Papers by Xiuze Zhou
Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation (2025.emnlp-main)
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| Challenge: | Existing approaches to adapt Mixture-of-Experts models to multiple domains are prohibitive computation, cross-domain interference or require separate runs per domain. |
| Approach: | They propose a dynamic expert specialization framework for multi-domain adaptation of Mixture-of-Experts models. |
| Outcome: | The proposed framework reduces forgetting by 89% compared to full fine-tuning as domains scale from 2 to 6 and achieves faster convergence than conventional methods. |
Cogito: A Cognitive Agentic Framework Driven by Dynamic Graph of Thoughts for Financial Report Generation (2026.findings-acl)
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| Challenge: | Existing approaches to financial report generation are insufficient to handle dynamic uncertainties of real-world financial environments. |
| Approach: | They propose a cognitively grounded agentic framework for professional financial report generation that is driven by Dynamic Graph of Thoughts and a social collaboration mechanism to facilitate coordinated agent interaction. |
| Outcome: | The proposed framework is based on a dynamic reasoning model and social collaboration mechanism. |
Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis (2025.acl-long)
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| Challenge: | Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures. |
| Approach: | They propose to analyze sparse MoE architectures against dense models to capture dynamic routing-expert interactions. |
| Outcome: | The proposed algorithm shows that sparse models achieve higher efficiency per layer . it also shows that deep Qwen-MoE mitigates expert failures while minimizing complexity . |