Papers by Xiaowei Jia
Regularized Graph Convolutional Networks for Short Text Classification (2020.coling-industry)
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| Challenge: | Short text classification is a problem in natural language processing, social network analysis, and e-commerce. |
| Approach: | They propose a short text classification technique that incorporates label dependencies into the output space to overcome the limitations of short text. |
| Outcome: | The proposed model outperforms baseline methods on proprietary and external datasets and is more robust to noise in textual features. |
Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond (2025.acl-industry)
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Liang Wen, Yunke Cai, Fenrui Xiao, Xin He, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Tanglifu Tanglifu, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia, Xiangzheng Zhang
| Challenge: | Experimental results show that opensource curriculum training is more effective when distinct datasets are available for different training stages. |
| Approach: | They propose an opensource suite for training long reasoning models using publicdata and models. |
| Outcome: | The proposed model outperforms DeepSeek-R1-DistillQwen-32B models in math reasoning. |
Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025.findings-acl)
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| Challenge: | Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose to examine how internal beliefs affect reasoning generation and reasoning-guided answer prediction in CoT by decomposing CoT into a two-stage process. |
| Outcome: | The proposed model beliefs affect reasoning generation and reasoning-guided answer prediction in CoT, and the results provide strong evidence of confirmation bias in LLMs. |
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search (2026.acl-long)
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Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao
| Challenge: | Existing methods for memory management struggle to capture fine-grained semantic relations between queries and documents. |
| Approach: | They propose a framework for reasoning and agentic search that grows fine-grained memory fragments from seed tokens from queries, then retraces and deep refines the memory via a contribution function. |
| Outcome: | Experiments on eight benchmark datasets show that MemSearch-o1 significantly mitigates memory dilution and more effectively activates reasoning potential of diverse LLMs. |