Papers by Yue Cao
ChuLo: Chunk-Level Key Information Representation for Long Document Understanding (2025.findings-acl)
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| Challenge: | Traditional approaches to truncate inputs, sparse self-attention, and chunking often lead to information loss and hinder the model’s ability to capture long-range dependencies. |
| Approach: | They propose a novel chunk representation method that uses unsupervised keyphrase extraction to group input tokens to retain core document content while reducing input length. |
| Outcome: | The proposed method minimizes information loss and improves the efficiency of Transformer-based models. |
Unified Thinker: A General Reasoning Core for Image Generation (2026.acl-long)
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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
| 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. |
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot (2021.emnlp-main)
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| Challenge: | Existing methods for paraphrase generation rely on language as the pivot . however, there is no evidence that parallel data of paraphrases is needed for paraphrasing. |
| Approach: | They propose to use semantic and syntactic representations as pivot for paraphrase generation. |
| Outcome: | The proposed method can generate paraphrases with better quality than using language as pivot. |
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs (2026.findings-acl)
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| Challenge: | Multi-agent LLMs are rapidly moving from prototype to real-world use . network topology is a first-order security parameter in multi-aggent systems . |
| Approach: | They propose a framework for comparing topology-conditioned memory leakage in multi-agent LLM systems. |
| Outcome: | The proposed framework evaluates topology-conditioned memory leakage in multi-agent LLM systems. |
InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning (2026.acl-long)
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Qihang Ai, Pi Bu, Yue Cao, Yingyao Wang, Jihao Gu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Zhicheng Zheng, Jun Song, Yuning Jiang
| Challenge: | Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. |
| Approach: | They propose a vision-language model that actively seeks human confirmation at critical decision points and a model inspired by reinforcement learning. |
| Outcome: | The proposed model achieves an improvement of 46.8% in inquiry success rate and the best overall success rate among existing baselines on InquireBench. |
Factual Error Correction for Abstractive Summarization Models (2020.emnlp-main)
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| Challenge: | Existing methods for abstractive summarization are unable to ensure factual consistency of generated summaries. |
| Approach: | They propose a post-editing corrector module to identify and correct factual errors in generated summaries. |
| Outcome: | The proposed model outperforms existing models on CNN/DailyMail dataset on factual consistency evaluation. |
Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations (2024.findings-emnlp)
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| Challenge: | State-of-the-art language models (LMs) sometimes generate that misalign with world knowledge. |
| Approach: | They propose a method to mitigate hallucinations by restoring the LM's internal fact recall pipeline by a targeted restoration of its internal fact-recall pipeline. |
| Outcome: | The proposed method shows superior performance compared to baselines. |
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization (2020.acl-main)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods and training end-to-end models. |
| Approach: | They propose to jointly learn to align and align to train a neural cross-lingual summarization model by using a large-scale corpus. |
| Outcome: | The proposed model outperforms competing models in most cases and can generate cross-lingual summaries without access to any cross-linguistic corpus. |
Which Sense Dominates Multisensory Semantic Understanding? A Brain Decoding Study (2024.lrec-main)
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| Challenge: | Decoding semantic meanings from brain activity is open to multisensory stimulation, as word meanings can be delivered by both auditory and visual inputs. |
| Approach: | They aim to develop a computational model to probing what information from the act of language understanding is represented in human brain. |
| Outcome: | The proposed model dissociates multisensory integration of word understanding into written text, spoken text and image perception respectively, exploring the decoding efficiency and reliability of unisensory information in the brain representation. |
RelationalCoder: Rethinking Complex Tables via Programmatic Relational Transformation (2025.acl-long)
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| Challenge: | Semi-structured tables remain a major obstacle for automated data processing and analytics. |
| Approach: | They propose a technique called Loop Reference Decoding which identifies expandable groups and replicates each group using a concise loop over its repetitive region. |
| Outcome: | The proposed technique reduces output length from O(N M) to approximately O(K) Extensive experiments on HiTab and MultiHiertt show that it boosts Llama-2 and Mistral models by more than 20%, and GPT-4o by over 4%. |
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
CLUE: A Chinese Language Understanding Evaluation Benchmark (2020.coling-main)
