Papers by Zihe Ye
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)
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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools. |
| Approach: | They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools. |
| Outcome: | The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili. |
MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)
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Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang
| Challenge: | Existing recommender systems rely on semantic user and item memories to make predictions, but these memories are kept in isolation. |
| Approach: | They propose a framework that architecturally decouples memory management from reasoning to decouple memory management and reasoning from the user and item memories. |
| Outcome: | The proposed framework decouples memory management from reasoning and achieves state-of-the-art performance on four benchmarks. |
H-Mem: Hybrid Multi-Dimensional Memory Management for Long-Context Conversational Agents (2026.eacl-long)
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| Challenge: | Existing frameworks for long-context conversational agents struggle to organize information across dimensions like time and topic, leading to poor retrieval. |
| Approach: | They propose a Hybrid Multi-Dimensional Memory architecture that stores conversational facts in two parallel hierarchical data structures: a temporal tree that organizes information chronologically and a semantic tree that arranges it conceptually. |
| Outcome: | The proposed architecture improves performance on long-context QA datasets by 8.4% compared to current systems. |
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)
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Bohan Lyu, Xin Cong, Heyang Yu, Pan Yang, Cheng Qian, Zihe Wang, Yujia Qin, Yining Ye, Yaxi Lu, Chen Qian, Zhong Zhang, Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains. |
| Approach: | They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries. |
| Outcome: | The proposed system outperforms baselines in the open domain task-solving benchmark. |