Papers by Yize Chen
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search (2025.acl-long)
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Yize Zhang, Tianshu Wang, Sirui Chen, Kun Wang, Xingyu Zeng, Hongyu Lin, Xianpei Han, Le Sun, Chaochao Lu
| Challenge: | Large language models (LLMs) have impressive capabilities but their application in open-ended, knowledge-intensive, complex reasoning scenarios is limited. |
| Approach: | They propose a framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation within a Monte Carlo tree search paradigm. |
| Outcome: | The proposed framework outperforms the state-of-the-art KAR methods by up to 23.10% and the latest RAG-equipped large reasoning models by upto 25.37%. |
Beyond Surface-Level Pattern Trap: LLM Agents for Faster and Smarter Cross-Architecture Code Migration (2026.findings-acl)
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| Challenge: | cross-architecture code migration is a resource-intensive and errorprone task. |
| Approach: | a framework for cross-architecture code migration is proposed to decouple implementation details through functional mining and code refactoring. |
| Outcome: | a new framework improves performance and correctness over state-of-the-art frameworks on OpenCV migration tasks. |
Live-Aid: A Large-Scale Dialogue Dataset and Benchmark for Interleaved Multi-party Interactions in Live Streaming (2026.findings-acl)
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Yiming Lei, Yize Fan, Zeming Liu, Jiaji Dong, Hui Qiu, Haitao Leng, Qingjie Liu, Kehai Chen, Tingting Gao, Yunhong Wang
| Challenge: | Existing Multimodal Large Language Models struggle with dynamic interactions due to the scarcity of high-quality interleaved data. |
| Approach: | They propose a large-scale interleaved live interaction Chinese dataset with human-annotated video responses. |
| Outcome: | The proposed model can be used to evaluate live interactions in Chinese over 1,100 hours and 80,037 dialogue turns. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |