Papers by Yufan Wu
Towards Self-Evolving Agents: Enabling Autonomy through Interactive Experience Refinement (2026.findings-acl)
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Cheng Yang, Xuemeng Yang, Licheng Wen, Daocheng Fu, Jianbiao Mei, Rong Wu, Pinlong Cai, Yufan Shen, Nianchen Deng, Jia Xu, Botian Shi, Yu Qiao, Haifeng Li
| Challenge: | Large Language Models struggle with complex, multi-step operational tasks because they remain static during inference and cannot learn from past experience. |
| Approach: | They propose a framework that organizes cross-domain insights to facilitate orchestration of long-horizon workflows. |
| Outcome: | The proposed framework outperforms existing methods on the TAC productivity benchmark and shows strong cross-task transferability. |
Self-Taught Agentic Long Context Understanding (2025.acl-long)
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Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)
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| Challenge: | Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. |
| Approach: | They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts. |
| Outcome: | The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines. |
Recurrent Attention for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent research questions the importance of dot-product self-attention in Transformer models and shows that most attention heads learn simple positional patterns. |
| Approach: | They propose a novel mechanism to replace dot-product self-attention with a recurrent atteNtion mechanism that directly learns attention weights without token-to-token interaction. |
| Outcome: | The proposed model outperforms the Transformer model on translation tasks with fewer parameters and inference time. |
GenPT: Beyond Self-Report for Reliable LLM Psychometrics via Generative Projective Testing (2026.acl-long)
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Ming Wang, Shuang Wu, Bixuan Wang, Lu Lin, Yuxin Chen, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang, Yufan Sun
| Challenge: | Large language models (LLMs) inherit contamination from training corpora, directional bias under social-desirability framing, and limited responsiveness to context beyond the item text. |
| Approach: | They propose a paradigm that reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
| Outcome: | The proposed paradigm reformulates TAT, Rorschach, and SCT with newly generated stimuli and organises assessment as a three-stage pipeline. |
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)
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Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai
| Challenge: | RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images . |
| Approach: | They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a . |
| Outcome: | The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images. |
Task-guided Disentangled Tuning for Pretrained Language Models (2022.findings-acl)
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| Challenge: | Pretrained language models are fine-tuned on task-specific datasets, but fail to capture task- specific patterns. |
| Approach: | They propose a method which disentangles task-relevant signals from entangled representations. |
| Outcome: | The proposed method improves generalization of representations by disentangling task-relevant signals from the entangled representations. |
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process . |
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)
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| Challenge: | Existing unsupervised summarization methods fail to consider efficiency and effectiveness when the input document is extremely long. |
| Approach: | They propose an efficient Coarse-to-Fine Facet-Aware Ranking framework for unsupervised long document summarization based on the semantic block. |
| Outcome: | The proposed framework can achieve new state-of-the-art unsupervised summarization results on Gov-Report, billSum, arXiv, and PubMed. |
Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models (2023.findings-emnlp)
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| Challenge: | Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. |
| Approach: | They propose a higher-order theory of mind benchmark and introduce a new deception mechanism to evaluate ToM reasoning. |
| Outcome: | The proposed benchmarks show that the LLMs are not performing well on higher-order tasks. |