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Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
| Challenge: | Existing language evaluation benchmarks for English are limited to English . lack of such benchmarks makes it difficult to replicate success in other languages . |
| Approach: | They introduce a large-scale Chinese language understanding evaluation benchmark . the benchmark uses a set of current state-of-the-art pre-trained Chinese models . |
| Outcome: | The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is released . the benchmark evaluates models across a wide range of tasks on original Chinese text . existing language evaluation benchmarks are mostly limited to English . |
Continual Learning for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models are data-driven and require large-scale training corpus . continual learning remains a big challenge for artificial intelligence systems and models . |
| Approach: | They propose a continual learning framework for NMT models that incorporates multiple stages of training to alleviate catastrophic forgetting problem. |
| Outcome: | The proposed framework achieves superior performance compared to baseline models in all settings. |
TableCoder: Table Extraction from Text via Reliable Code Generation (2025.acl-industry)
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| Challenge: | Structured table extraction from unstructured text is critical for automating data processing tasks across industries where accuracy and reliability are paramount. |
| Approach: | They propose a natural language-based method for extracting structured tables from text . they use Python classes or SQL statements to explicitly construct table structures . |
| Outcome: | The proposed method improves F1 scores and mitigates hallucinations . it integrates with standard SQL databases and Python workflows, ensuring seamless deployment . |
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network (2020.findings-emnlp)
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| Challenge: | Paraphrases refer to texts that convey the same meaning with different expression forms. |
| Approach: | They propose to incorporate a diversity loss term into a deep generative model to generate diverse paraphrases. |
| Outcome: | The proposed model can generate more diverse paraphrases compared with baselines. |
WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation (2021.findings-acl)
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| Challenge: | Existing methods for instance weighting cannot learn the weights which make the model generalize well in target domain. |
| Approach: | They propose a modelagnostic instance weighting algorithm which can learn the instance weights instead of manually designed weighting metrics. |
| Outcome: | The proposed method can learn the instance weights instead of manually designed weighting metrics. |
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing paraphrase datasets are mainly from news, novels, or social media platforms. |
| Approach: | They propose to build a large-scale paraphrase dataset using intra-paper and inter-paper methods . they use PDBERT as a general paraphrase discovering method to take advantage of paraphrased sentences . |
| Outcome: | The proposed dataset includes 33,981 paraphrase pairs from ACL and 316,063 pairs from arXiv . the major advantages of paraphrases lie in the prominent length and textual diversity . |
FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)
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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
| 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. |
Learning with Rejection for Abstractive Text Summarization (2022.emnlp-main)
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| Challenge: | Existing abstractive summarization systems produce non-factual summaries due to noise in the training dataset. |
| Approach: | They propose a training objective for abstractive summarization based on rejection learning that learns whether or not to reject potentially noisy tokens. |
| Outcome: | The proposed method significantly improves the factuality of generated summaries in automatic and human evaluations when compared to baseline models. |
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)
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| Challenge: | Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored. |
| Approach: | They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks. |
| Outcome: | The proposed model achieves 65.96% F1 score on the FUNSD dataset. |
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization (2022.acl-long)
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| Challenge: | State-of-the-art abstractive summarization systems often generate hallucinations, i.e., content that is not directly inferable from the source document. |
| Approach: | They propose a detection approach that separates factual from non-factual hallucinations of entities by masked language models. |
| Outcome: | The proposed method outperforms baselines in accuracy and F1 scores and has a strong correlation with human judgments on factuality classification tasks. |
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)
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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng
| Challenge: | Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment. |
| Approach: | They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion. |
| Outcome: | The proposed training recipe bridges atomic action execution and strategic task completion. |