments. |
| Approach: | They propose to reconstruct web agents' reasoning skills into chain-of-thought rationales by fine-tuning their LLM backbone into a web-based model. |
| Outcome: | The proposed approach significantly improves the agent self-improving benchmark OpenWebVoyager, demonstrating that it can be used to improve the agent's reasoning skills. |
WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model (2025.emnlp-main)
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| Challenge: | Agent self-improvement, where agents train their underlying Large Language Model (LLM) on self-sampled trajectories, shows promising results but often stagnates in web environments due to limited exploration and under-utilization of pretrained web knowledge. |
| Approach: | They propose a co-evolving Large Language Model (LLM) that predicts the next observation based on current observation and action within the web environment. |
| Outcome: | The proposed framework shows that agents can perform better in real-world web environments without using any distillation from more powerful close-sourced models. |
Enhancing Multimodal Unified Representations for Cross Modal Generalization (2025.findings-acl)
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Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Minghui Fang, Jieming Zhu, Zhenhua Dong, Sashuai Zhou, Zhou Zhao
| Challenge: | Existing studies on discrete unified representations overlook important distinctions between different dimensions of features. |
| Approach: | They propose to use a codebook to optimize unified representations from pretraining and fine- and coarse-grained disentangling to optimize the representations. |
| Outcome: | The proposed methods improve the interpretability of multimodal unified representations . they use training-free optimization of codebook and fine and coarse cross-modal disentangling . |
Implicit Deep Latent Variable Models for Text Generation (D19-1)
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| Challenge: | Variational auto-encoders have been used for text generation but their representation power is limited due to two reasons. |
| Approach: | They advocate sample-based representations of variational distributions for natural language . they further develop an LVM to directly match the aggregated posterior to the prior . |
| Outcome: | The proposed model can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)
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Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu
| Challenge: | Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling. |
| Approach: | They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention. |
| Outcome: | The proposed methods lower irregular attention entropy and narrow performance gaps. |
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability (2025.emnlp-main)
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| Challenge: | Existing methods to extend context length of Large Language Models (LLMs) still struggle with retrieval and reasoning in long context inputs. |
| Approach: | They propose a coarse-to-fine method to enhance multi-document question-answering capacities by removing background and distracting documents. |
| Outcome: | Experiments show that CAFE outperforms baseline methods on multiple documents. |
Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)
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| Challenge: | Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts. |
| Approach: | They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot. |
| Outcome: | The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks. |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)
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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng
| Challenge: | Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling. |
| Approach: | They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries. |
| Outcome: | The proposed method is significantly more robust to noisy training data than existing methods. |
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification (2026.findings-acl)
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Yuxuan Wan, Tianqing Fang, Zaitang LI, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu
| Challenge: | Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. |
| Approach: | They propose an inference-time scaling of verification wherein an agent self-improves at test time by evaluating its generated answers. |
| Outcome: | The proposed model outperforms vanilla agent-as-judge and LLM judge baselines by 12%–48% in meta-evaluation F1 score. |
Assessing the Reliability of Word Embedding Gender Bias Measures (2021.emnlp-main)
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| Challenge: | Various measures have been proposed to quantify human-like social biases in word embeddings, but they can suffer from measurement error. |
| Approach: | They propose to assess the reliability of word embedding gender bias measures by examining their reliability across different choices of random seeds, scoring rules and words. |
| Outcome: | The proposed measures can suffer from measurement error, and the results inform better design of word embedding gender bias measures. |
Continual Named Entity Recognition without Catastrophic Forgetting (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (CNER) is a burgeoning area of research . a new paradigm has ushered NER into a non-entity type at the current step t . |
| Approach: | They propose a pooled feature distillation loss that skillfully navigates the trade-off between retaining knowledge of old entity types and acquiring new ones. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on ten CNER settings using three datasets. |