Papers by Bin Tang
R2A-TLS: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Integration (2025.findings-emnlp)
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| Challenge: | Existing methods struggle to capture coherent event narratives due to fragmented descriptions . Existing approaches accumulate noise through iterative retrieval strategies that lack relevance evaluation. |
| Approach: | They propose a reflective retrieval-augmented timeline summarization with Causal-Semantic Intergration approach for open-domain timeline summarizing . |
| Outcome: | The proposed approach outperforms the best prior published approaches. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning (2025.findings-emnlp)
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| Challenge: | Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning. |
| Approach: | They propose a dual-system LoRA framework that partitions data and parameters by System 1 or System 2 demands and adopts a two-stage fine-tuning strategy to enhance knowledge and intuition. |
| Outcome: | The proposed framework partitions data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task. |
Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models (2026.findings-acl)
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| Challenge: | Existing Vid-LLMs lack robust mechanisms for maintaining grounded spatiotemporal beliefs under conversational feedback. |
| Approach: | They propose a negation-based gaslighting evaluation framework and introduce a benchmark to investigate spatiotemporal sycophancy. |
| Outcome: | The proposed framework evaluates state-of-the-art Vid-LLMs across video understanding tasks. |
Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding (2026.acl-long)
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Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu
| Challenge: | Existing methods for streaming video understanding are query-agnostic and implicitly model video evidence. |
| Approach: | They propose a framework that establishes explicit, structured alignment between the accumulated video evidence and the query’s expected response conditions via scene graphs. |
| Outcome: | The proposed model achieves more interpretable and accurate response timing decisions on both proactive and reactive tasks. |
ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)
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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang
| Challenge: | retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures . |
| Approach: | They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents. |
| Outcome: | The proposed method reduces offline indexing costs and accelerates retrieval. |
Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning (2026.findings-acl)
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Haiyang Yu, Yuchuan Wu, Fan Shi, Jinghui Lu, Ke Niu, Xiaodong Ge, Minghan Zhuo, Jingqun Tang, Bin Li
| Challenge: | Existing document benchmarks focus on English printed texts or simplified Chinese . current vision-language models struggle with visual complexity and poor adaptability . |
| Approach: | They propose a benchmark to evaluate Chinese ancient documents' visual/linguistic complexity . ancient documents are valuable cultural heritage, but they face challenges in digitization and understanding . |
| Outcome: | the first benchmark for Chinese ancient documents evaluates VLMs from OCR to knowledge reasoning . ancient documents carry thousands of years of Chinese history and culture . traditional methods only scan images, while current models struggle with visual complexity . |
Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models (2024.lrec-main)
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Boxi Cao, Qiaoyu Tang, Hongyu Lin, Shanshan Jiang, Bin Dong, Xianpei Han, Jiawei Chen, Tianshu Wang, Le Sun
| Challenge: | Pre-trained language models have shown remarkable memory formation, but vanilla networks without pre-training suffer catastrophic forgetting problem. |
| Approach: | They conduct experiments to investigate the retentive-forgetful contradiction between vanilla and pre-trained language models by controlling the target knowledge types, learning strategies and learning schedules. |
| Outcome: | The results show that pre-trained language models are forgetful and pre-training leads to retentive models . |
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration (2026.findings-acl)
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Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen
| Challenge: | Existing benchmarks emphasize general-domain retrieval or static scientific question answering . SciExplore focuses on scientific database navigation, ambiguous literature retrieval, missing reference completion, and cross-source structured knowledge synthesis tasks. |
| Approach: | They propose a benchmark to evaluate scientific information-seeking and reasoning capabilities of LLMs and agents. |
| Outcome: | The new benchmark assesses the capabilities of state-of-the-art LLMs and agents in scientific research workflows. |
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)
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| Challenge: | Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects . |
| Approach: | They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs . |
| Outcome: | Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings. |
Adaptive Zooming via Relevance-Informed Positional Resource Allocation for Training-free LLM Context Extension (2026.findings-acl)
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Hongbo Zhao, Huibin Wang, Bin Tang, Xianming Hu, Yihong Huang, Yijun Shen, Nuoyi Chen, Ping Li, Kai Zhang
| Challenge: | Existing training-free methods for extrapolating beyond training context lengths are semantics-agnostic . Existing methods that focus on relative token distances can indiscriminately blur semantically relevant and irrelevant tokens . |
| Approach: | They propose an adaptive positional zooming method that uses semantic relevance to extrapolate beyond training context lengths. |
| Outcome: | Experiments show that RiPRA outperforms existing training-free extrapolation methods . relevant tokens get higher positional resolution, while irrelevant tokens are compressed . |
Text Editing as Imitation Game (2022.findings-emnlp)
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| Challenge: | Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification. |
| Approach: | They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens. |
| Outcome: | The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness. |
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (2023.findings-acl)
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| Challenge: | Existing methods to construct knowledge graphs are limited to a small set of relations due to manual cost or restrictions in text corpus. |
| Approach: | They propose to automatically construct knowledge graphs (KGs) of diverse new relations from pretrained language models that accept knowledge queries with prompts. |
| Outcome: | The proposed framework extracts knowledge of over 400 new relations from pretrained language models, including RoBERTaNet, with minimal input of a relation definition and a few shot of example entity pairs. |
Modeling Uncertainty in Composed Image Retrieval via Probabilistic Embeddings (2025.acl-long)
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Haomiao Tang, Jinpeng Wang, Yuang Peng, GuangHao Meng, Ruisheng Luo, Bin Chen, Long Chen, Yaowei Wang, Shu-Tao Xia
| Challenge: | Composed Image Retrieval (CIR) combines text and reference images to search for images . metric learning methods that focus on point embeddings fail to capture uncertainty in input data . |
| Approach: | They propose a framework that captures uncertainty in images and queries by Gaussian distributions in latent space rather than fixed points. |
| Outcome: | Experiments show that the proposed framework quantifies quality and semantic uncertainties . it can handle polysemy and ambiguity in search intentions, authors say . |