Papers by Zichen Wen
Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More (2025.emnlp-main)
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Zichen Wen, Yifeng Gao, Shaobo Wang, Junyuan Zhang, Qintong Zhang, Weijia Li, Conghui He, Linfeng Zhang
| Challenge: | Vision tokens in multimodal large language models often dominate computational overhead due to excessive length compared to linguistic modality. |
| Approach: | They propose a token pruning method which defines an importance criterion for vision tokens and prunes the unimportant vision token during inference. |
| Outcome: | The proposed method can prune 88.9% of vision tokens while maintaining comparable performance. |
Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem? (2025.findings-acl)
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| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
| Approach: | They propose to prune redundant tokens in MLLMs to reduce computation and storage costs. |
| Outcome: | The proposed method reduces the computational and storage costs of MLLMs by identifying redundant tokens and pruning them. |
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning (2025.acl-long)
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Shaobo Wang, Xiangqi Jin, Ziming Wang, Jize Wang, Jiajun Zhang, Kaixin Li, Zichen Wen, Zhong Li, Conghui He, Xuming Hu, Linfeng Zhang
| Challenge: | Using fine-tuning on task-specific data is essential for large language models to be effective in specialized tasks. |
| Approach: | They propose a method that leverages few-shot in-context learning with the model to be fine-tuned. |
| Outcome: | The proposed method outperforms existing methods with a 3.1-point improvement and a 7.4 speedup on the Llama-3-8B-Instruct model using just 10% of the dataset. |
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods (2026.acl-long)
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Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Xin Zou, Yuqian Fu, Bin Ren, Linfeng Zhang, Xuming Hu
| Challenge: | Recent efforts to accelerate inference in Multimodal Large Language Models have focused on visual token compression. |
| Approach: | They propose a framework that leverages downsampling as a discriminator to denoise existing benchmarks. |
| Outcome: | The proposed evaluation framework leverages downsampling as a discriminator to denoise existing benchmarks. |
StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding (2026.findings-acl)
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Junxi Wang, Te Sun, Jiayi Zhu, Junxian Li, Haowen Xu, Zichen Wen, Xuming Hu, Zhiyu li, Linfeng Zhang
| Challenge: | StreamMeCo is an efficient Stream Agent Memory Compression framework for video understanding. |
| Approach: | They propose an efficient Stream Agent Memory Compression framework that evicts redundant memory nodes and introduces a time-decay memory retrieval mechanism to mitigate performance degradation. |
| Outcome: | The proposed framework achieves 1.87 speedup in memory retrieval while delivering an average accuracy improvement of 1.0% on three challenging benchmark datasets. |
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths (2021.emnlp-main)
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| Challenge: | Existing taxonomies are unable to maintain coverage due to the rising of new concepts . TEMP uses pre-trained contextual encoders to predict the position of new ideas . |
| Approach: | They propose a self-supervised taxonomy expansion method that ranks taxonomies by ranking them . they use pre-trained contextual encoders to train the model with dynamic margin loss . |
| Outcome: | The proposed method outperforms state-of-the-art taxonomy expansion methods by 14.3% and 15.8% on public benchmarks. |
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems (2026.acl-long)
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| Challenge: | Automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption. |
| Approach: | They propose a plug-and-play compression framework for graph-structured multi-agent workflows . they estimate the importance score of each agent and remove redundant agents . |
| Outcome: | Experiments show that AgentSlimming reduces average token cost by 78.9% with negligible performance degradation. |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
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| Challenge: | Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes. |
| Approach: | They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two MIMIC datasets. |